# # R script for ASE350 set 5789 PTEH Deacetylation DELO # # Bruno Godin # NREL # Oct 2015 require(XLConnect) require(car) require(lme4) require(extRemes) require(stringi) require(tidyr) # # set working directory # this is the location where the folders holding the spreadsheets is located setwd("C:/Users/b.godin/Dropbox/CRA-W/NREL/Article/R scripts/") # # # # PART One A rm(list=ls(all=TRUE)) # # # # # Load PT data SET 5 and 9 temp1 <- loadWorkbook("ASE350 Deacetylation Exp.xlsx") #temp1 <- loadWorkbook("150424 ASE350 Deacetylation Exp Feedstock Reactivity 2015-Set5789.xlsx") PT.all <- readWorksheet(temp1,sheet="Digestion1WithOutDeacet",startCol=1,endCol=61) rm(temp1) PT <- PT.all [,c(1:5,7:9,11:13,23,25:29,31:35,36:42,43:52,53:56,57:58,61)] colnames(PT)<-c("Sample","Ftid","Ltid","WStid","Batch", "Material","TRB","Date","Temp","hTime","sTime","Lseverity","ODW.g","WS.g","L.g","L.vol","ODW1.g","G","X", "Sta", "Suc","Aca", "mG.c","tG.c","mX.c","tX.c","AA.c","HMF.c","Furf.c","mG.Y","mG.R","tG.Y","tG.R","mX.Y","mX.R","tX.Y","tX.R","A.Y","A.R","HMF.Y","HMF.R","Furf.Y","Furf.R","Fglu","Ffru","tF.R") # Sample 1 Sample Name # Ftid 2 Feedstock Tracking ID # Ltid 3 Pretreated Liquor Tracking ID # WStid 4 Washed Solids Tracking ID # Material 7 Material Name # TRB 8 TRB reference # Date 9 Date of pretreatment # Temp 11 ASE 350 temperature (C) # hTime 12 ASE 350 heating time (min) # sTime 13 ASE 350 reaction (static) time (min) # Lseverity 23 Log(severity) # ODW.g 25 oven dry weight of feedstock (g) # WS.g 26 wet weight of pretreated solids (g) # L.g 27 weight of liquor before normalization (g) # L.vol 28 volume of liquor (after normalization; should be 200) (g) # ODW1.g 29 oven dry weight of pretreated solids (g) # G 31 glucan content # X 32 xylan content # Sta 33 glucan content # Suc 34 xylan content # Aca 35 Acetyl content # mG.c 36 monomeric glucose concentration in liquor (g/L) # tG.c 37 total glucose concentration in liquor (g/L) # mX.c 38 monomeric xylose concentration in liquor (g/L) # tX.c 39 total xylose concentration in liquor (g/L) # AA.c 40 acetic acic concentration in liquor (g/L) # HMF.c 41 HMF concentration in liquor (g/L) # Furf.c 42 furfural concentration in liquor (g/L) # mG.Y 43 monomeric glucan yield (%) # mG.R 44 monomeric glucose release (g/g) # tG.Y 45 total glucan yield (%) # tG.R 46 total glucan release (g/g) # mX.Y 47 monomeric xylan yield (%) # mX.R 48 monomeric xylose release (g/g) # tX.Y 49 total xylan yield (g/g) # tX.R 50 total xylose release (g/g) # A.Y 51 acetate yield (%) # A.R 52 acetate release (g/g) # HMF.Y 53 HMF yield (%) # HMF.R 54 HMF release (g/g) # Furf.Y 55 furfural yield (%) # Furf.R 56 furfural release (g/g) # Fglu 57 free glucose content # Ffru 58 free fructose content # # Load EH data temp2 <- loadWorkbook("150424 ASE350 Deacetylation Exp Feedstock Reactivity 2015-Set5789.xlsx") EH.all <- readWorksheet(temp2,sheet="Digestion2WithOutDeacet", header=TRUE,startCol=1,endCol=44) rm(temp2) EH <-EH.all[,c(1:3,5,6,9,13:16,20,27:32,33:38,39:44)] colnames(EH)<-c("Sample","Ftid","WStid","Ltid","Batch","TRB", "SolSlu","Wet.g","ODW.g","Eg/g","Total_g","C.c","G.c","X.c","Ga.c","Ar.c", "Fr.c", "C.g","G.g","X.g","Ga.g","Ar.g","Fr.g","C.R","G.R","X.R","Ga.R","Ar.R","Fr.R") # Sample 1 Sample Name # Ftid 2 Feedstock Tracking ID # WStid 3 Washed solids Tracking ID # Ltid 5 Liquor Tracking ID # TRB 9 TRB reference # SolSlu 13 % Solids of t0 EH Slurry # Wet.g 14 Wet weight of washed solids used (g) # ODW.g 15 oven dry weight of washed solids (g) # Eg/g 16 enzyme loading (g/g ODW solids) # Total_g 20 total mass of all constituents (media, biomass, enzyme, buffer) (g) # C.c 27 cellobiose concentration (g/L) # G.c 28 glucose concentration (g/L) # X.c 29 xylose concentration (g/L) # Ga.c 30 galactose concentration (g/L) # Ar.c 31 arabinose concentration (g/L) # Fr.c 32 fructose concentration (g/L) # C.g 33 cellobiose mass (g) # G.g 34 glucose mass (g) # X.g 35 xylose mass (g) # Ga.g 36 galactose mass (g) # Ar.g 37 arabinose mass (g) # Fr.g 38 fructose mass (g) # C.R 39 cellobiose release (g/g) # G.R 40 glucose release (g/g) # X.R 41 xylose release (g/g) # Ga.R 42 galactose release (g/g) # Ar.R 43 arabinose release (g/g) # Fr.R 44 fructose release (g/g) # # Merge PT and EH data data.all <- merge(PT, EH, by="WStid") #toremove1 <- which(data.all$Sample.x=="Kramer33A14") #data <- data.all[-toremove1,] data <- data.all a<-nrow(data) data$row.names.g <- cbind(data$row.names, 1:a) rm(a) # # Correction factors cg <-180/162 cx <-150/132 Cgsta <-180/162 Cgsuc <-(342/324)/2 Cace <- 1.3750 HydDil <- 5.175/5 data$Totglu <- ((as.numeric(data$G) / 100) * cg) + ((as.numeric(data$Sta) / 100) * Cgsta) + ((as.numeric(data$Suc) / 100) * Cgsuc) + ((as.numeric(data$Fglu) / 100)) data$Totglufru <- ((as.numeric(data$G) / 100) * cg) + ((as.numeric(data$Sta) / 100) * Cgsta) + ((as.numeric(data$Suc) / 100) * Cgsuc) + ((as.numeric(data$Suc) / 100) * Cgsuc) + ((as.numeric(data$Fglu) / 100)) + ((as.numeric(data$Ffru) / 100)) # # Conversion factor for EH data$EHtoPT <- as.numeric(data$ODW1.g) / as.numeric(data$ODW.g.x) # # PT release and yield data$pt.Gm.r <- as.numeric(data$mG.R) data$pt.Xm.r <- as.numeric(data$mX.R) data$pt.Gt.r <- as.numeric(data$tG.R) data$pt.Xt.r <- as.numeric(data$tX.R) data$pt.At.r <- as.numeric(data$A.R) data$pt.GXm.r <- data$pt.Gm.r + data$pt.Xm.r data$pt.GXt.r <- data$pt.Gt.r + data$pt.Xt.r data$pt.GXAt.r <- data$pt.Gt.r + data$pt.Xt.r + data$pt.At.r data$pt.Ft.r <- as.numeric(data$tF.R) data$pt.Gt.y <- data$pt.Gt.r / (data$Totglu) data$pt.Xt.y <- data$pt.Xt.r / ((as.numeric(data$X) /100)*cx) data$pt.Gm.y <- data$pt.Gm.r / (data$Totglu) data$pt.Xm.y <- data$pt.Xm.r / ((as.numeric(data$X) /100)*cx) data$pt.At.y <- data$pt.At.r / ((as.numeric(data$Aca) /100)*Cace) data$pt.GXt.y <- (data$pt.Gt.r + data$pt.Xt.r) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx)) data$pt.GXm.y <- (data$pt.Gm.r + data$pt.Xm.r) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx)) data$pt.GXAt.y <- (data$pt.Gt.r + data$pt.Xt.r + data$pt.At.r) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx) +((as.numeric(data$Aca)/ 100)*Cace)) data$pt.Ft1.y <- data$pt.Ft.r / ((((as.numeric(data$Suc) / 100) * Cgsuc)) + ((as.numeric(data$Ffru) / 100))) data$pt.Ft2.y <- data$pt.Ft.r / (data$Totglufru) # # EH release data$eh.G.r <- as.numeric(data$G.R) data$eh.X.r <- as.numeric(data$X.R) data$eh.GX.r <- data$eh.G.r + data$eh.X.r data$eh.G.y <- ((data$EHtoPT * data$eh.G.r) / (data$Totglu)) data$eh.X.y <- ((data$EHtoPT * data$eh.X.r) / ((as.numeric(data$X) /100)*cx)) data$eh.GX.y <- (data$EHtoPT *data$eh.G.r) + (data$EHtoPT *data$eh.X.r) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx)) # # PTEH release and yield data$pteh.G.r <- as.numeric(data$tG.R) + (data$EHtoPT * as.numeric(data$G.R)) data$pteh.X.r <- as.numeric(data$tX.R) + (data$EHtoPT * as.numeric(data$X.R)) data$pteh.GX.r <- data$pteh.G.r + data$pteh.X.r data$pteh.G.y <- data$pteh.G.r / (data$Totglu) data$pteh.X.y <- data$pteh.X.r / ((as.numeric(data$X)/ 100)*cx) data$pteh.GX.y <- (data$pteh.G.r + data$pteh.X.r) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx)) # New calculated variables # pt.Gt.r PT only - total glucose release (g/g) # pt.Gt.y PT only - total glucan yield (%) # pt.tX.r PT only - total xylose release (g/g) # pt.tX.y PT only - total xylan yield (%) # pt.AX.r PT only - total acetate release (g/g) # pt.AX.y PT only - total acetyl yield (%) # pt.GXt.r PT only - total glucose+xylose release (g/g) # pt.GXt.y PT only - total glucan+xylan yield (%) # pt.GXAt.r PT only - total glucan+xylose+acetate release (g/g) # pt.GXAt.y PT only - total glucan+xylan+acetyl yield (%) # eh.G.r EH only - total glucose release (g/g) # eh.G.y EH only - total glucan yield (%) # eh.X.r EH only - total xylose release (g/g) # eh.X.y EH only - total xylan yield (%) # eh.GX.r EH only - total glucose+xylose release (g/g) # eh.GX.y EH only - total glucan+xylan yield (%) # pteh.G.r overall (PT+EH) glucose release (g/g) # pteh.G.y overall (PT+EH) glucan yield (%) # pteh.X.r overall (PT+EH) xylose release (g/g) # pteh.X.y overall (PT+EH) xylan yield (%) # pteh.GX.r overall (PT+EH) glucose+xylose release (g/g) (reactivity) # pteh.GX.y overall (PT+EH) glucan+xylane yield (%) (reactivity) data.set59<-as.data.frame(cbind(data$Sample.x, data$Ftid.x, data$Ltid.x, data$WStid, data$Temp, data$Batch.x, data$EHtoPT, data$pt.Gm.r, data$pt.Xm.r, data$pt.GXm.r, data$pt.Gm.y, data$pt.Xm.y, data$pt.GXm.y, data$pt.Gt.r, data$pt.Xt.r, data$pt.At.r, data$pt.GXt.r, data$pt.GXAt.r, data$pt.Gt.y, data$pt.Xt.y, data$pt.At.y, data$pt.GXt.y, data$pt.GXAt.y, data$eh.G.r, data$eh.X.r, data$eh.GX.r, data$eh.G.y, data$eh.X.y, data$eh.GX.y, data$pteh.G.r, data$pteh.X.r, data$pteh.GX.r, data$pteh.G.y, data$pteh.X.y, data$pteh.GX.y)) colnames(data.set59)<-c("Sample", "Ftid", "Ltid", "WStid", "Temp", "Batch", "EHtoPT", "pt.Gm.r", "pt.Xm.r", "pt.GXm.r", "pt.Gm.y", "pt.Xm.y", "pt.GXm.y", "pt.Gt.r", "pt.Xt.r", "pt.At.r", "pt.GXt.r", "pt.GXAt.r", "pt.Gt.y", "pt.Xt.y", "pt.At.y", "pt.GXt.y", "pt.GXAt.y", "eh.G.r", "eh.X.r", "eh.GX.r", "eh.G.y", "eh.X.y", "eh.GX.y", "pteh.G.r", "pteh.X.r", "pteh.GX.r", "pteh.G.y", "pteh.X.y", "pteh.GX.y") write.csv(data.set59, file ="ASE350-set59-data-DELO.csv") eh.ref.lines <- (which(EH.all$Feedstock.Tracking.ID=="P080828CS" | EH.all$Feedstock.Tracking.ID=="P120927CS")) set59.eh.ref <- data.frame(EH.all[eh.ref.lines,]) # # # # PART One B rm(list=ls(all=TRUE)) # # # # # Load PT data temp1 <- loadWorkbook("150424 ASE350 Deacetylation Exp Feedstock Reactivity 2015-Set5789.xlsx") PT.all <- readWorksheet(temp1,sheet="Digestion1WithDeacet",startCol=1,endCol=89) rm(temp1) PT <- PT.all [,c(1:6,8:10,12,26:32,34:52,53:84,85:86,89)] colnames(PT)<-c("Sample", "Ftid", "LtidA", "LtidC", "WStid", "Batch", "Material", "TRB", "Date", "Temp", "ODW.g", "WS.g", "L.g.A", "L.g.C", "L.volA", "L.volC", "ODW1.g", "G", "X", "Sta", "Suc", "Aca", "mG.cA", "mG.cC", "tG.cA", "tG.cC", "mX.cA", "mX.cC", "tX.cA", "tX.cC", "AA.cA", "AA.cC", "HMF.cA", "HMF.cC", "Furf.cA", "Furf.cC", "mG.YA", "mG.YC", "mG.RA", "mG.RC", "tG.YA", "tG.YC", "tG.RA", "tG.RC", "mX.YA", "mX.YC", "mX.RA", "mX.RC", "tX.YA", "tX.YC", "tX.RA", "tX.RC", "Ab.YA", "Ab.YC", "Ab.RA", "Ab.RC", "A.YA", "A.YC", "A.RA", "A.RC", "HMF.YA", "HMF.YC", "HMF.RA", "HMF.RC", "Furf.YA", "Furf.YC", "Furf.RA", "Furf.RC", "Fglu", "Ffru", "tF.R") # A Flask A # C Flask C # Sample Sample Name # Ftid Feedstock Tracking ID # Ltid Pretreated Liquor Tracking ID # WStid Washed Solids Tracking ID # Material Material Name # TRB TRB reference # Date Date of pretreatment # Temp ASE 350 temperature (C) # ODW.g oven dry weight of feedstock (g) # WS.g wet weight of pretreated solids (g) # L.g weight of liquor before normalization (g) # L.vol volume of liquor (after normalization; should be 200) (g) # ODW1.g oven dry weight of pretreated solids (g) # G glucan content # X xylan content # Sta glucan content # Suc xylan content # Aca Acetyl content # mG.c monomeric glucose concentration in liquor (g/L) # tG.c total glucose concentration in liquor (g/L) # mX.c monomeric xylose concentration in liquor (g/L) # tX.c total xylose concentration in liquor (g/L) # AA.c acetic acic concentration in liquor (g/L) # HMF.c HMF concentration in liquor (g/L) # Furf.c furfural concentration in liquor (g/L) # mG.Y monomeric glucan yield (%) # mG.R monomeric glucose release (g/g) # tG.Y total glucan yield (%) # tG.R total glucan release (g/g) # mX.Y monomeric xylan yield (%) # mX.R monomeric xylose release (g/g) # tX.Y total xylan yield (g/g) # tX.R total xylose release (g/g) # A.Y acetate yield (%) # A.R acetate release (g/g) # HMF.Y HMF yield (%) # HMF.R HMF release (g/g) # Furf.Y furfural yield (%) # Furf.R furfural release (g/g) # Fglu free glucose content # Ffru free fructose content # # Load EH data temp2 <- loadWorkbook("150424 ASE350 Deacetylation Exp Feedstock Reactivity 2015-Set5789.xlsx") EH.all <- readWorksheet(temp2,sheet="Digestion2WithDeacet", header=TRUE,startCol=1,endCol=44) rm(temp2) EH <-EH.all[,c(1:3,5,6,9,13:16,20,27:32,33:38,39:44)] colnames(EH)<-c("Sample","Ftid","WStid","Ltid", "Batch","TRB", "SolSlu","Wet.g","ODW.g","Eg/g","Total_g","C.c","G.c","X.c","Ga.c","Ar.c", "Fr.c", "C.g","G.g","X.g","Ga.g","Ar.g","Fr.g","C.R","G.R","X.R","Ga.R","Ar.R","Fr.R") # Sample 1 Sample Name # Ftid 2 Feedstock Tracking ID # WStid 3 Washed solids Tracking ID # Ltid 5 Liquor Tracking ID # TRB 9 TRB reference # SolSlu 13 % Solids of t0 EH Slurry # Wet.g 14 Wet weight of washed solids used (g) # ODW.g 15 oven dry weight of washed solids (g) # Eg/g 16 enzyme loading (g/g ODW solids) # Total_g 20 total mass of all constituents (media, biomass, enzyme, buffer) (g) # C.c 27 cellobiose concentration (g/L) # G.c 28 glucose concentration (g/L) # X.c 29 xylose concentration (g/L) # Ga.c 30 galactose concentration (g/L) # Ar.c 31 arabinose concentration (g/L) # Fr.c 32 fructose concentration (g/L) # C.g 33 cellobiose mass (g) # G.g 34 glucose mass (g) # X.g 35 xylose mass (g) # Ga.g 36 galactose mass (g) # Ar.g 37 arabinose mass (g) # Fr.g 38 fructose mass (g) # C.R 39 cellobiose release (g/g) # G.R 40 glucose release (g/g) # X.R 41 xylose release (g/g) # Ga.R 42 galactose release (g/g) # Ar.R 43 arabinose release (g/g) # Fr.R 44 fructose release (g/g) # # Merge PT and EH data data.all <- merge(PT, EH, by="WStid") data <- data.all a<-nrow(data) data$row.names.g <- cbind(data$row.names, 1:a) rm(a) # # Correction factors cg <-180/162 cx <-150/132 Cgsta <-180/162 Cgsuc <-(342/324)/2 Cace <- 1.3750 HydDil <- 5.175/5 data$Totglu <- ((as.numeric(data$G) / 100) * cg) + ((as.numeric(data$Sta) / 100) * Cgsta) + ((as.numeric(data$Suc) / 100) * Cgsuc) + ((as.numeric(data$Fglu) / 100)) data$Totglufru <- ((as.numeric(data$G) / 100) * cg) + ((as.numeric(data$Sta) / 100) * Cgsta) + ((as.numeric(data$Suc) / 100) * Cgsuc) + ((as.numeric(data$Suc) / 100) * Cgsuc) + ((as.numeric(data$Fglu) / 100)) + ((as.numeric(data$Ffru) / 100)) # # Conversion factor for EH data$EHtoPT <- as.numeric(data$ODW1.g) / as.numeric(data$ODW.g.x) # # PT release and yield data$pt.Gt.rA <- as.numeric(data$tG.RA) data$pt.Gt.rC <- as.numeric(data$tG.RC) data$pt.Gt.rABC <- as.numeric(data$tG.RA) + as.numeric(data$tG.RC) data$pt.Xt.rA <- as.numeric(data$tX.RA) data$pt.Xt.rC <- as.numeric(data$tX.RC) data$pt.Xt.rABC <- as.numeric(data$tX.RA) + as.numeric(data$tX.RC) data$pt.At.rA <- as.numeric(data$A.RA) data$pt.At.rC <- as.numeric(data$A.RC) data$pt.At.rABC <- as.numeric(data$A.RA) + as.numeric(data$A.RC) data$pt.GXt.rA <- data$pt.Gt.rA + data$pt.Xt.rA data$pt.GXt.rC <- data$pt.Gt.rC + data$pt.Xt.rC data$pt.GXt.rABC <- data$pt.Gt.rABC + data$pt.Xt.rABC data$pt.GXAt.rA <- data$pt.Gt.rA + data$pt.Xt.rA + data$pt.At.rA data$pt.GXAt.rC <- data$pt.Gt.rC + data$pt.Xt.rC + data$pt.At.rC data$pt.GXAt.rABC <- data$pt.Gt.rABC + data$pt.Xt.rABC + data$pt.At.rABC data$pt.Ft.r <- as.numeric(data$tF.R) data$pt.Gt.yA <- data$pt.Gt.rA / (data$Totglu) data$pt.Gt.yC <- data$pt.Gt.rC / (data$Totglu) data$pt.Gt.yABC <- data$pt.Gt.yA + data$pt.Gt.yC data$pt.Xt.yA <- data$pt.Xt.rA / ((as.numeric(data$X) /100)*cx) data$pt.Xt.yC <- data$pt.Xt.rC / ((as.numeric(data$X) /100)*cx) data$pt.Xt.yABC <- data$pt.Xt.yA + data$pt.Xt.yC data$pt.At.yA <- data$pt.At.rA / ((as.numeric(data$Aca) /100)*Cace) data$pt.At.yC <- data$pt.At.rC / ((as.numeric(data$Aca) /100)*Cace) data$pt.At.yABC <- data$pt.At.yA + data$pt.At.yC data$pt.GXt.yA <- (data$pt.Gt.rA + data$pt.Xt.rA) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx)) data$pt.GXt.yC <- (data$pt.Gt.rC + data$pt.Xt.rC) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx)) data$pt.GXt.yABC <- data$pt.GXt.yA + data$pt.GXt.yC data$pt.GXAt.yA <- (data$pt.Gt.rA + data$pt.Xt.rA + data$pt.At.rA) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx) +((as.numeric(data$Aca)/ 100)*Cace)) data$pt.GXAt.yC <- (data$pt.Gt.rC + data$pt.Xt.rC + data$pt.At.rC) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx) +((as.numeric(data$Aca)/ 100)*Cace)) data$pt.GXAt.yABC<- data$pt.GXAt.yA + data$pt.GXAt.yC data$pt.Ft1.y <- data$pt.Ft.r / ((((as.numeric(data$Suc) / 100) * Cgsuc)) + ((as.numeric(data$Ffru) / 100))) data$pt.Ft2.y <- data$pt.Ft.r / (data$Totglufru) # # EH release data$eh.G.r <- as.numeric(data$G.R) data$eh.X.r <- as.numeric(data$X.R) data$eh.GX.r <- data$eh.G.r + data$eh.X.r data$eh.G.y <- ((data$EHtoPT * data$eh.G.r) / (data$Totglu)) data$eh.X.y <- ((data$EHtoPT * data$eh.X.r) / ((as.numeric(data$X) /100)*cx)) data$eh.GX.y <- (data$EHtoPT *data$eh.G.r) + (data$EHtoPT *data$eh.X.r) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx)) # # PTEH release and yield data$pteh.G.r <- as.numeric(data$tG.RC) + (data$EHtoPT * as.numeric(data$G.R)) data$pteh.X.r <- as.numeric(data$tX.RC) + (data$EHtoPT * as.numeric(data$X.R)) data$pteh.GX.r <- data$pteh.G.r + data$pteh.X.r data$pteh.G.y <- data$pteh.G.r / (data$Totglu) data$pteh.X.y <- data$pteh.X.r / ((as.numeric(data$X)/ 100)*cx) data$pteh.GX.y <- (data$pteh.G.r + data$pteh.X.r) / ((data$Totglu) + ((as.numeric(data$X)/ 100)*cx)) # New calculated variables # pt.Gt.r PT only - total glucose release (g/g) # pt.Gt.y PT only - total glucan yield (%) # pt.tX.r PT only - total xylose release (g/g) # pt.tX.y PT only - total xylan yield (%) # pt.AX.r PT only - total acetate release (g/g) # pt.AX.y PT only - total acetyl yield (%) # pt.GXt.r PT only - total glucose+xylose release (g/g) # pt.GXt.y PT only - total glucan+xylan yield (%) # pt.GXAt.r PT only - total glucan+xylose+acetate release (g/g) # pt.GXAt.y PT only - total glucan+xylan+acetyl yield (%) # eh.G.r EH only - total glucose release (g/g) # eh.G.y EH only - total glucan yield (%) # eh.X.r EH only - total xylose release (g/g) # eh.X.y EH only - total xylan yield (%) # eh.GX.r EH only - total glucose+xylose release (g/g) # eh.GX.y EH only - total glucan+xylan yield (%) # pteh.G.r overall (PT+EH) glucose release (g/g) # pteh.G.y overall (PT+EH) glucan yield (%) # pteh.X.r overall (PT+EH) xylose release (g/g) # pteh.X.y overall (PT+EH) xylan yield (%) # pteh.GX.r overall (PT+EH) glucose+xylose release (g/g) (reactivity) # pteh.GX.y overall (PT+EH) glucan+xylane yield (%) (reactivity) data.set3<-as.data.frame(cbind(data$Sample.x, data$Ftid.x, data$LtidA, data$LtidC, data$WStid, data$Temp, data$Batch.x, data$EHtoPT, data$pt.Gt.rA, data$pt.Gt.rC,data$pt.Gt.rABC, data$pt.Xt.rA,data$pt.Xt.rC,data$pt.Xt.rABC, data$pt.At.rA,data$pt.At.rC,data$pt.At.rABC, data$pt.GXt.rA,data$pt.GXt.rC, data$pt.GXt.rABC, data$pt.GXAt.rA,data$pt.GXAt.rC,data$pt.GXAt.rABC, data$pt.Gt.yA,data$pt.Gt.yC,data$pt.Gt.yABC, data$pt.Xt.yA,data$pt.Xt.yC,data$pt.Xt.yABC, data$pt.At.yA,data$pt.At.yC,data$pt.At.yABC, data$pt.GXt.yA,data$pt.GXt.yC,data$pt.GXt.yABC, data$pt.GXAt.yA,data$pt.GXAt.yC,data$pt.GXAt.yABC, data$eh.G.r, data$eh.X.r, data$eh.GX.r, data$eh.G.y, data$eh.X.y, data$eh.GX.y, data$pteh.G.r, data$pteh.X.r, data$pteh.GX.r, data$pteh.G.y, data$pteh.X.y, data$pteh.GX.y)) colnames(data.set3)<-c("Sample", "Ftid", "LtidA", "LtidC", "WStid", "Temp", "Batch", "EHtoPT", "pt.Gt.rA", "pt.Gt.rC", "pt.Gt.rABC", "pt.Xt.rA","pt.Xt.rC","pt.Xt.rABC", "pt.At.rA","pt.At.rC","pt.At.rABC", "pt.GXt.rA","pt.GXt.rC","pt.GXt.rABC", "pt.GXAt.rA","pt.GXAt.rC","pt.GXAt.rABC", "pt.Gt.yA","pt.Gt.yC","pt.Gt.yABC", "pt.Xt.yA","pt.Xt.yC","pt.Xt.yABC", "pt.At.yA","pt.At.yC", "pt.At.yABC", "pt.GXt.yA","pt.GXt.yC","pt.GXt.yABC", "pt.GXAt.yA","pt.GXAt.yC", "pt.GXAt.yABC", "eh.G.r", "eh.X.r", "eh.GX.r", "eh.G.y", "eh.X.y", "eh.GX.y", "pteh.G.r", "pteh.X.r", "pteh.GX.r", "pteh.G.y", "pteh.X.y", "pteh.GX.y") write.csv(data.set3, file ="ASE350-set78-data-DELO.csv") eh.ref.lines <- (which(EH.all$Feedstock.Tracking.ID=="P080828CS" | EH.all$Feedstock.Tracking.ID=="P120927CS")) set3.eh.ref <- data.frame(EH.all[eh.ref.lines,]) # # # # PART Two A rm(list=ls(all=TRUE)) # # # # # set working file data.wk <- read.csv("ASE350-set59-data-DELO.csv", header = TRUE) data.wk$ID <- paste(data.wk$Sample, data.wk$Temp, data.wk$Batch , sep="_") data.wk$IDB <- paste(data.wk$Sample, data.wk$Temp , sep="_") a<-nrow(data.wk) data.wk$row.names.g <- cbind(data.wk$row.names, 1:a) rm(a) # # Outlier #outlier <- which(data.wk$WStid=="18767") #data.wk <- data.wk[-outlier,] # # Divide data according to the substrate subset.1<-subset(data.wk, data.wk$Sample=="Kramer 33a14") subset.2<-subset(data.wk, data.wk$Sample=="Sorghum wild") subset.3<-subset(data.wk, data.wk$Sample=="Sorghum Stacked") subset.4<-subset(data.wk, data.wk$Sample=="Sorghum BMR6") subset.5<-subset(data.wk, data.wk$Sample=="Sorghum BMR12") data.sor.all <- rbind(subset.2,subset.3,subset.4,subset.5) data.sor.sel <- rbind(subset.2,subset.3) # # Summary of data.wkset per substrate & modality for pteh.G.y data.wk_n_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), length) names(data.wk_n_glu)[1]<-"Substrate" names(data.wk_n_glu)[2]<-"n" data.wk_ave_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), mean, na.rm=TRUE) names(data.wk_ave_glu)[1]<-"Substrate" names(data.wk_ave_glu)[2]<-"Mean" data.wk_med_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), median, na.rm=TRUE) names(data.wk_med_glu)[1]<-"Substrate" names(data.wk_med_glu)[2]<-"Median" data.wk_sd_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), sd, na.rm=TRUE) names(data.wk_sd_glu)[1]<-"Substrate" names(data.wk_sd_glu)[2]<-"SD" data.wk_Rsd_glu <- as.data.frame((data.wk_sd_glu$SD/data.wk_ave_glu$Mean)*(100)) names(data.wk_Rsd_glu)[1]<-"RSD" data.wk_Rsd_glu$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_Conf_glu <- as.data.frame((data.wk_sd_glu$SD*2.120 )/sqrt(data.wk_n_glu$n)) names(data.wk_Conf_glu)[1]<-"Confidence interval of the mean" data.wk_Conf_glu$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_var_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), var, na.rm=TRUE) names(data.wk_var_glu)[1]<-"Substrate" names(data.wk_var_glu)[2]<-"Variance" data.wk_min_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), min, na.rm=TRUE) names(data.wk_min_glu)[1]<-"Substrate" names(data.wk_min_glu)[2]<-"Min" data.wk_max_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), max, na.rm=TRUE) names(data.wk_max_glu)[1]<-"Substrate" names(data.wk_max_glu)[2]<-"Max" data.wk_ran_glu<-as.data.frame(data.wk_max_glu$Max-data.wk_min_glu$Min) names(data.wk_ran_glu)[1]<-"Range" data.wk_ran_glu$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_firqua_glu<-as.data.frame(tapply(data.wk$pteh.G.y, as.factor(data.wk$IDB), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_glu)[1]<-"First quantile" data.wk_firqua_glu$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_lasquat_glu<-as.data.frame(tapply(data.wk$pteh.G.y, as.factor(data.wk$IDB), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_glu)[1]<-"Last quantile" data.wk_lasquat_glu$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") # Summary of data.wkset pteh.G.y summarygluG<-merge(data.wk_n_glu,data.wk_ave_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_med_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_sd_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_Rsd_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_Conf_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_var_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_min_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_max_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_ran_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_firqua_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_lasquat_glu, by="Substrate") summarygluG$Biomass.num <- c(1,2,3,4,5,6,7,8,9) summarygluG$carbohydrate<-rep("Glucose.pteh.y",9) # # Summary of data.wkset per substrate & modality for pteh.X.y data.wk_n_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), length) names(data.wk_n_xyl)[1]<-"Substrate" names(data.wk_n_xyl)[2]<-"n" data.wk_ave_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), mean, na.rm=TRUE) names(data.wk_ave_xyl)[1]<-"Substrate" names(data.wk_ave_xyl)[2]<-"Mean" data.wk_med_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), median, na.rm=TRUE) names(data.wk_med_xyl)[1]<-"Substrate" names(data.wk_med_xyl)[2]<-"Median" data.wk_sd_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), sd, na.rm=TRUE) names(data.wk_sd_xyl)[1]<-"Substrate" names(data.wk_sd_xyl)[2]<-"SD" data.wk_Rsd_xyl <- as.data.frame((data.wk_sd_xyl$SD/data.wk_ave_xyl$Mean)*(100)) names(data.wk_Rsd_xyl)[1]<-"RSD" data.wk_Rsd_xyl$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_Conf_xyl <- as.data.frame((data.wk_sd_xyl$SD*2.120 )/sqrt(data.wk_n_xyl$n)) names(data.wk_Conf_xyl)[1]<-"Confidence interval of the mean" data.wk_Conf_xyl$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_var_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), var, na.rm=TRUE) names(data.wk_var_xyl)[1]<-"Substrate" names(data.wk_var_xyl)[2]<-"Variance" data.wk_min_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), min, na.rm=TRUE) names(data.wk_min_xyl)[1]<-"Substrate" names(data.wk_min_xyl)[2]<-"Min" data.wk_max_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), max, na.rm=TRUE) names(data.wk_max_xyl)[1]<-"Substrate" names(data.wk_max_xyl)[2]<-"Max" data.wk_ran_xyl<-as.data.frame(data.wk_max_xyl$Max-data.wk_min_xyl$Min) names(data.wk_ran_xyl)[1]<-"Range" data.wk_ran_xyl$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_firqua_xyl<-as.data.frame(tapply(data.wk$pteh.X.y, as.factor(data.wk$IDB), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_xyl)[1]<-"First quantile" data.wk_firqua_xyl$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_lasquat_xyl<-as.data.frame(tapply(data.wk$pteh.X.y, as.factor(data.wk$IDB), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_xyl)[1]<-"Last quantile" data.wk_lasquat_xyl$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") # Summary of data.wkset pteh.X.y summaryxylG<-merge(data.wk_n_xyl,data.wk_ave_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_med_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_sd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Rsd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Conf_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_var_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_min_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_max_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_ran_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_firqua_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_lasquat_xyl, by="Substrate") summaryxylG$Biomass.num <- c(1,2,3,4,5,6,7,8,9) summaryxylG$carbohydrate<-rep("Xylose.pteh.y",9) # # Summary of data.wkset per substrate & modality for Glucose.Xylose data.wk_n_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), length) names(data.wk_n_pteh.GX.y)[1]<-"Substrate" names(data.wk_n_pteh.GX.y)[2]<-"n" data.wk_ave_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), mean, na.rm=TRUE) names(data.wk_ave_pteh.GX.y)[1]<-"Substrate" names(data.wk_ave_pteh.GX.y)[2]<-"Mean" data.wk_med_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), median, na.rm=TRUE) names(data.wk_med_pteh.GX.y)[1]<-"Substrate" names(data.wk_med_pteh.GX.y)[2]<-"Median" data.wk_sd_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), sd, na.rm=TRUE) names(data.wk_sd_pteh.GX.y)[1]<-"Substrate" names(data.wk_sd_pteh.GX.y)[2]<-"SD" data.wk_Rsd_pteh.GX.y <- as.data.frame((data.wk_sd_pteh.GX.y$SD/data.wk_ave_pteh.GX.y$Mean)*(100)) names(data.wk_Rsd_pteh.GX.y)[1]<-"RSD" data.wk_Rsd_pteh.GX.y$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_Conf_pteh_GX.y <- as.data.frame((data.wk_sd_pteh.GX.y$SD*2.120 )/sqrt(data.wk_n_pteh.GX.y$n)) names(data.wk_Conf_pteh_GX.y)[1]<-"Confidence interval of the mean" data.wk_Conf_pteh_GX.y$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_var_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), var, na.rm=TRUE) names(data.wk_var_pteh.GX.y)[1]<-"Substrate" names(data.wk_var_pteh.GX.y)[2]<-"Variance" data.wk_min_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), min, na.rm=TRUE) names(data.wk_min_pteh.GX.y)[1]<-"Substrate" names(data.wk_min_pteh.GX.y)[2]<-"Min" data.wk_max_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), max, na.rm=TRUE) names(data.wk_max_pteh.GX.y)[1]<-"Substrate" names(data.wk_max_pteh.GX.y)[2]<-"Max" data.wk_ran_pteh.GX.y<-as.data.frame(data.wk_max_pteh.GX.y$Max-data.wk_min_pteh.GX.y$Min) names(data.wk_ran_pteh.GX.y)[1]<-"Range" data.wk_ran_pteh.GX.y$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_firqua_pteh.GX.y<-as.data.frame(tapply(data.wk$pteh.GX.y, as.factor(data.wk$IDB), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_pteh.GX.y)[1]<-"First quantile" data.wk_firqua_pteh.GX.y$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_lasquat_pteh.GX.y<-as.data.frame(tapply(data.wk$pteh.GX.y, as.factor(data.wk$IDB), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_pteh.GX.y)[1]<-"Last quantile" data.wk_lasquat_pteh.GX.y$Substrate <- c("Kramer 33a14_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") # Summary of data.wkset Glucose.Xylose summarypteh.GX.yG<-merge(data.wk_n_pteh.GX.y,data.wk_ave_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_med_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_sd_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_Rsd_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_Conf_pteh_GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_var_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_min_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_max_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_ran_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_firqua_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_lasquat_pteh.GX.y, by="Substrate") summarypteh.GX.yG$Biomass.num <- c(1,2,3,4,5,6,7,8,9) summarypteh.GX.yG$carbohydrate<-rep("Glucose.Xylose.pteh.y",9) # # Merge subset / summary summary.pteh.G<-rbind(summarygluG,summaryxylG,summarypteh.GX.yG) summary.pteh.G<-separate(data = summary.pteh.G, col = Substrate, into = c("Substrate", "Temp"), sep = "_") write.csv(summary.pteh.G, file ="ASE350-summary-pteh-G-y-59-DELO.csv") # # # # PART Two B (per Batch) rm(list=ls(all=TRUE)) # # # # # set working file data.wk <- read.csv("ASE350-set59-data-DELO.csv", header = TRUE) data.wk$ID <- paste(data.wk$Sample, data.wk$Temp, data.wk$Batch , sep="_") #data.wk$ID <- paste(data.wk$Sample, data.wk$Temp , sep="_") a<-nrow(data.wk) data.wk$row.names.g <- cbind(data.wk$row.names, 1:a) rm(a) # # Outlier #outlier <- which(data.wk$WStid=="18767") #data.wk <- data.wk[-outlier,] # # Divide data according to the substrate subset.1<-subset(data.wk, data.wk$Sample=="Kramer 33a14") subset.2<-subset(data.wk, data.wk$Sample=="Sorghum wild") subset.3<-subset(data.wk, data.wk$Sample=="Sorghum Stacked") subset.4<-subset(data.wk, data.wk$Sample=="Sorghum BMR6") subset.5<-subset(data.wk, data.wk$Sample=="Sorghum BMR12") data.sor.all <- rbind(subset.2,subset.3,subset.4,subset.5) data.sor.sel <- rbind(subset.2,subset.3) # # Summary of data.wkset per substrate & modality for pteh.G.y data.wk_n_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), length) names(data.wk_n_glu)[1]<-"Substrate" names(data.wk_n_glu)[2]<-"n" data.wk_ave_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), mean, na.rm=TRUE) names(data.wk_ave_glu)[1]<-"Substrate" names(data.wk_ave_glu)[2]<-"Mean" data.wk_med_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), median, na.rm=TRUE) names(data.wk_med_glu)[1]<-"Substrate" names(data.wk_med_glu)[2]<-"Median" data.wk_sd_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), sd, na.rm=TRUE) names(data.wk_sd_glu)[1]<-"Substrate" names(data.wk_sd_glu)[2]<-"SD" data.wk_Rsd_glu <- as.data.frame((data.wk_sd_glu$SD/data.wk_ave_glu$Mean)*(100)) names(data.wk_Rsd_glu)[1]<-"RSD" data.wk_Rsd_glu$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_Conf_glu <- as.data.frame((data.wk_sd_glu$SD*2.306)/sqrt(data.wk_n_glu$n)) names(data.wk_Conf_glu)[1]<-"Confidence interval of the mean" data.wk_Conf_glu$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_var_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), var, na.rm=TRUE) names(data.wk_var_glu)[1]<-"Substrate" names(data.wk_var_glu)[2]<-"Variance" data.wk_min_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), min, na.rm=TRUE) names(data.wk_min_glu)[1]<-"Substrate" names(data.wk_min_glu)[2]<-"Min" data.wk_max_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), max, na.rm=TRUE) names(data.wk_max_glu)[1]<-"Substrate" names(data.wk_max_glu)[2]<-"Max" data.wk_ran_glu<-as.data.frame(data.wk_max_glu$Max-data.wk_min_glu$Min) names(data.wk_ran_glu)[1]<-"Range" data.wk_ran_glu$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_firqua_glu<-as.data.frame(tapply(data.wk$pteh.G.y, as.factor(data.wk$ID), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_glu)[1]<-"First quantile" data.wk_firqua_glu$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_lasquat_glu<-as.data.frame(tapply(data.wk$pteh.G.y, as.factor(data.wk$ID), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_glu)[1]<-"Last quantile" data.wk_lasquat_glu$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") # Summary of data.wkset pteh.G.y summarygluG<-merge(data.wk_n_glu,data.wk_ave_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_med_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_sd_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_Rsd_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_Conf_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_var_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_min_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_max_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_ran_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_firqua_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_lasquat_glu, by="Substrate") summarygluG$Biomass.num <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18) summarygluG$carbohydrate<-rep("Glucose.pteh.y",18) # # Summary of data.wkset per substrate & modality for pteh.X.y data.wk_n_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), length) names(data.wk_n_xyl)[1]<-"Substrate" names(data.wk_n_xyl)[2]<-"n" data.wk_ave_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), mean, na.rm=TRUE) names(data.wk_ave_xyl)[1]<-"Substrate" names(data.wk_ave_xyl)[2]<-"Mean" data.wk_med_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), median, na.rm=TRUE) names(data.wk_med_xyl)[1]<-"Substrate" names(data.wk_med_xyl)[2]<-"Median" data.wk_sd_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), sd, na.rm=TRUE) names(data.wk_sd_xyl)[1]<-"Substrate" names(data.wk_sd_xyl)[2]<-"SD" data.wk_Rsd_xyl <- as.data.frame((data.wk_sd_xyl$SD/data.wk_ave_xyl$Mean)*(100)) names(data.wk_Rsd_xyl)[1]<-"RSD" data.wk_Rsd_xyl$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_Conf_xyl <- as.data.frame((data.wk_sd_xyl$SD*2.306)/sqrt(data.wk_n_xyl$n)) names(data.wk_Conf_xyl)[1]<-"Confidence interval of the mean" data.wk_Conf_xyl$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_var_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), var, na.rm=TRUE) names(data.wk_var_xyl)[1]<-"Substrate" names(data.wk_var_xyl)[2]<-"Variance" data.wk_min_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), min, na.rm=TRUE) names(data.wk_min_xyl)[1]<-"Substrate" names(data.wk_min_xyl)[2]<-"Min" data.wk_max_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), max, na.rm=TRUE) names(data.wk_max_xyl)[1]<-"Substrate" names(data.wk_max_xyl)[2]<-"Max" data.wk_ran_xyl<-as.data.frame(data.wk_max_xyl$Max-data.wk_min_xyl$Min) names(data.wk_ran_xyl)[1]<-"Range" data.wk_ran_xyl$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_firqua_xyl<-as.data.frame(tapply(data.wk$pteh.X.y, as.factor(data.wk$ID), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_xyl)[1]<-"First quantile" data.wk_firqua_xyl$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_lasquat_xyl<-as.data.frame(tapply(data.wk$pteh.X.y, as.factor(data.wk$ID), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_xyl)[1]<-"Last quantile" data.wk_lasquat_xyl$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") # Summary of data.wkset pteh.X.y summaryxylG<-merge(data.wk_n_xyl,data.wk_ave_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_med_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_sd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Rsd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Conf_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_var_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_min_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_max_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_ran_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_firqua_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_lasquat_xyl, by="Substrate") summaryxylG$Biomass.num <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18) summaryxylG$carbohydrate<-rep("Xylose.pteh.y",18) # # Summary of data.wkset per substrate & modality for Glucose.Xylose data.wk_n_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), length) names(data.wk_n_pteh.GX.y)[1]<-"Substrate" names(data.wk_n_pteh.GX.y)[2]<-"n" data.wk_ave_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), mean, na.rm=TRUE) names(data.wk_ave_pteh.GX.y)[1]<-"Substrate" names(data.wk_ave_pteh.GX.y)[2]<-"Mean" data.wk_med_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), median, na.rm=TRUE) names(data.wk_med_pteh.GX.y)[1]<-"Substrate" names(data.wk_med_pteh.GX.y)[2]<-"Median" data.wk_sd_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), sd, na.rm=TRUE) names(data.wk_sd_pteh.GX.y)[1]<-"Substrate" names(data.wk_sd_pteh.GX.y)[2]<-"SD" data.wk_Rsd_pteh.GX.y <- as.data.frame((data.wk_sd_pteh.GX.y$SD/data.wk_ave_pteh.GX.y$Mean)*(100)) names(data.wk_Rsd_pteh.GX.y)[1]<-"RSD" data.wk_Rsd_pteh.GX.y$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_Conf_pteh.GX.y <- as.data.frame((data.wk_sd_pteh.GX.y$SD*2.306)/sqrt(data.wk_n_pteh.GX.y$n)) names(data.wk_Conf_pteh.GX.y)[1]<-"Confidence interval of the mean" data.wk_Conf_pteh.GX.y$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_var_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), var, na.rm=TRUE) names(data.wk_var_pteh.GX.y)[1]<-"Substrate" names(data.wk_var_pteh.GX.y)[2]<-"Variance" data.wk_min_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), min, na.rm=TRUE) names(data.wk_min_pteh.GX.y)[1]<-"Substrate" names(data.wk_min_pteh.GX.y)[2]<-"Min" data.wk_max_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), max, na.rm=TRUE) names(data.wk_max_pteh.GX.y)[1]<-"Substrate" names(data.wk_max_pteh.GX.y)[2]<-"Max" data.wk_ran_pteh.GX.y<-as.data.frame(data.wk_max_pteh.GX.y$Max-data.wk_min_pteh.GX.y$Min) names(data.wk_ran_pteh.GX.y)[1]<-"Range" data.wk_ran_pteh.GX.y$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_firqua_pteh.GX.y<-as.data.frame(tapply(data.wk$pteh.GX.y, as.factor(data.wk$ID), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_pteh.GX.y)[1]<-"First quantile" data.wk_firqua_pteh.GX.y$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_lasquat_pteh.GX.y<-as.data.frame(tapply(data.wk$pteh.GX.y, as.factor(data.wk$ID), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_pteh.GX.y)[1]<-"Last quantile" data.wk_lasquat_pteh.GX.y$Substrate <- c("Kramer 33a14_130_A", "Kramer 33a14_130_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") # Summary of data.wkset Glucose.Xylose summarypteh.GX.yG<-merge(data.wk_n_pteh.GX.y,data.wk_ave_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_med_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_sd_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_Rsd_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_Conf_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_var_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_min_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_max_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_ran_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_firqua_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_lasquat_pteh.GX.y, by="Substrate") summarypteh.GX.yG$Biomass.num <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18) summarypteh.GX.yG$carbohydrate<-rep("Glucose.Xylose.pteh.y",18) # # Merge subset / summary summary.pteh.G<-rbind(summarygluG,summaryxylG,summarypteh.GX.yG) summary.pteh.G<-separate(data = summary.pteh.G, col = Substrate, into = c("Substrate", "Temp", "Batch"), sep = "_") write.csv(summary.pteh.G, file ="ASE350-summary-pteh-G-y-59-batch-DELO.csv") # # # # PART Two C rm(list=ls(all=TRUE)) # # # # # set working file data.wk <- read.csv("ASE350-set78-data-DELO.csv", header = TRUE) data.wk$ID <- paste(data.wk$Sample, data.wk$Temp, data.wk$Batch , sep="_") data.wk$IDB <- paste(data.wk$Sample, data.wk$Temp , sep="_") a<-nrow(data.wk) data.wk$row.names.g <- cbind(data.wk$row.names, 1:a) rm(a) # # Outlier #outlier <- which(data.wk$WStid=="18716" | data.wk$WStid=="18767") #data.wk <- data.wk[-outlier,] # # Divide data according to the substrate subset.1<-subset(data.wk, data.wk$Sample=="Kramer 33a14") subset.2<-subset(data.wk, data.wk$Sample=="Sorghum wild") subset.3<-subset(data.wk, data.wk$Sample=="Sorghum Stacked") subset.4<-subset(data.wk, data.wk$Sample=="Sorghum BMR6") subset.5<-subset(data.wk, data.wk$Sample=="Sorghum BMR12") data.sor.all <- rbind(subset.2,subset.3,subset.4,subset.5) data.sor.sel <- rbind(subset.2,subset.3) # # Summary of data.wkset per substrate & modality for pteh.G.y data.wk_n_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), length) names(data.wk_n_glu)[1]<-"Substrate" names(data.wk_n_glu)[2]<-"n" data.wk_ave_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), mean, na.rm=TRUE) names(data.wk_ave_glu)[1]<-"Substrate" names(data.wk_ave_glu)[2]<-"Mean" data.wk_med_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), median, na.rm=TRUE) names(data.wk_med_glu)[1]<-"Substrate" names(data.wk_med_glu)[2]<-"Median" data.wk_sd_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), sd, na.rm=TRUE) names(data.wk_sd_glu)[1]<-"Substrate" names(data.wk_sd_glu)[2]<-"SD" data.wk_Rsd_glu <- as.data.frame((data.wk_sd_glu$SD/data.wk_ave_glu$Mean)*(100)) names(data.wk_Rsd_glu)[1]<-"RSD" data.wk_Rsd_glu$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_Conf_glu <- as.data.frame((data.wk_sd_glu$SD*2.120)/sqrt(data.wk_n_glu$n)) names(data.wk_Conf_glu)[1]<-"Confidence interval of the mean" data.wk_Conf_glu$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_var_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), var, na.rm=TRUE) names(data.wk_var_glu)[1]<-"Substrate" names(data.wk_var_glu)[2]<-"Variance" data.wk_min_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), min, na.rm=TRUE) names(data.wk_min_glu)[1]<-"Substrate" names(data.wk_min_glu)[2]<-"Min" data.wk_max_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$IDB)), max, na.rm=TRUE) names(data.wk_max_glu)[1]<-"Substrate" names(data.wk_max_glu)[2]<-"Max" data.wk_ran_glu<-as.data.frame(data.wk_max_glu$Max-data.wk_min_glu$Min) names(data.wk_ran_glu)[1]<-"Range" data.wk_ran_glu$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_firqua_glu<-as.data.frame(tapply(data.wk$pteh.G.y, as.factor(data.wk$IDB), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_glu)[1]<-"First quantile" data.wk_firqua_glu$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_lasquat_glu<-as.data.frame(tapply(data.wk$pteh.G.y, as.factor(data.wk$IDB), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_glu)[1]<-"Last quantile" data.wk_lasquat_glu$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") # Summary of data.wkset pteh.G.y summarygluG<-merge(data.wk_n_glu,data.wk_ave_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_med_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_sd_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_Rsd_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_Conf_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_var_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_min_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_max_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_ran_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_firqua_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_lasquat_glu, by="Substrate") summarygluG$Biomass.num <- c(1,2,3,4,5,6,7,8,9) summarygluG$carbohydrate<-rep("Glucose.PTEH.y-DELO",9) # # Summary of data.wkset per substrate & modality for pteh.X.y data.wk_n_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), length) names(data.wk_n_xyl)[1]<-"Substrate" names(data.wk_n_xyl)[2]<-"n" data.wk_ave_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), mean, na.rm=TRUE) names(data.wk_ave_xyl)[1]<-"Substrate" names(data.wk_ave_xyl)[2]<-"Mean" data.wk_med_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), median, na.rm=TRUE) names(data.wk_med_xyl)[1]<-"Substrate" names(data.wk_med_xyl)[2]<-"Median" data.wk_sd_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), sd, na.rm=TRUE) names(data.wk_sd_xyl)[1]<-"Substrate" names(data.wk_sd_xyl)[2]<-"SD" data.wk_Rsd_xyl <- as.data.frame((data.wk_sd_xyl$SD/data.wk_ave_xyl$Mean)*(100)) names(data.wk_Rsd_xyl)[1]<-"RSD" data.wk_Rsd_xyl$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_Conf_xyl <- as.data.frame((data.wk_sd_xyl$SD*2.120)/sqrt(data.wk_n_xyl$n)) names(data.wk_Conf_xyl)[1]<-"Confidence interval of the mean" data.wk_Conf_xyl$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_var_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), var, na.rm=TRUE) names(data.wk_var_xyl)[1]<-"Substrate" names(data.wk_var_xyl)[2]<-"Variance" data.wk_min_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), min, na.rm=TRUE) names(data.wk_min_xyl)[1]<-"Substrate" names(data.wk_min_xyl)[2]<-"Min" data.wk_max_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$IDB)), max, na.rm=TRUE) names(data.wk_max_xyl)[1]<-"Substrate" names(data.wk_max_xyl)[2]<-"Max" data.wk_ran_xyl<-as.data.frame(data.wk_max_xyl$Max-data.wk_min_xyl$Min) names(data.wk_ran_xyl)[1]<-"Range" data.wk_ran_xyl$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_firqua_xyl<-as.data.frame(tapply(data.wk$pteh.X.y, as.factor(data.wk$IDB), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_xyl)[1]<-"First quantile" data.wk_firqua_xyl$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_lasquat_xyl<-as.data.frame(tapply(data.wk$pteh.X.y, as.factor(data.wk$IDB), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_xyl)[1]<-"Last quantile" data.wk_lasquat_xyl$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") # Summary of data.wkset pteh.X.y summaryxylG<-merge(data.wk_n_xyl,data.wk_ave_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_med_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_sd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Rsd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Conf_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_var_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_min_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_max_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_ran_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_firqua_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_lasquat_xyl, by="Substrate") summaryxylG$Biomass.num <- c(1,2,3,4,5,6,7,8,9) summaryxylG$carbohydrate<-rep("Xylose.PTEH.y-DELO",9) # # Summary of data.wkset per substrate & modality for Glucose.Xylose data.wk_n_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), length) names(data.wk_n_pteh.GX.y)[1]<-"Substrate" names(data.wk_n_pteh.GX.y)[2]<-"n" data.wk_ave_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), mean, na.rm=TRUE) names(data.wk_ave_pteh.GX.y)[1]<-"Substrate" names(data.wk_ave_pteh.GX.y)[2]<-"Mean" data.wk_med_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), median, na.rm=TRUE) names(data.wk_med_pteh.GX.y)[1]<-"Substrate" names(data.wk_med_pteh.GX.y)[2]<-"Median" data.wk_sd_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), sd, na.rm=TRUE) names(data.wk_sd_pteh.GX.y)[1]<-"Substrate" names(data.wk_sd_pteh.GX.y)[2]<-"SD" data.wk_Rsd_pteh.GX.y <- as.data.frame((data.wk_sd_pteh.GX.y$SD/data.wk_ave_pteh.GX.y$Mean)*(100)) names(data.wk_Rsd_pteh.GX.y)[1]<-"RSD" data.wk_Rsd_pteh.GX.y$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_Conf_pteh_GX.y <- as.data.frame((data.wk_sd_pteh.GX.y$SD*2.120)/sqrt(data.wk_n_pteh.GX.y$n)) names(data.wk_Conf_pteh_GX.y)[1]<-"Confidence interval of the mean" data.wk_Conf_pteh_GX.y$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_var_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), var, na.rm=TRUE) names(data.wk_var_pteh.GX.y)[1]<-"Substrate" names(data.wk_var_pteh.GX.y)[2]<-"Variance" data.wk_min_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), min, na.rm=TRUE) names(data.wk_min_pteh.GX.y)[1]<-"Substrate" names(data.wk_min_pteh.GX.y)[2]<-"Min" data.wk_max_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$IDB)), max, na.rm=TRUE) names(data.wk_max_pteh.GX.y)[1]<-"Substrate" names(data.wk_max_pteh.GX.y)[2]<-"Max" data.wk_ran_pteh.GX.y<-as.data.frame(data.wk_max_pteh.GX.y$Max-data.wk_min_pteh.GX.y$Min) names(data.wk_ran_pteh.GX.y)[1]<-"Range" data.wk_ran_pteh.GX.y$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_firqua_pteh.GX.y<-as.data.frame(tapply(data.wk$pteh.GX.y, as.factor(data.wk$IDB), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_pteh.GX.y)[1]<-"First quantile" data.wk_firqua_pteh.GX.y$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") data.wk_lasquat_pteh.GX.y<-as.data.frame(tapply(data.wk$pteh.GX.y, as.factor(data.wk$IDB), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_pteh.GX.y)[1]<-"Last quantile" data.wk_lasquat_pteh.GX.y$Substrate <- c("Kramer 33a14_160", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum wild_150", "Sorghum wild_160") # Summary of data.wkset Glucose.Xylose summarypteh.GX.yG<-merge(data.wk_n_pteh.GX.y,data.wk_ave_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_med_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_sd_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_Rsd_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_Conf_pteh_GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_var_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_min_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_max_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_ran_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_firqua_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_lasquat_pteh.GX.y, by="Substrate") summarypteh.GX.yG$Biomass.num <- c(1,2,3,4,5,6,7,8,9) summarypteh.GX.yG$carbohydrate<-rep("Glucose.Xylose.PTEH.y-DELO",9) # # Merge subset / summary summary.pteh.G<-rbind(summarygluG,summaryxylG,summarypteh.GX.yG) summary.pteh.G<-separate(data = summary.pteh.G, col = Substrate, into = c("Substrate", "Temp"), sep = "_") write.csv(summary.pteh.G, file ="ASE350-summary-pteh-g-y-78-DELO.csv") # # # # PART Two D (per Batch) rm(list=ls(all=TRUE)) # # # # # set working file data.wk <- read.csv("ASE350-set78-data-DELO.csv", header = TRUE) data.wk$ID <- paste(data.wk$Sample, data.wk$Temp, data.wk$Batch , sep="_") #data.wk$ID <- paste(data.wk$Sample, data.wk$Temp , sep="_") a<-nrow(data.wk) data.wk$row.names.g <- cbind(data.wk$row.names, 1:a) rm(a) # # Outlier #outlier <- which(data.wk$WStid=="18716" | data.wk$WStid=="18767") #data.wk <- data.wk[-outlier,] # # Divide data according to the substrate subset.1<-subset(data.wk, data.wk$Sample=="Kramer 33a14") subset.2<-subset(data.wk, data.wk$Sample=="Sorghum wild") subset.3<-subset(data.wk, data.wk$Sample=="Sorghum Stacked") subset.4<-subset(data.wk, data.wk$Sample=="Sorghum BMR6") subset.5<-subset(data.wk, data.wk$Sample=="Sorghum BMR12") data.sor.all <- rbind(subset.2,subset.3,subset.4,subset.5) data.sor.sel <- rbind(subset.2,subset.3) # # Summary of data.wkset per substrate & modality for pteh.G.y data.wk_n_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), length) names(data.wk_n_glu)[1]<-"Substrate" names(data.wk_n_glu)[2]<-"n" data.wk_ave_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), mean, na.rm=TRUE) names(data.wk_ave_glu)[1]<-"Substrate" names(data.wk_ave_glu)[2]<-"Mean" data.wk_med_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), median, na.rm=TRUE) names(data.wk_med_glu)[1]<-"Substrate" names(data.wk_med_glu)[2]<-"Median" data.wk_sd_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), sd, na.rm=TRUE) names(data.wk_sd_glu)[1]<-"Substrate" names(data.wk_sd_glu)[2]<-"SD" data.wk_Rsd_glu <- as.data.frame((data.wk_sd_glu$SD/data.wk_ave_glu$Mean)*(100)) names(data.wk_Rsd_glu)[1]<-"RSD" data.wk_Rsd_glu$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_Conf_glu <- as.data.frame((data.wk_sd_glu$SD*2.306 )/sqrt(data.wk_n_glu$n)) names(data.wk_Conf_glu)[1]<-"Confidence interval of the mean" data.wk_Conf_glu$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_var_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), var, na.rm=TRUE) names(data.wk_var_glu)[1]<-"Substrate" names(data.wk_var_glu)[2]<-"Variance" data.wk_min_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), min, na.rm=TRUE) names(data.wk_min_glu)[1]<-"Substrate" names(data.wk_min_glu)[2]<-"Min" data.wk_max_glu <- aggregate(data.wk$pteh.G.y, by=list(as.factor(data.wk$ID)), max, na.rm=TRUE) names(data.wk_max_glu)[1]<-"Substrate" names(data.wk_max_glu)[2]<-"Max" data.wk_ran_glu<-as.data.frame(data.wk_max_glu$Max-data.wk_min_glu$Min) names(data.wk_ran_glu)[1]<-"Range" data.wk_ran_glu$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_firqua_glu<-as.data.frame(tapply(data.wk$pteh.G.y, as.factor(data.wk$ID), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_glu)[1]<-"First quantile" data.wk_firqua_glu$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_lasquat_glu<-as.data.frame(tapply(data.wk$pteh.G.y, as.factor(data.wk$ID), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_glu)[1]<-"Last quantile" data.wk_lasquat_glu$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") # Summary of data.wkset pteh.G.y summarygluG<-merge(data.wk_n_glu,data.wk_ave_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_med_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_sd_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_Rsd_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_Conf_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_var_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_min_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_max_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_ran_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_firqua_glu, by="Substrate") summarygluG<-merge(summarygluG,data.wk_lasquat_glu, by="Substrate") summarygluG$Biomass.num <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18) summarygluG$carbohydrate<-rep("Glucose.PTEH.y-DELO",9) # # Summary of data.wkset per substrate & modality for pteh.X.y data.wk_n_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), length) names(data.wk_n_xyl)[1]<-"Substrate" names(data.wk_n_xyl)[2]<-"n" data.wk_ave_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), mean, na.rm=TRUE) names(data.wk_ave_xyl)[1]<-"Substrate" names(data.wk_ave_xyl)[2]<-"Mean" data.wk_med_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), median, na.rm=TRUE) names(data.wk_med_xyl)[1]<-"Substrate" names(data.wk_med_xyl)[2]<-"Median" data.wk_sd_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), sd, na.rm=TRUE) names(data.wk_sd_xyl)[1]<-"Substrate" names(data.wk_sd_xyl)[2]<-"SD" data.wk_Rsd_xyl <- as.data.frame((data.wk_sd_xyl$SD/data.wk_ave_xyl$Mean)*(100)) names(data.wk_Rsd_xyl)[1]<-"RSD" data.wk_Rsd_xyl$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_Conf_xyl <- as.data.frame((data.wk_sd_xyl$SD*2.306 )/sqrt(data.wk_n_xyl$n)) names(data.wk_Conf_xyl)[1]<-"Confidence interval of the mean" data.wk_Conf_xyl$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_var_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), var, na.rm=TRUE) names(data.wk_var_xyl)[1]<-"Substrate" names(data.wk_var_xyl)[2]<-"Variance" data.wk_min_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), min, na.rm=TRUE) names(data.wk_min_xyl)[1]<-"Substrate" names(data.wk_min_xyl)[2]<-"Min" data.wk_max_xyl <- aggregate(data.wk$pteh.X.y, by=list(as.factor(data.wk$ID)), max, na.rm=TRUE) names(data.wk_max_xyl)[1]<-"Substrate" names(data.wk_max_xyl)[2]<-"Max" data.wk_ran_xyl<-as.data.frame(data.wk_max_xyl$Max-data.wk_min_xyl$Min) names(data.wk_ran_xyl)[1]<-"Range" data.wk_ran_xyl$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_firqua_xyl<-as.data.frame(tapply(data.wk$pteh.X.y, as.factor(data.wk$ID), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_xyl)[1]<-"First quantile" data.wk_firqua_xyl$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_lasquat_xyl<-as.data.frame(tapply(data.wk$pteh.X.y, as.factor(data.wk$ID), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_xyl)[1]<-"Last quantile" data.wk_lasquat_xyl$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") # Summary of data.wkset pteh.X.y summaryxylG<-merge(data.wk_n_xyl,data.wk_ave_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_med_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_sd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Rsd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Conf_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_var_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_min_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_max_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_ran_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_firqua_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_lasquat_xyl, by="Substrate") summaryxylG$Biomass.num <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18) summaryxylG$carbohydrate<-rep("Xylose.PTEH.y-DELO",9) # # Summary of data.wkset per substrate & modality for Glucose.Xylose data.wk_n_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), length) names(data.wk_n_pteh.GX.y)[1]<-"Substrate" names(data.wk_n_pteh.GX.y)[2]<-"n" data.wk_ave_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), mean, na.rm=TRUE) names(data.wk_ave_pteh.GX.y)[1]<-"Substrate" names(data.wk_ave_pteh.GX.y)[2]<-"Mean" data.wk_med_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), median, na.rm=TRUE) names(data.wk_med_pteh.GX.y)[1]<-"Substrate" names(data.wk_med_pteh.GX.y)[2]<-"Median" data.wk_sd_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), sd, na.rm=TRUE) names(data.wk_sd_pteh.GX.y)[1]<-"Substrate" names(data.wk_sd_pteh.GX.y)[2]<-"SD" data.wk_Rsd_pteh.GX.y <- as.data.frame((data.wk_sd_pteh.GX.y$SD/data.wk_ave_pteh.GX.y$Mean)*(100)) names(data.wk_Rsd_pteh.GX.y)[1]<-"RSD" data.wk_Rsd_pteh.GX.y$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_Conf_pteh.GX.y <- as.data.frame((data.wk_sd_pteh.GX.y$SD*2.306 )/sqrt(data.wk_n_pteh.GX.y$n)) names(data.wk_Conf_pteh.GX.y)[1]<-"Confidence interval of the mean" data.wk_Conf_pteh.GX.y$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_var_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), var, na.rm=TRUE) names(data.wk_var_pteh.GX.y)[1]<-"Substrate" names(data.wk_var_pteh.GX.y)[2]<-"Variance" data.wk_min_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), min, na.rm=TRUE) names(data.wk_min_pteh.GX.y)[1]<-"Substrate" names(data.wk_min_pteh.GX.y)[2]<-"Min" data.wk_max_pteh.GX.y <- aggregate(data.wk$pteh.GX.y, by=list(as.factor(data.wk$ID)), max, na.rm=TRUE) names(data.wk_max_pteh.GX.y)[1]<-"Substrate" names(data.wk_max_pteh.GX.y)[2]<-"Max" data.wk_ran_pteh.GX.y<-as.data.frame(data.wk_max_pteh.GX.y$Max-data.wk_min_pteh.GX.y$Min) names(data.wk_ran_pteh.GX.y)[1]<-"Range" data.wk_ran_pteh.GX.y$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_firqua_pteh.GX.y<-as.data.frame(tapply(data.wk$pteh.GX.y, as.factor(data.wk$ID), quantile, 0.25, na.rm=TRUE)) names(data.wk_firqua_pteh.GX.y)[1]<-"First quantile" data.wk_firqua_pteh.GX.y$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") data.wk_lasquat_pteh.GX.y<-as.data.frame(tapply(data.wk$pteh.GX.y, as.factor(data.wk$ID), quantile, 0.75, na.rm=TRUE)) names(data.wk_lasquat_pteh.GX.y)[1]<-"Last quantile" data.wk_lasquat_pteh.GX.y$Substrate <- c("Kramer 33a14_160_A", "Kramer 33a14_160_B", "Sorghum BMR12_150_A","Sorghum BMR12_150_B","Sorghum BMR12_160_A","Sorghum BMR12_160_B", "Sorghum BMR6_150_A","Sorghum BMR6_150_B","Sorghum BMR6_160_A","Sorghum BMR6_160_B", "Sorghum Stacked_150_A", "Sorghum Stacked_150_B","Sorghum Stacked_160_A","Sorghum Stacked_160_B", "Sorghum wild_150_A","Sorghum wild_150_B","Sorghum wild_160_A","Sorghum wild_160_B") # Summary of data.wkset Glucose.Xylose summarypteh.GX.yG<-merge(data.wk_n_pteh.GX.y,data.wk_ave_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_med_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_sd_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_Rsd_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_Conf_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_var_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_min_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_max_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_ran_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_firqua_pteh.GX.y, by="Substrate") summarypteh.GX.yG<-merge(summarypteh.GX.yG,data.wk_lasquat_pteh.GX.y, by="Substrate") summarypteh.GX.yG$Biomass.num <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18) summarypteh.GX.yG$carbohydrate<-rep("Glucose.Xylose.PTEH.y-DELO",9) # # Merge subset / summary summary.pteh.G<-rbind(summarygluG,summaryxylG,summarypteh.GX.yG) summary.pteh.G<-separate(data = summary.pteh.G, col = Substrate, into = c("Substrate", "Temp", "Batch"), sep = "_") write.csv(summary.pteh.G, file ="ASE350-summary-pteh-g-y-78-batch-DELO.csv") # # # # PART Three rm(list=ls(all=TRUE)) # # # # # set working file 1 data.wk.1 <- read.csv("ASE350-set59-data-DELO.csv", header = TRUE) data.wk.1$ID <- paste(data.wk.1$Sample, data.wk.1$Temp, data.wk.1$Batch, "Acid only" , sep="_") a.1<-nrow(data.wk.1) data.wk.1$row.names.g <- cbind(data.wk.1$row.names, 1:a.1) rm(a.1) # # Outlier #outlier.1 <- which(data.wk.1$WStid=="18767") #data.wk.1 <- data.wk.1[-outlier.1,] # # Divide data according to the substrate subset.1.1<-subset(data.wk.1, data.wk.1$Sample=="Kramer 33a14") subset.2.1<-subset(data.wk.1, data.wk.1$Sample=="Sorghum wild") subset.3.1<-subset(data.wk.1, data.wk.1$Sample=="Sorghum Stacked") subset.4.1<-subset(data.wk.1, data.wk.1$Sample=="Sorghum BMR6") subset.5.1<-subset(data.wk.1, data.wk.1$Sample=="Sorghum BMR12") data.sor.all.1 <- rbind(subset.2.1,subset.3.1,subset.4.1,subset.5.1) data.sor.sel.1 <- rbind(subset.2.1,subset.3.1) # # set working file 2 data.wk.2 <- read.csv("ASE350-summary-pteh-G-y-59-batch-DELO.csv", header = TRUE) data.wk.2$ID <- paste(data.wk.2$Substrate, data.wk.2$Temp, data.wk.2$Batch, "Acid only" , sep="_") a.2<-nrow(data.wk.2) data.wk.2$row.names.g <- cbind(data.wk.2$row.names, 1:a.2) rm(a.2) toremove1.2 <- which(data.wk.2$Substrate=="Kramer 33a14") data.2 <- data.wk.2[-toremove1.2,] # # Divide data according to the variable subset.glu.agg.2 <-subset(data.2, data.2$carbohydrate=="Glucose.pteh.y") subset.xyl.agg.2 <-subset(data.2, data.2$carbohydrate=="Xylose.pteh.y") subset.glxy.agg.2 <-subset(data.2, data.2$carbohydrate=="Glucose.Xylose.pteh.y") # # set working directory 3 data.wk.3 <- read.csv("ASE350-set78-data-DELO.csv", header = TRUE) data.wk.3$ID <- paste(data.wk.3$Sample, data.wk.3$Temp, data.wk.3$Batch, "Deacetylation and Acid" , sep="_") a.3<-nrow(data.wk.3) data.wk.3$row.names.g <- cbind(data.wk.3$row.names, 1:a.3) rm(a.3) # # Outlier #outlier <- which(data.wk.3$WStid=="18716" | data.wk.3$WStid=="18767") #data.wk.3 <- data.wk.3[-outlier,] # # Divide data according to the substrate subset.1.2<-subset(data.wk.3, data.wk.3$Sample=="Kramer 33a14") subset.2.2<-subset(data.wk.3, data.wk.3$Sample=="Sorghum wild") subset.3.2<-subset(data.wk.3, data.wk.3$Sample=="Sorghum Stacked") subset.4.2<-subset(data.wk.3, data.wk.3$Sample=="Sorghum BMR6") subset.5.2<-subset(data.wk.3, data.wk.3$Sample=="Sorghum BMR12") data.sor.all.2 <- rbind(subset.2.2,subset.3.2,subset.4.2,subset.5.2) data.sor.sel.2 <- rbind(subset.2.2,subset.3.2) # # set working file 4 data.wk.4 <- read.csv("ASE350-summary-PTEH-g-y-78-batch-DELO.csv", header = TRUE) data.wk.4$ID <- paste(data.wk.4$Substrate, data.wk.4$Temp, data.wk.4$Batch, "Deacetylation and Acid" , sep="_") a.4<-nrow(data.wk.4) data.wk.4$row.names.g <- cbind(data.wk.4$row.names, 1:a.4) rm(a.4) toremove1.4 <- which(data.wk.4$Substrate=="Kramer 33a14") data.4 <- data.wk.4[-toremove1.4,] # # Divide data according to the variable subset.glu.agg.4 <-subset(data.4, data.4$carbohydrate=="Glucose.PTEH.y-DELO") subset.xyl.agg.4 <-subset(data.4, data.4$carbohydrate=="Xylose.PTEH.y-DELO") subset.glxy.agg.4 <-subset(data.4, data.4$carbohydrate=="Glucose.Xylose.PTEH.y-DELO") # # set working file 5 data.sor.all.1a <- data.sor.all.1[,c(1:4,4,5:8,15:24,25:30,31:36,37)] colnames(data.sor.all.1a)<-c("X","Sample","Ftid","LtidA","LtidC","WStid","Temp", "Batch","EHtoPT", "pt.Gt.r","pt.Xt.r","pt.At.r","pt.GXt.r","pt.GXAt.r","pt.Gt.y","pt.Xt.y","pt.At.y","pt.GXt.y","pt.GXAt.y", "eh.G.r","eh.X.r","eh.GX.r","eh.G.y","eh.X.y","eh.GX.y", "pteh.G.r","pteh.X.r","pteh.GX.r","pteh.G.y","pteh.X.y","pteh.GX.y", "ID") data.sor.all.2a <- data.sor.all.2[,c(1:3,4,5:9,12,14,17,20,23,27,29,32,35,38,40:45,46:51,52)] colnames(data.sor.all.2a)<-c("X","Sample","Ftid","LtidA","LtidC","WStid","Temp", "Batch","EHtoPT", "pt.Gt.r","pt.Xt.r","pt.At.r","pt.GXt.r","pt.GXAt.r","pt.Gt.y","pt.Xt.y","pt.At.y","pt.GXt.y","pt.GXAt.y", "eh.G.r","eh.X.r","eh.GX.r","eh.G.y","eh.X.y","eh.GX.y", "pteh.G.r","pteh.X.r","pteh.GX.r","pteh.G.y","pteh.X.y","pteh.GX.y", "ID") data.sor.all.raw <- rbind(data.sor.all.1a,data.sor.all.2a) data.sor.all.raw$PT <- c(rep("Acid only",32),rep("Deacetylation and Acid",32)) a.5<-nrow(data.sor.all.raw) data.sor.all.raw$row.names.g <- cbind(data.sor.all.raw$row.names, 1:a.5) rm(a.5) subset.2<-subset(data.sor.all.raw, data.sor.all.raw$Sample=="Sorghum wild") subset.3<-subset(data.sor.all.raw, data.sor.all.raw$Sample=="Sorghum Stacked") subset.4<-subset(data.sor.all.raw, data.sor.all.raw$Sample=="Sorghum BMR6") subset.5<-subset(data.sor.all.raw, data.sor.all.raw$Sample=="Sorghum BMR12") data.sor.all <- rbind(subset.2,subset.3,subset.4,subset.5) data.sor.sel <- rbind(subset.2,subset.3) subset.glu.agg <- rbind(subset.glu.agg.2,subset.glu.agg.4) subset.glu.agg <- subset.glu.agg[,-20] a.6<-nrow(subset.glu.agg) subset.glu.agg$row.names.g <- cbind(subset.glu.agg$row.names, 1:a.6) rm(a.6) subset.glu.agg$PT <- c(rep("Acid only",16),rep("Deacetylation and Acid",16)) subset.xyl.agg <- rbind(subset.xyl.agg.2,subset.xyl.agg.4) subset.xyl.agg <- subset.xyl.agg[,-20] a.7<-nrow(subset.xyl.agg) subset.xyl.agg$row.names.g <- cbind(subset.xyl.agg$row.names, 1:a.7) rm(a.7) subset.xyl.agg$PT <- c(rep("Acid only",16),rep("Deacetylation and Acid",16)) subset.glxy.agg <- rbind(subset.glxy.agg.2,subset.glxy.agg.4) subset.glxy.agg <- subset.glxy.agg[,-20] a.8<-nrow(subset.glxy.agg) subset.glxy.agg$row.names.g <- cbind(subset.glxy.agg$row.names, 1:a.8) rm(a.8) subset.glxy.agg$PT <- c(rep("Acid only",16),rep("Deacetylation and Acid",16)) # # Factor and levels data.sor.sel$Sample <- as.factor(data.sor.sel$Sample) data.sor.sel$Sample <- droplevels(data.sor.sel$Sample) data.sor.sel$Sample <- relevel(data.sor.sel$Sample, "Sorghum wild") data.sor.sel$Temp <- as.factor(data.sor.sel$Temp) data.sor.sel$Temp <- droplevels(data.sor.sel$Temp) data.sor.sel$Temp <- relevel(data.sor.sel$Temp, "150") data.sor.sel$PT <- as.factor(data.sor.sel$PT) data.sor.sel$PT <- droplevels(data.sor.sel$PT) data.sor.sel$PT <- relevel(data.sor.sel$PT, "Acid only") data.sor.sel$Batch <- as.factor(data.sor.sel$Batch) data.sor.sel$Batch <- droplevels(data.sor.sel$Batch) data.sor.sel$Batch <- relevel(data.sor.sel$Batch, "A") # # check normality # pteh.G.y tapply(data.sor.sel$pteh.G.y, as.factor(data.sor.sel$ID), shapiro.test) # pteh.X.y tapply(data.sor.sel$pteh.X.y, as.factor(data.sor.sel$ID), shapiro.test) # Glucose.Xylose.y tapply(data.sor.sel$pteh.GX.y, as.factor(data.sor.sel$ID), shapiro.test) # check equality of variance # pteh.G.y leveneTest(data.sor.sel$pteh.G.y, as.factor(data.sor.sel$ID), mean) leveneTest(data.sor.sel$pteh.G.y, as.factor(data.sor.sel$ID), median) # pteh.X.y leveneTest(data.sor.sel$pteh.X.y, as.factor(data.sor.sel$ID), mean) leveneTest(data.sor.sel$pteh.X.y, as.factor(data.sor.sel$ID), median) # Glucose.Xylose.y leveneTest(data.sor.sel$pteh.GX.y, as.factor(data.sor.sel$ID), mean) leveneTest(data.sor.sel$pteh.GX.y, as.factor(data.sor.sel$ID), median) # # anova 4 - pteh.G.y aov4.av1glu <- lm(pteh.G.y ~ Sample + as.factor(Temp) + PT + Batch + Sample*as.factor(Temp) + Sample*PT + PT*as.factor(Temp) , data=data.sor.sel) summary(aov4.av1glu) Anova(aov4.av1glu, type="II") aov4.av1glu.res=data.sor.sel aov4.av1glu.res$M1.Fit = fitted(aov4.av1glu) aov4.av1glu.res$M1.Resid = resid(aov4.av1glu) shapiro.test(aov4.av1glu.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov4.av1glu.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose PTEH yield", col=as.factor(aov4.av1glu.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov4.av1glu.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov4.av1glu.res$M1.Resid), sd=sd(aov4.av1glu.res$M1.Resid)), add=TRUE) qqnorm(aov4.av1glu.res$M1.Resid, main="Q-Q Plot - Glucose PTEH yield", cex.main=0.7) qqline(aov4.av1glu.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov4.av1glu.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose PTH yield", col=as.factor(aov4.av1glu.res$ID), pch=20, cex.main=0.7) lm1<-lm(pteh.G.y ~ as.factor(Sample) + as.factor(Temp) + PT + Batch , data=data.sor.sel) TukeyHSD(aov(lm1)) # # anova 4 - pteh.X.y aov4.av1xyl <- lm(pteh.X.y ~ Sample + as.factor(Temp) + PT + Batch + Sample*as.factor(Temp) + Sample*PT + PT*as.factor(Temp), data=data.sor.sel) summary(aov4.av1xyl) Anova(aov4.av1xyl, type="II") aov4.av1xyl.res=data.sor.sel aov4.av1xyl.res$M1.Fit = fitted(aov4.av1xyl) aov4.av1xyl.res$M1.Resid = resid(aov4.av1xyl) shapiro.test(aov4.av1xyl.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov4.av1xyl.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose PTEH yield", col=as.factor(aov4.av1xyl.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov4.av1xyl.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov4.av1xyl.res$M1.Resid), sd=sd(aov4.av1xyl.res$M1.Resid)), add=TRUE) qqnorm(aov4.av1xyl.res$M1.Resid, main="Q-Q Plot - Xylose PTEH yield", cex.main=0.7) qqline(aov4.av1xyl.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov4.av1xyl.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose PTEH yield", col=as.factor(aov4.av1xyl.res$ID), pch=20, cex.main=0.7) lm2<-lm(pteh.X.y ~ as.factor(Sample) + as.factor(Temp) + PT + Batch, data=data.sor.sel) TukeyHSD(aov(lm2)) # # anova 4 - pteh.GX.y aov4.av1glxy <- lm(pteh.GX.y ~ Sample + as.factor(Temp) + PT + Batch + Sample*as.factor(Temp) + Sample*PT + PT*as.factor(Temp) , data=data.sor.sel) summary(aov4.av1glxy) Anova(aov4.av1glxy, type="II") aov4.av1glxy.res=data.sor.sel aov4.av1glxy.res$M1.Fit = fitted(aov4.av1glxy) aov4.av1glxy.res$M1.Resid = resid(aov4.av1glxy) shapiro.test(aov4.av1glxy.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov4.av1glxy.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose.Xylose PTEH yield", col=as.factor(aov4.av1glxy.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov4.av1glxy.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose.Xylose PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov4.av1glxy.res$M1.Resid), sd=sd(aov4.av1glxy.res$M1.Resid)), add=TRUE) qqnorm(aov4.av1glxy.res$M1.Resid, main="Q-Q Plot - Glucose.Xylose PTEH yield", cex.main=0.7) qqline(aov4.av1glxy.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov4.av1glxy.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose.Xylose PTEH yield", col=as.factor(aov4.av1glxy.res$ID), pch=20, cex.main=0.7) lm3<-lm(pteh.GX.y ~ as.factor(Sample) + as.factor(Temp) + PT + Batch, data=data.sor.sel) TukeyHSD(aov(lm3)) # # Divide data according to the temperature subset.150 <-subset(data.sor.sel, data.sor.sel$Temp=="150") subset.160 <-subset(data.sor.sel, data.sor.sel$Temp=="160") # Equality of variance # pteh.G.y leveneTest(subset.150$pteh.G.y, as.factor(subset.150$ID), mean) leveneTest(subset.150$pteh.G.y, as.factor(subset.150$ID), median) leveneTest(subset.160$pteh.G.y, as.factor(subset.160$ID), mean) leveneTest(subset.160$pteh.G.y, as.factor(subset.160$ID), median) # pteh.X.y leveneTest(subset.150$pteh.X.y, as.factor(subset.150$ID), mean) leveneTest(subset.150$pteh.X.y, as.factor(subset.150$ID), median) leveneTest(subset.160$pteh.X.y, as.factor(subset.160$ID), mean) leveneTest(subset.160$pteh.X.y, as.factor(subset.160$ID), median) # pteh.GX.y leveneTest(subset.150$pteh.GX.y, as.factor(subset.150$ID), mean) leveneTest(subset.150$pteh.GX.y, as.factor(subset.150$ID), median) leveneTest(subset.160$pteh.GX.y, as.factor(subset.160$ID), mean) leveneTest(subset.160$pteh.GX.y, as.factor(subset.160$ID), median) # # Glucose #150 aov1.av1glu150 <- lm(pteh.G.y ~ Sample + PT + Batch + PT*Sample , data=subset.150) summary(aov1.av1glu150) Anova(aov1.av1glu150, type="II") aov1.av1glu150.res=subset.150 aov1.av1glu150.res$M1.Fit = fitted(aov1.av1glu150) aov1.av1glu150.res$M1.Resid = resid(aov1.av1glu150) shapiro.test(aov1.av1glu150.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.150$Sample, subset.150$PT, subset.150$pteh.G.y) windows(record=TRUE) interaction.plot(subset.150$PT, subset.150$Sample, subset.150$pteh.G.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glu150.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose 150 PTEH yield", col=as.factor(aov1.av1glu150.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov1.av1glu150.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose 150 PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov1.av1glu150.res$M1.Resid), sd=sd(aov1.av1glu150.res$M1.Resid)), add=TRUE) qqnorm(aov1.av1glu150.res$M1.Resid, main="Q-Q Plot - Glucose 150 PTEH yield", cex.main=0.7) qqline(aov1.av1glu150.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glu150.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose 150 PTEH yield", col=as.factor(aov1.av1glu150.res$ID), pch=20, cex.main=0.7) lm101<-aov(pteh.G.y ~ as.factor(Sample) + PT + Batch , data=subset.150) TukeyHSD(aov(lm101)) #160 aov1.av1glu160 <- aov(pteh.G.y ~ Sample + PT + Batch + PT*Sample , data=subset.160) summary(aov1.av1glu160) aov1.av1glu160.res=subset.160 aov1.av1glu160.res$M1.Fit = fitted(aov1.av1glu160) aov1.av1glu160.res$M1.Resid = resid(aov1.av1glu160) shapiro.test(aov1.av1glu160.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.160$Sample, subset.160$PT, subset.160$pteh.G.y) windows(record=TRUE) interaction.plot(subset.160$PT, subset.160$Sample, subset.160$pteh.G.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glu160.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose 160 pteh yield", col=as.factor(aov1.av1glu160.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov1.av1glu160.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose 160 pteh yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov1.av1glu160.res$M1.Resid), sd=sd(aov1.av1glu160.res$M1.Resid)), add=TRUE) qqnorm(aov1.av1glu160.res$M1.Resid, main="Q-Q Plot - Glucose 160 pteh yield", cex.main=0.7) qqline(aov1.av1glu160.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glu160.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose 160 pteh yield", col=as.factor(aov1.av1glu160.res$ID), pch=20, cex.main=0.7) lm102<-aov(pteh.G.y ~ as.factor(Sample) + PT + Batch , data=subset.160) TukeyHSD(aov(lm102)) # # Xylose #150 aov1.av1xyl150 <- lm(pteh.X.y ~ Sample + PT + Batch + PT*Sample , data=subset.150) summary(aov1.av1xyl150) Anova(aov1.av1xyl150, type="II") aov1.av1xyl150.res=subset.150 aov1.av1xyl150.res$M1.Fit = fitted(aov1.av1xyl150) aov1.av1xyl150.res$M1.Resid = resid(aov1.av1xyl150) shapiro.test(aov1.av1xyl150.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.150$Sample, subset.150$PT, subset.150$pteh.X.y) windows(record=TRUE) interaction.plot(subset.150$PT, subset.150$Sample, subset.150$pteh.X.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xyl150.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose 150 PTEH yield", col=as.factor(aov1.av1xyl150.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov1.av1xyl150.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose 150 PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov1.av1xyl150.res$M1.Resid), sd=sd(aov1.av1xyl150.res$M1.Resid)), add=TRUE) qqnorm(aov1.av1xyl150.res$M1.Resid, main="Q-Q Plot - Xylose 150 PTEH yield", cex.main=0.7) qqline(aov1.av1xyl150.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1xyl150.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose 150 PTEH yield", col=as.factor(aov1.av1xyl150.res$ID), pch=20, cex.main=0.7) lm103<-aov(pteh.X.y ~ as.factor(Sample) + PT + Batch , data=subset.150) TukeyHSD(aov(lm103)) #160 aov1.av1xyl160 <- lm(pteh.X.y ~ Sample + PT + Batch + PT*Sample , data=subset.160) summary(aov1.av1xyl160) Anova(aov1.av1xyl160, type="II") aov1.av1xyl160.res=subset.160 aov1.av1xyl160.res$M1.Fit = fitted(aov1.av1xyl160) aov1.av1xyl160.res$M1.Resid = resid(aov1.av1xyl160) shapiro.test(aov1.av1xyl160.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.160$Sample, subset.160$PT, subset.160$pteh.X.y) windows(record=TRUE) interaction.plot(subset.160$PT, subset.160$Sample, subset.160$pteh.X.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xyl160.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose 160 pteh yield", col=as.factor(aov1.av1xyl160.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov1.av1xyl160.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose 160 pteh yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov1.av1xyl160.res$M1.Resid), sd=sd(aov1.av1xyl160.res$M1.Resid)), add=TRUE) qqnorm(aov1.av1xyl160.res$M1.Resid, main="Q-Q Plot - Xylose 160 pteh yield", cex.main=0.7) qqline(aov1.av1xyl160.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1xyl160.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose 160 pteh yield", col=as.factor(aov1.av1xyl160.res$ID), pch=20, cex.main=0.7) lm104<-aov(pteh.X.y ~ as.factor(Sample) + PT + Batch , data=subset.160) TukeyHSD(aov(lm104)) # # Glucose+Xylose #150 aov1.av1glxy150 <- lm(pteh.GX.y ~ Sample + PT + Batch + PT*Sample , data=subset.150) summary(aov1.av1glxy150) Anova(aov1.av1glxy150, type="II") aov1.av1glxy150.res=subset.150 aov1.av1glxy150.res$M1.Fit = fitted(aov1.av1glxy150) aov1.av1glxy150.res$M1.Resid = resid(aov1.av1glxy150) shapiro.test(aov1.av1glxy150.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.150$Sample, subset.150$PT, subset.150$pteh.GX.y) windows(record=TRUE) interaction.plot(subset.150$PT, subset.150$Sample, subset.150$pteh.GX.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxy150.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 150 PTEH yield", col=as.factor(aov1.av1glxy150.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov1.av1glxy150.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose 150 PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov1.av1glxy150.res$M1.Resid), sd=sd(aov1.av1glxy150.res$M1.Resid)), add=TRUE) qqnorm(aov1.av1glxy150.res$M1.Resid, main="Q-Q Plot - Glucose+Xylose 150 PTEH yield", cex.main=0.7) qqline(aov1.av1glxy150.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glxy150.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 150 PTEH yield", col=as.factor(aov1.av1glxy150.res$ID), pch=20, cex.main=0.7) lm105<-aov(pteh.GX.y ~ as.factor(Sample) + PT + Batch , data=subset.150) TukeyHSD(aov(lm105)) #160 aov1.av1glxy160 <- lm(pteh.GX.y ~ Sample + PT + Batch + PT*Sample , data=subset.160) summary(aov1.av1glu160) Anova(aov1.av1glxy160, type="II") aov1.av1glxy160.res=subset.160 aov1.av1glxy160.res$M1.Fit = fitted(aov1.av1glxy160) aov1.av1glxy160.res$M1.Resid = resid(aov1.av1glxy160) shapiro.test(aov1.av1glxy160.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.160$Sample, subset.160$PT, subset.160$pteh.GX.y) windows(record=TRUE) interaction.plot(subset.160$PT, subset.160$Sample, subset.160$pteh.GX.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxy160.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 160 pteh yield", col=as.factor(aov1.av1glxy160.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov1.av1glxy160.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose 160 pteh yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov1.av1glxy160.res$M1.Resid), sd=sd(aov1.av1glxy160.res$M1.Resid)), add=TRUE) qqnorm(aov1.av1glxy160.res$M1.Resid, main="Q-Q Plot - Glucose+Xylose 160 pteh yield", cex.main=0.7) qqline(aov1.av1glxy160.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glxy160.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 160 pteh yield", col=as.factor(aov1.av1glxy160.res$ID), pch=20, cex.main=0.7) lm106<-aov(pteh.GX.y ~ as.factor(Sample) + PT + Batch , data=subset.160) TukeyHSD(aov(lm106)) # # Divide data according to the PT subset.acid <-subset(data.sor.sel, data.sor.sel$PT=="Acid only") subset.deac <-subset(data.sor.sel, data.sor.sel$PT=="Deacetylation and Acid") subset.acid.150 <-subset(subset.acid, subset.acid$Temp=="150") subset.acid.160 <-subset(subset.acid, subset.acid$Temp=="160") subset.deac.150 <-subset(subset.deac, subset.deac$Temp=="150") subset.deac.160 <-subset(subset.deac, subset.deac$Temp=="160") # Equality of variance # pteh.G.y leveneTest(subset.acid$pteh.G.y, as.factor(subset.acid$ID), mean) leveneTest(subset.acid$pteh.G.y, as.factor(subset.acid$ID), median) leveneTest(subset.deac$pteh.G.y, as.factor(subset.deac$ID), mean) leveneTest(subset.deac$pteh.G.y, as.factor(subset.deac$ID), median) # pteh.X.y leveneTest(subset.acid$pteh.X.y, as.factor(subset.acid$ID), mean) leveneTest(subset.acid$pteh.X.y, as.factor(subset.acid$ID), median) leveneTest(subset.deac$pteh.X.y, as.factor(subset.deac$ID), mean) leveneTest(subset.deac$pteh.X.y, as.factor(subset.deac$ID), median) # pteh.GX.y leveneTest(subset.acid$pteh.GX.y, as.factor(subset.acid$ID), mean) leveneTest(subset.acid$pteh.GX.y, as.factor(subset.acid$ID), median) leveneTest(subset.deac$pteh.GX.y, as.factor(subset.deac$ID), mean) leveneTest(subset.deac$pteh.GX.y, as.factor(subset.deac$ID), median) # # Glucose #acid aov1.av1gluacid <- aov(pteh.G.y ~ Sample + as.factor(Temp) + Batch + as.factor(Temp)*Sample , data=subset.acid) summary(aov1.av1gluacid) aov1.av1gluacid.res=subset.acid aov1.av1gluacid.res$M1.Fit = fitted(aov1.av1gluacid) aov1.av1gluacid.res$M1.Resid = resid(aov1.av1gluacid) shapiro.test(aov1.av1gluacid.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.acid$Sample, subset.acid$Temp, subset.acid$pteh.G.y) windows(record=TRUE) interaction.plot(subset.acid$Temp, subset.acid$Sample, subset.acid$pteh.G.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1gluacid.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose acid PTEH yield", col=as.factor(aov1.av1gluacid.res$ID), pch=20, cex.main=0.7) hist(aov1.av1gluacid.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose acid PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1gluacid.res$M1.Resid, main="Q-Q Plot - Glucose acid PTEH yield", cex.main=0.7) qqline(aov1.av1gluacid.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1gluacid.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose acid PTEH yield", col=as.factor(aov1.av1gluacid.res$ID), pch=20, cex.main=0.7) lm111<-aov(pteh.G.y ~ as.factor(Sample) + as.factor(Temp) + Batch , data=subset.acid) TukeyHSD(aov(lm111)) #deac aov1.av1gludeac <- aov(pteh.G.y ~ Sample + as.factor(Temp) + Batch + as.factor(Temp)*Sample , data=subset.deac) summary(aov1.av1gludeac) aov1.av1gludeac.res=subset.deac aov1.av1gludeac.res$M1.Fit = fitted(aov1.av1gludeac) aov1.av1gludeac.res$M1.Resid = resid(aov1.av1gludeac) shapiro.test(aov1.av1gludeac.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.deac$Sample, subset.deac$Temp, subset.deac$pteh.G.y) windows(record=TRUE) interaction.plot(subset.deac$Temp, subset.deac$Sample, subset.deac$pteh.G.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1gludeac.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose deac pteh yield", col=as.factor(aov1.av1gludeac.res$ID), pch=20, cex.main=0.7) hist(aov1.av1gludeac.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose deac pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1gludeac.res$M1.Resid, main="Q-Q Plot - Glucose deac pteh yield", cex.main=0.7) qqline(aov1.av1gludeac.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1gludeac.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose deac pteh yield", col=as.factor(aov1.av1gludeac.res$ID), pch=20, cex.main=0.7) lm112<-aov(pteh.G.y ~ as.factor(Sample) + as.factor(Temp) + Batch , data=subset.deac) TukeyHSD(aov(lm112)) # # Xylose #acid aov1.av1xylacid <- aov(pteh.X.y ~ Sample + as.factor(Temp) + Batch + as.factor(Temp)*Sample , data=subset.acid) summary(aov1.av1xylacid) aov1.av1xylacid.res=subset.acid aov1.av1xylacid.res$M1.Fit = fitted(aov1.av1xylacid) aov1.av1xylacid.res$M1.Resid = resid(aov1.av1xylacid) shapiro.test(aov1.av1xylacid.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.acid$Sample, subset.acid$Temp, subset.acid$pteh.X.y) windows(record=TRUE) interaction.plot(subset.acid$Temp, subset.acid$Sample, subset.acid$pteh.X.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xylacid.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose acid PTEH yield", col=as.factor(aov1.av1xylacid.res$ID), pch=20, cex.main=0.7) hist(aov1.av1xylacid.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose acid PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1xylacid.res$M1.Resid, main="Q-Q Plot - Xylose acid PTEH yield", cex.main=0.7) qqline(aov1.av1xylacid.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1xylacid.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose acid PTEH yield", col=as.factor(aov1.av1xylacid.res$ID), pch=20, cex.main=0.7) lm113<-aov(pteh.X.y ~ as.factor(Sample) + as.factor(Temp) + Batch , data=subset.acid) TukeyHSD(aov(lm113)) #deac aov1.av1xyldeac <- aov(pteh.X.y ~ Sample + as.factor(Temp) + Batch + as.factor(Temp)*Sample , data=subset.deac) summary(aov1.av1xyldeac) aov1.av1xyldeac.res=subset.deac aov1.av1xyldeac.res$M1.Fit = fitted(aov1.av1xyldeac) aov1.av1xyldeac.res$M1.Resid = resid(aov1.av1xyldeac) shapiro.test(aov1.av1xyldeac.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.deac$Sample, subset.deac$Temp, subset.deac$pteh.X.y) windows(record=TRUE) interaction.plot(subset.deac$Temp, subset.deac$Sample, subset.deac$pteh.X.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xyldeac.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose deac pteh yield", col=as.factor(aov1.av1xyldeac.res$ID), pch=20, cex.main=0.7) hist(aov1.av1xyldeac.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose deac pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1xyldeac.res$M1.Resid, main="Q-Q Plot - Xylose deac pteh yield", cex.main=0.7) qqline(aov1.av1xyldeac.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1xyldeac.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose deac pteh yield", col=as.factor(aov1.av1xyldeac.res$ID), pch=20, cex.main=0.7) lm114<-aov(pteh.X.y ~ as.factor(Sample) + as.factor(Temp) + Batch , data=subset.deac) TukeyHSD(aov(lm114)) # # Glucose+Xylose #acid aov1.av1glxyacid <- aov(pteh.GX.y ~ Sample + as.factor(Temp) + Batch + as.factor(Temp)*Sample , data=subset.acid) summary(aov1.av1glxyacid) aov1.av1glxyacid.res=subset.acid aov1.av1glxyacid.res$M1.Fit = fitted(aov1.av1glxyacid) aov1.av1glxyacid.res$M1.Resid = resid(aov1.av1glxyacid) shapiro.test(aov1.av1glxyacid.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.acid$Sample, subset.acid$Temp, subset.acid$pteh.GX.y) windows(record=TRUE) interaction.plot(subset.acid$Temp, subset.acid$Sample, subset.acid$pteh.GX.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxyacid.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose acid PTEH yield", col=as.factor(aov1.av1glxyacid.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glxyacid.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose acid PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glxyacid.res$M1.Resid, main="Q-Q Plot - Glucose+Xylose acid PTEH yield", cex.main=0.7) qqline(aov1.av1glxyacid.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glxyacid.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose acid PTEH yield", col=as.factor(aov1.av1glxyacid.res$ID), pch=20, cex.main=0.7) lm115<-aov(pteh.GX.y ~ as.factor(Sample) + as.factor(Temp) + Batch , data=subset.acid) TukeyHSD(aov(lm115)) #deac aov1.av1glxydeac <- aov(pteh.GX.y ~ Sample + as.factor(Temp) + Batch + as.factor(Temp)*Sample , data=subset.deac) summary(aov1.av1glxydeac) aov1.av1glxydeac.res=subset.deac aov1.av1glxydeac.res$M1.Fit = fitted(aov1.av1glxydeac) aov1.av1glxydeac.res$M1.Resid = resid(aov1.av1glxydeac) shapiro.test(aov1.av1glxydeac.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.deac$Sample, subset.deac$Temp, subset.deac$pteh.GX.y) windows(record=TRUE) interaction.plot(subset.deac$Temp, subset.deac$Sample, subset.deac$pteh.GX.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxydeac.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose deac pteh yield", col=as.factor(aov1.av1glxydeac.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glxydeac.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose deacpteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glxydeac.res$M1.Resid, main="Q-Q Plot - Glucose+Xylose deacpteh yield", cex.main=0.7) qqline(aov1.av1glxydeac.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glxydeac.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose deacpteh yield", col=as.factor(aov1.av1glxydeac.res$ID), pch=20, cex.main=0.7) lm116<-aov(pteh.GX.y ~ as.factor(Sample) + as.factor(Temp) + Batch , data=subset.deac) TukeyHSD(aov(lm116)) # # Divide data according to the Sample subset.wild <-subset(data.sor.sel, data.sor.sel$Sample=="Sorghum wild") subset.stac <-subset(data.sor.sel, data.sor.sel$Sample=="Sorghum Stacked") # Equality of variance # pteh.G.y leveneTest(subset.wild$pteh.G.y, as.factor(subset.wild$ID), mean) leveneTest(subset.wild$pteh.G.y, as.factor(subset.wild$ID), median) leveneTest(subset.stac$pteh.G.y, as.factor(subset.stac$ID), mean) leveneTest(subset.stac$pteh.G.y, as.factor(subset.stac$ID), median) # pteh.X.y leveneTest(subset.wild$pteh.X.y, as.factor(subset.wild$ID), mean) leveneTest(subset.wild$pteh.X.y, as.factor(subset.wild$ID), median) leveneTest(subset.stac$pteh.X.y, as.factor(subset.stac$ID), mean) leveneTest(subset.stac$pteh.X.y, as.factor(subset.stac$ID), median) # pteh.GX.y leveneTest(subset.wild$pteh.GX.y, as.factor(subset.wild$ID), mean) leveneTest(subset.wild$pteh.GX.y, as.factor(subset.wild$ID), median) leveneTest(subset.stac$pteh.GX.y, as.factor(subset.stac$ID), mean) leveneTest(subset.stac$pteh.GX.y, as.factor(subset.stac$ID), median) # # Glucose #wild aov1.av1gluwild <- aov(pteh.G.y ~ as.factor(Temp) + PT + Batch + PT*as.factor(Temp) , data=subset.wild) summary(aov1.av1gluwild) aov1.av1gluwild.res=subset.wild aov1.av1gluwild.res$M1.Fit = fitted(aov1.av1gluwild) aov1.av1gluwild.res$M1.Resid = resid(aov1.av1gluwild) shapiro.test(aov1.av1gluwild.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.wild$Temp, subset.wild$PT, subset.wild$pteh.G.y) windows(record=TRUE) interaction.plot(subset.wild$PT, subset.wild$Temp, subset.wild$pteh.G.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1gluwild.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose wild PTEH yield", col=as.factor(aov1.av1gluwild.res$ID), pch=20, cex.main=0.7) hist(aov1.av1gluwild.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose wild PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1gluwild.res$M1.Resid, main="Q-Q Plot - Glucose wild PTEH yield", cex.main=0.7) qqline(aov1.av1gluwild.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1gluwild.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose wild PTEH yield", col=as.factor(aov1.av1gluwild.res$ID), pch=20, cex.main=0.7) lm121<-aov(pteh.G.y ~ as.factor(Temp) + PT + Batch , data=subset.wild) TukeyHSD(aov(lm121)) #stac aov1.av1glustac <- aov(pteh.G.y ~ as.factor(Temp) + PT + Batch + PT*as.factor(Temp) , data=subset.stac) summary(aov1.av1glustac) aov1.av1glustac.res=subset.stac aov1.av1glustac.res$M1.Fit = fitted(aov1.av1glustac) aov1.av1glustac.res$M1.Resid = resid(aov1.av1glustac) shapiro.test(aov1.av1glustac.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.stac$Temp, subset.stac$PT, subset.stac$pteh.G.y) windows(record=TRUE) interaction.plot(subset.stac$PT, subset.stac$Temp, subset.stac$pteh.G.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glustac.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose stac pteh yield", col=as.factor(aov1.av1glustac.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glustac.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose stac pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glustac.res$M1.Resid, main="Q-Q Plot - Glucose stac pteh yield", cex.main=0.7) qqline(aov1.av1glustac.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glustac.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose stac pteh yield", col=as.factor(aov1.av1glustac.res$ID), pch=20, cex.main=0.7) lm122<-aov(pteh.G.y ~ as.factor(Temp) + PT + Batch , data=subset.stac) TukeyHSD(aov(lm122)) # # Xylose #wild aov1.av1xylwild <- aov(pteh.X.y ~ as.factor(Temp) + PT + Batch + PT*as.factor(Temp) , data=subset.wild) summary(aov1.av1xylwild) aov1.av1xylwild.res=subset.wild aov1.av1xylwild.res$M1.Fit = fitted(aov1.av1xylwild) aov1.av1xylwild.res$M1.Resid = resid(aov1.av1xylwild) shapiro.test(aov1.av1xylwild.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.wild$Temp, subset.wild$PT, subset.wild$pteh.X.y) windows(record=TRUE) interaction.plot(subset.wild$PT, subset.wild$Temp, subset.wild$pteh.X.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xylwild.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose wild PTEH yield", col=as.factor(aov1.av1xylwild.res$ID), pch=20, cex.main=0.7) hist(aov1.av1xylwild.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose wild PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1xylwild.res$M1.Resid, main="Q-Q Plot - Xylose wild PTEH yield", cex.main=0.7) qqline(aov1.av1xylwild.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1xylwild.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose wild PTEH yield", col=as.factor(aov1.av1xylwild.res$ID), pch=20, cex.main=0.7) lm123<-aov(pteh.X.y ~ as.factor(Temp) + PT + Batch , data=subset.wild) TukeyHSD(aov(lm123)) #stac aov1.av1xylstac <- aov(pteh.X.y ~ Temp + PT + Batch + PT*Temp , data=subset.stac) summary(aov1.av1xylstac) aov1.av1xylstac.res=subset.stac aov1.av1xylstac.res$M1.Fit = fitted(aov1.av1xylstac) aov1.av1xylstac.res$M1.Resid = resid(aov1.av1xylstac) shapiro.test(aov1.av1xylstac.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.stac$Temp, subset.stac$PT, subset.stac$pteh.X.y) windows(record=TRUE) interaction.plot(subset.stac$PT, subset.stac$Temp, subset.stac$pteh.X.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xylstac.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose stac pteh yield", col=as.factor(aov1.av1xylstac.res$ID), pch=20, cex.main=0.7) hist(aov1.av1xylstac.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose stac pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1xylstac.res$M1.Resid, main="Q-Q Plot - Xylose stac pteh yield", cex.main=0.7) qqline(aov1.av1xylstac.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1xylstac.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose stac pteh yield", col=as.factor(aov1.av1xylstac.res$ID), pch=20, cex.main=0.7) lm124<-aov(pteh.X.y ~ as.factor(Temp) + PT + Batch , data=subset.stac) TukeyHSD(aov(lm124)) # # Glucose+Xylose #wild aov1.av1glxywild <- aov(pteh.GX.y ~ Temp + PT + Batch + PT*Temp , data=subset.wild) summary(aov1.av1glxywild) aov1.av1glxywild.res=subset.wild aov1.av1glxywild.res$M1.Fit = fitted(aov1.av1glxywild) aov1.av1glxywild.res$M1.Resid = resid(aov1.av1glxywild) shapiro.test(aov1.av1glxywild.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.wild$Temp, subset.wild$Temp, subset.wild$pteh.GX.y) windows(record=TRUE) interaction.plot(subset.wild$Temp, subset.wild$Temp, subset.wild$pteh.GX.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxywild.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose wild PTEH yield", col=as.factor(aov1.av1glxywild.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glxywild.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose wild PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glxywild.res$M1.Resid, main="Q-Q Plot - Glucose+Xylose wild PTEH yield", cex.main=0.7) qqline(aov1.av1glxywild.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glxywild.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose wild PTEH yield", col=as.factor(aov1.av1glxywild.res$ID), pch=20, cex.main=0.7) lm125<-aov(pteh.GX.y ~ as.factor(Temp) + PT + Batch , data=subset.wild) TukeyHSD(aov(lm125)) #stac aov1.av1glxystac <- aov(pteh.GX.y ~ Temp + PT + Batch + PT*Temp , data=subset.stac) summary(aov1.av1glxystac) aov1.av1glxystac.res=subset.stac aov1.av1glxystac.res$M1.Fit = fitted(aov1.av1glxystac) aov1.av1glxystac.res$M1.Resid = resid(aov1.av1glxystac) shapiro.test(aov1.av1glxystac.res$M1.Resid) windows(record=TRUE) interaction.plot(subset.stac$Temp, subset.stac$PT, subset.stac$pteh.GX.y) windows(record=TRUE) interaction.plot(subset.stac$PT, subset.stac$Temp, subset.stac$pteh.GX.y) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxystac.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose stac pteh yield", col=as.factor(aov1.av1glxystac.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glxystac.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose stac pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glxystac.res$M1.Resid, main="Q-Q Plot - Glucose+Xylose stacpteh yield", cex.main=0.7) qqline(aov1.av1glxystac.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glxystac.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose stacpteh yield", col=as.factor(aov1.av1glxystac.res$ID), pch=20, cex.main=0.7) lm126<-aov(pteh.GX.y ~ as.factor(Temp) + PT + Batch , data=subset.stac) TukeyHSD(aov(lm126)) # # Divide data according to the PT and Temp subset.acid.150 <-subset(subset.acid, subset.acid$Temp=="150") subset.acid.160 <-subset(subset.acid, subset.acid$Temp=="160") subset.deac.150 <-subset(subset.deac, subset.deac$Temp=="150") subset.deac.160 <-subset(subset.deac, subset.deac$Temp=="160") # Equality of variance # pteh.G.y leveneTest(subset.acid.150$pteh.G.y, as.factor(subset.acid.150$ID), mean) leveneTest(subset.acid.150$pteh.G.y, as.factor(subset.acid.150$ID), median) leveneTest(subset.acid.160$pteh.G.y, as.factor(subset.acid.160$ID), mean) leveneTest(subset.acid.160$pteh.G.y, as.factor(subset.acid.160$ID), median) leveneTest(subset.deac.150$pteh.G.y, as.factor(subset.deac.150$ID), mean) leveneTest(subset.deac.150$pteh.G.y, as.factor(subset.deac.150$ID), median) leveneTest(subset.deac.160$pteh.G.y, as.factor(subset.deac.160$ID), mean) leveneTest(subset.deac.160$pteh.G.y, as.factor(subset.deac.160$ID), median) # pteh.X.y leveneTest(subset.acid.150$pteh.X.y, as.factor(subset.acid.150$ID), mean) leveneTest(subset.acid.150$pteh.X.y, as.factor(subset.acid.150$ID), median) leveneTest(subset.acid.160$pteh.X.y, as.factor(subset.acid.160$ID), mean) leveneTest(subset.acid.160$pteh.X.y, as.factor(subset.acid.160$ID), median) leveneTest(subset.deac.150$pteh.X.y, as.factor(subset.deac.150$ID), mean) leveneTest(subset.deac.150$pteh.X.y, as.factor(subset.deac.150$ID), median) leveneTest(subset.deac.160$pteh.X.y, as.factor(subset.deac.160$ID), mean) leveneTest(subset.deac.160$pteh.X.y, as.factor(subset.deac.160$ID), median) # pteh.GX.y leveneTest(subset.acid.150$pteh.GX.y, as.factor(subset.acid.150$ID), mean) leveneTest(subset.acid.150$pteh.GX.y, as.factor(subset.acid.150$ID), median) leveneTest(subset.acid.160$pteh.GX.y, as.factor(subset.acid.160$ID), mean) leveneTest(subset.acid.160$pteh.GX.y, as.factor(subset.acid.160$ID), median) leveneTest(subset.deac.150$pteh.GX.y, as.factor(subset.deac.150$ID), mean) leveneTest(subset.deac.150$pteh.GX.y, as.factor(subset.deac.150$ID), median) leveneTest(subset.deac.160$pteh.GX.y, as.factor(subset.deac.160$ID), mean) leveneTest(subset.deac.160$pteh.GX.y, as.factor(subset.deac.160$ID), median) subset.acid.150 <-subset(subset.acid, subset.acid$Temp=="150") subset.acid.160 <-subset(subset.acid, subset.acid$Temp=="160") subset.deac.150 <-subset(subset.deac, subset.deac$Temp=="150") subset.deac.160 <-subset(subset.deac, subset.deac$Temp=="160") # # Glucose # acid.150 aov1.av1gluacid.150 <- aov(pteh.G.y ~ Sample + Batch , data=subset.acid.150) summary(aov1.av1gluacid.150) aov1.av1gluacid.150.res=subset.wild aov1.av1gluacid.150.res$M1.Fit = fitted(aov1.av1gluacid.150) aov1.av1gluacid.150.res$M1.Resid = resid(aov1.av1gluacid.150) shapiro.test(aov1.av1gluacid.150.res$M1.Resid) # acid.160 aov1.av1gluacid.160 <- aov(pteh.G.y ~ Sample + Batch , data=subset.acid.160) summary(aov1.av1gluacid.160) aov1.av1gluacid.160.res=subset.wild aov1.av1gluacid.160.res$M1.Fit = fitted(aov1.av1gluacid.160) aov1.av1gluacid.160.res$M1.Resid = resid(aov1.av1gluacid.160) shapiro.test(aov1.av1gluacid.160.res$M1.Resid) # deac.150 aov1.av1gludeac.150 <- aov(pteh.G.y ~ Sample + Batch , data=subset.deac.150) summary(aov1.av1gludeac.150) aov1.av1gludeac.150.res=subset.wild aov1.av1gludeac.150.res$M1.Fit = fitted(aov1.av1gludeac.150) aov1.av1gludeac.150.res$M1.Resid = resid(aov1.av1gludeac.150) shapiro.test(aov1.av1gludeac.150.res$M1.Resid) # deac.160 aov1.av1gludeac.160 <- aov(pteh.G.y ~ Sample + Batch , data=subset.deac.160) summary(aov1.av1gludeac.160) aov1.av1gludeac.160.res=subset.wild aov1.av1gludeac.160.res$M1.Fit = fitted(aov1.av1gludeac.160) aov1.av1gludeac.160.res$M1.Resid = resid(aov1.av1gludeac.160) shapiro.test(aov1.av1gludeac.160.res$M1.Resid) # # Xylose # acid.150 aov1.av1xylacid.150 <- aov(pteh.X.y ~ Sample + Batch , data=subset.acid.150) summary(aov1.av1xylacid.150) aov1.av1xylacid.150.res=subset.wild aov1.av1xylacid.150.res$M1.Fit = fitted(aov1.av1xylacid.150) aov1.av1xylacid.150.res$M1.Resid = resid(aov1.av1xylacid.150) shapiro.test(aov1.av1xylacid.150.res$M1.Resid) # acid.160 aov1.av1xylacid.160 <- aov(pteh.X.y ~ Sample + Batch , data=subset.acid.160) summary(aov1.av1xylacid.160) aov1.av1xylacid.160.res=subset.wild aov1.av1xylacid.160.res$M1.Fit = fitted(aov1.av1xylacid.160) aov1.av1xylacid.160.res$M1.Resid = resid(aov1.av1xylacid.160) shapiro.test(aov1.av1xylacid.160.res$M1.Resid) # deac.150 aov1.av1xyldeac.150 <- aov(pteh.X.y ~ Sample + Batch , data=subset.deac.150) summary(aov1.av1xyldeac.150) aov1.av1xyldeac.150.res=subset.wild aov1.av1xyldeac.150.res$M1.Fit = fitted(aov1.av1xyldeac.150) aov1.av1xyldeac.150.res$M1.Resid = resid(aov1.av1xyldeac.150) shapiro.test(aov1.av1xyldeac.150.res$M1.Resid) # deac.160 aov1.av1xyldeac.160 <- aov(pteh.X.y ~ Sample + Batch , data=subset.deac.160) summary(aov1.av1xyldeac.160) aov1.av1xyldeac.160.res=subset.wild aov1.av1xyldeac.160.res$M1.Fit = fitted(aov1.av1xyldeac.160) aov1.av1xyldeac.160.res$M1.Resid = resid(aov1.av1xyldeac.160) shapiro.test(aov1.av1xyldeac.160.res$M1.Resid) # # Glucose+Xylose # acid.150 aov1.av1glxyacid.150 <- aov(pteh.GX.y ~ Sample + Batch , data=subset.acid.150) summary(aov1.av1glxyacid.150) aov1.av1glxyacid.150.res=subset.wild aov1.av1glxyacid.150.res$M1.Fit = fitted(aov1.av1glxyacid.150) aov1.av1glxyacid.150.res$M1.Resid = resid(aov1.av1glxyacid.150) shapiro.test(aov1.av1glxyacid.150.res$M1.Resid) # acid.160 aov1.av1glxyacid.160 <- aov(pteh.GX.y ~ Sample + Batch , data=subset.acid.160) summary(aov1.av1glxyacid.160) aov1.av1glxyacid.160.res=subset.wild aov1.av1glxyacid.160.res$M1.Fit = fitted(aov1.av1glxyacid.160) aov1.av1glxyacid.160.res$M1.Resid = resid(aov1.av1glxyacid.160) shapiro.test(aov1.av1glxyacid.160.res$M1.Resid) # deac.150 aov1.av1glxydeac.150 <- aov(pteh.GX.y ~ Sample + Batch , data=subset.deac.150) summary(aov1.av1glxydeac.150) aov1.av1glxydeac.150.res=subset.wild aov1.av1glxydeac.150.res$M1.Fit = fitted(aov1.av1glxydeac.150) aov1.av1glxydeac.150.res$M1.Resid = resid(aov1.av1glxydeac.150) shapiro.test(aov1.av1glxydeac.150.res$M1.Resid) # deac.160 aov1.av1glxydeac.160 <- aov(pteh.GX.y ~ Sample + Batch , data=subset.deac.160) summary(aov1.av1glxydeac.160) aov1.av1glxydeac.160.res=subset.wild aov1.av1glxydeac.160.res$M1.Fit = fitted(aov1.av1glxydeac.160) aov1.av1glxydeac.160.res$M1.Resid = resid(aov1.av1glxydeac.160) shapiro.test(aov1.av1glxydeac.160.res$M1.Resid) # # Divide data according to the Sample and Temperature subset.wild.150 <-subset(subset.wild, subset.wild$Temp=="150") subset.wild.160 <-subset(subset.wild, subset.wild$Temp=="160") subset.stac.150 <-subset(subset.stac, subset.stac$Temp=="150") subset.stac.160 <-subset(subset.stac, subset.stac$Temp=="160") # Equality of variance # pteh.G.y leveneTest(subset.wild.150$pteh.G.y, as.factor(subset.wild.150$ID), mean) leveneTest(subset.wild.150$pteh.G.y, as.factor(subset.wild.150$ID), median) leveneTest(subset.wild.160$pteh.G.y, as.factor(subset.wild.160$ID), mean) leveneTest(subset.wild.160$pteh.G.y, as.factor(subset.wild.160$ID), median) leveneTest(subset.stac.150$pteh.G.y, as.factor(subset.stac.150$ID), mean) leveneTest(subset.stac.150$pteh.G.y, as.factor(subset.stac.150$ID), median) leveneTest(subset.stac.160$pteh.G.y, as.factor(subset.stac.160$ID), mean) leveneTest(subset.stac.160$pteh.G.y, as.factor(subset.stac.160$ID), median) # pteh.X.y leveneTest(subset.wild.150$pteh.X.y, as.factor(subset.wild.150$ID), mean) leveneTest(subset.wild.150$pteh.X.y, as.factor(subset.wild.150$ID), median) leveneTest(subset.wild.160$pteh.X.y, as.factor(subset.wild.160$ID), mean) leveneTest(subset.wild.160$pteh.X.y, as.factor(subset.wild.160$ID), median) leveneTest(subset.stac.150$pteh.X.y, as.factor(subset.stac.150$ID), mean) leveneTest(subset.stac.150$pteh.X.y, as.factor(subset.stac.150$ID), median) leveneTest(subset.stac.160$pteh.X.y, as.factor(subset.stac.160$ID), mean) leveneTest(subset.stac.160$pteh.X.y, as.factor(subset.stac.160$ID), median) # pteh.GX.y leveneTest(subset.wild.150$pteh.GX.y, as.factor(subset.wild.150$ID), mean) leveneTest(subset.wild.150$pteh.GX.y, as.factor(subset.wild.150$ID), median) leveneTest(subset.wild.160$pteh.GX.y, as.factor(subset.wild.160$ID), mean) leveneTest(subset.wild.160$pteh.GX.y, as.factor(subset.wild.160$ID), median) leveneTest(subset.stac.150$pteh.GX.y, as.factor(subset.stac.150$ID), mean) leveneTest(subset.stac.150$pteh.GX.y, as.factor(subset.stac.150$ID), median) leveneTest(subset.stac.160$pteh.GX.y, as.factor(subset.stac.160$ID), mean) leveneTest(subset.stac.160$pteh.GX.y, as.factor(subset.stac.160$ID), median) # # Glucose #wild.150 aov1.av1gluwild.150 <- aov(pteh.G.y ~ PT + Batch , data=subset.wild.150 ) summary(aov1.av1gluwild.150) aov1.av1gluwild.150.res=subset.wild aov1.av1gluwild.150.res$M1.Fit = fitted(aov1.av1gluwild.150) aov1.av1gluwild.150.res$M1.Resid = resid(aov1.av1gluwild.150) shapiro.test(aov1.av1gluwild.150.res$M1.Resid) #wild.160 aov1.av1gluwild.160 <- aov(pteh.G.y ~ PT + Batch , data=subset.wild.160 ) summary(aov1.av1gluwild.160) aov1.av1gluwild.160.res=subset.wild aov1.av1gluwild.160.res$M1.Fit = fitted(aov1.av1gluwild.160) aov1.av1gluwild.160.res$M1.Resid = resid(aov1.av1gluwild.160) shapiro.test(aov1.av1gluwild.160.res$M1.Resid) #stac.150 aov1.av1glustac.150 <- aov(pteh.G.y ~ PT + Batch , data=subset.stac.150 ) summary(aov1.av1glustac.150) aov1.av1glustac.150.res=subset.stac aov1.av1glustac.150.res$M1.Fit = fitted(aov1.av1glustac.150) aov1.av1glustac.150.res$M1.Resid = resid(aov1.av1glustac.150) shapiro.test(aov1.av1glustac.150.res$M1.Resid) #stac.160 aov1.av1glustac.160 <- aov(pteh.G.y ~ PT + Batch , data=subset.stac.160 ) summary(aov1.av1glustac.160) aov1.av1glustac.160.res=subset.stac aov1.av1glustac.160.res$M1.Fit = fitted(aov1.av1glustac.160) aov1.av1glustac.160.res$M1.Resid = resid(aov1.av1glustac.160) shapiro.test(aov1.av1glustac.160.res$M1.Resid) # # Xylose #wild.150 aov1.av1gluwild.150 <- aov(pteh.X.y ~ PT + Batch , data=subset.wild.150 ) summary(aov1.av1gluwild.150) aov1.av1gluwild.150.res=subset.wild aov1.av1gluwild.150.res$M1.Fit = fitted(aov1.av1gluwild.150) aov1.av1gluwild.150.res$M1.Resid = resid(aov1.av1gluwild.150) shapiro.test(aov1.av1gluwild.150.res$M1.Resid) #wild.160 aov1.av1gluwild.160 <- aov(pteh.X.y ~ PT + Batch , data=subset.wild.160 ) summary(aov1.av1gluwild.160) aov1.av1gluwild.160.res=subset.wild aov1.av1gluwild.160.res$M1.Fit = fitted(aov1.av1gluwild.160) aov1.av1gluwild.160.res$M1.Resid = resid(aov1.av1gluwild.160) shapiro.test(aov1.av1gluwild.160.res$M1.Resid) #stac.150 aov1.av1glustac.150 <- aov(pteh.X.y ~ PT + Batch , data=subset.stac.150 ) summary(aov1.av1glustac.150) aov1.av1glustac.150.res=subset.stac aov1.av1glustac.150.res$M1.Fit = fitted(aov1.av1glustac.150) aov1.av1glustac.150.res$M1.Resid = resid(aov1.av1glustac.150) shapiro.test(aov1.av1glustac.150.res$M1.Resid) #stac.160 aov1.av1glustac.160 <- aov(pteh.X.y ~ PT + Batch , data=subset.stac.160 ) summary(aov1.av1glustac.160) aov1.av1glustac.160.res=subset.stac aov1.av1glustac.160.res$M1.Fit = fitted(aov1.av1glustac.160) aov1.av1glustac.160.res$M1.Resid = resid(aov1.av1glustac.160) shapiro.test(aov1.av1glustac.160.res$M1.Resid) # # Glucose+Xylose #wild.150 aov1.av1gluwild.150 <- aov(pteh.GX.y ~ PT + Batch , data=subset.wild.150 ) summary(aov1.av1gluwild.150) aov1.av1gluwild.150.res=subset.wild aov1.av1gluwild.150.res$M1.Fit = fitted(aov1.av1gluwild.150) aov1.av1gluwild.150.res$M1.Resid = resid(aov1.av1gluwild.150) shapiro.test(aov1.av1gluwild.150.res$M1.Resid) #wild.160 aov1.av1gluwild.160 <- aov(pteh.GX.y ~ PT + Batch , data=subset.wild.160 ) summary(aov1.av1gluwild.160) aov1.av1gluwild.160.res=subset.wild aov1.av1gluwild.160.res$M1.Fit = fitted(aov1.av1gluwild.160) aov1.av1gluwild.160.res$M1.Resid = resid(aov1.av1gluwild.160) shapiro.test(aov1.av1gluwild.160.res$M1.Resid) #stac.150 aov1.av1glustac.150 <- aov(pteh.GX.y ~ PT + Batch , data=subset.stac.150 ) summary(aov1.av1glustac.150) aov1.av1glustac.150.res=subset.stac aov1.av1glustac.150.res$M1.Fit = fitted(aov1.av1glustac.150) aov1.av1glustac.150.res$M1.Resid = resid(aov1.av1glustac.150) shapiro.test(aov1.av1glustac.150.res$M1.Resid) #stac.160 aov1.av1glustac.160 <- aov(pteh.GX.y ~ PT + Batch , data=subset.stac.160 ) summary(aov1.av1glustac.160) aov1.av1glustac.160.res=subset.stac aov1.av1glustac.160.res$M1.Fit = fitted(aov1.av1glustac.160) aov1.av1glustac.160.res$M1.Resid = resid(aov1.av1glustac.160) shapiro.test(aov1.av1glustac.160.res$M1.Resid) # # Divide data according to the Sample and PT subset.wild.acid <-subset(subset.wild, subset.wild$PT=="Acid only") subset.wild.deac <-subset(subset.wild, subset.wild$PT=="Deacetylation and Acid") subset.stac.acid <-subset(subset.stac, subset.stac$PT=="Acid only") subset.stac.deac <-subset(subset.stac, subset.stac$PT=="Deacetylation and Acid") # Equality of variance # pteh.G.y leveneTest(subset.wild.acid$pteh.G.y, as.factor(subset.wild.acid$ID), mean) leveneTest(subset.wild.acid$pteh.G.y, as.factor(subset.wild.acid$ID), median) leveneTest(subset.wild.deac$pteh.G.y, as.factor(subset.wild.deac$ID), mean) leveneTest(subset.wild.deac$pteh.G.y, as.factor(subset.wild.deac$ID), median) leveneTest(subset.stac.acid$pteh.G.y, as.factor(subset.stac.acid$ID), mean) leveneTest(subset.stac.acid$pteh.G.y, as.factor(subset.stac.acid$ID), median) leveneTest(subset.stac.deac$pteh.G.y, as.factor(subset.stac.deac$ID), mean) leveneTest(subset.stac.deac$pteh.G.y, as.factor(subset.stac.deac$ID), median) # pteh.X.y leveneTest(subset.wild.acid$pteh.X.y, as.factor(subset.wild.acid$ID), mean) leveneTest(subset.wild.acid$pteh.X.y, as.factor(subset.wild.acid$ID), median) leveneTest(subset.wild.deac$pteh.X.y, as.factor(subset.wild.deac$ID), mean) leveneTest(subset.wild.deac$pteh.X.y, as.factor(subset.wild.deac$ID), median) leveneTest(subset.stac.acid$pteh.X.y, as.factor(subset.stac.acid$ID), mean) leveneTest(subset.stac.acid$pteh.X.y, as.factor(subset.stac.acid$ID), median) leveneTest(subset.stac.deac$pteh.X.y, as.factor(subset.stac.deac$ID), mean) leveneTest(subset.stac.deac$pteh.X.y, as.factor(subset.stac.deac$ID), median) # pteh.GX.y leveneTest(subset.wild.acid$pteh.GX.y, as.factor(subset.wild.acid$ID), mean) leveneTest(subset.wild.acid$pteh.GX.y, as.factor(subset.wild.acid$ID), median) leveneTest(subset.wild.deac$pteh.GX.y, as.factor(subset.wild.deac$ID), mean) leveneTest(subset.wild.deac$pteh.GX.y, as.factor(subset.wild.deac$ID), median) leveneTest(subset.stac.acid$pteh.GX.y, as.factor(subset.stac.acid$ID), mean) leveneTest(subset.stac.acid$pteh.GX.y, as.factor(subset.stac.acid$ID), median) leveneTest(subset.stac.deac$pteh.GX.y, as.factor(subset.stac.deac$ID), mean) leveneTest(subset.stac.deac$pteh.GX.y, as.factor(subset.stac.deac$ID), median) # # Glucose #wild.acid aov1.av1gluwild.acid <- aov(pteh.G.y ~ Temp + Batch , data=subset.wild.acid ) summary(aov1.av1gluwild.acid) aov1.av1gluwild.acid.res=subset.wild aov1.av1gluwild.acid.res$M1.Fit = fitted(aov1.av1gluwild.acid) aov1.av1gluwild.acid.res$M1.Resid = resid(aov1.av1gluwild.acid) shapiro.test(aov1.av1gluwild.acid.res$M1.Resid) #wild.deac aov1.av1gluwild.deac <- aov(pteh.G.y ~ Temp + Batch , data=subset.wild.deac ) summary(aov1.av1gluwild.deac ) aov1.av1gluwild.deac.res=subset.wild aov1.av1gluwild.deac.res$M1.Fit = fitted(aov1.av1gluwild.deac ) aov1.av1gluwild.deac.res$M1.Resid = resid(aov1.av1gluwild.deac ) shapiro.test(aov1.av1gluwild.deac.res$M1.Resid) #stac.acid aov1.av1glustac.acid <- aov(pteh.G.y ~ Temp + Batch , data=subset.stac.acid ) summary(aov1.av1glustac.acid) aov1.av1glustac.acid.res=subset.stac aov1.av1glustac.acid.res$M1.Fit = fitted(aov1.av1glustac.acid) aov1.av1glustac.acid.res$M1.Resid = resid(aov1.av1glustac.acid) shapiro.test(aov1.av1glustac.acid.res$M1.Resid) #stac.deac aov1.av1glustac.deac <- aov(pteh.G.y ~ Temp + Batch , data=subset.stac.deac ) summary(aov1.av1glustac.deac ) aov1.av1glustac.deac.res=subset.stac aov1.av1glustac.deac.res$M1.Fit = fitted(aov1.av1glustac.deac ) aov1.av1glustac.deac.res$M1.Resid = resid(aov1.av1glustac.deac ) shapiro.test(aov1.av1glustac.deac.res$M1.Resid) # # Xylose #wild.acid aov1.av1gluwild.acid <- aov(pteh.X.y ~ Temp + Batch , data=subset.wild.acid ) summary(aov1.av1gluwild.acid) aov1.av1gluwild.acid.res=subset.wild aov1.av1gluwild.acid.res$M1.Fit = fitted(aov1.av1gluwild.acid) aov1.av1gluwild.acid.res$M1.Resid = resid(aov1.av1gluwild.acid) shapiro.test(aov1.av1gluwild.acid.res$M1.Resid) #wild.deac aov1.av1gluwild.deac <- aov(pteh.X.y ~ Temp + Batch , data=subset.wild.deac ) summary(aov1.av1gluwild.deac ) aov1.av1gluwild.deac.res=subset.wild aov1.av1gluwild.deac.res$M1.Fit = fitted(aov1.av1gluwild.deac ) aov1.av1gluwild.deac.res$M1.Resid = resid(aov1.av1gluwild.deac ) shapiro.test(aov1.av1gluwild.deac.res$M1.Resid) #stac.acid aov1.av1glustac.acid <- aov(pteh.X.y ~ Temp + Batch , data=subset.stac.acid ) summary(aov1.av1glustac.acid) aov1.av1glustac.acid.res=subset.stac aov1.av1glustac.acid.res$M1.Fit = fitted(aov1.av1glustac.acid) aov1.av1glustac.acid.res$M1.Resid = resid(aov1.av1glustac.acid) shapiro.test(aov1.av1glustac.acid.res$M1.Resid) #stac.deac aov1.av1glustac.deac <- aov(pteh.X.y ~ Temp + Batch , data=subset.stac.deac ) summary(aov1.av1glustac.deac ) aov1.av1glustac.deac.res=subset.stac aov1.av1glustac.deac.res$M1.Fit = fitted(aov1.av1glustac.deac ) aov1.av1glustac.deac.res$M1.Resid = resid(aov1.av1glustac.deac ) shapiro.test(aov1.av1glustac.deac.res$M1.Resid) # # Glucose+Xylose #wild.acid aov1.av1gluwild.acid <- aov(pteh.GX.y ~ Temp + Batch , data=subset.wild.acid ) summary(aov1.av1gluwild.acid) aov1.av1gluwild.acid.res=subset.wild aov1.av1gluwild.acid.res$M1.Fit = fitted(aov1.av1gluwild.acid) aov1.av1gluwild.acid.res$M1.Resid = resid(aov1.av1gluwild.acid) shapiro.test(aov1.av1gluwild.acid.res$M1.Resid) #wild.deac aov1.av1gluwild.deac <- aov(pteh.GX.y ~ Temp + Batch , data=subset.wild.deac ) summary(aov1.av1gluwild.deac) aov1.av1gluwild.deac.res=subset.wild aov1.av1gluwild.deac.res$M1.Fit = fitted(aov1.av1gluwild.deac) aov1.av1gluwild.deac.res$M1.Resid = resid(aov1.av1gluwild.deac) shapiro.test(aov1.av1gluwild.deac.res$M1.Resid) #stac.acid aov1.av1glustac.acid <- aov(pteh.GX.y ~ Temp + Batch , data=subset.stac.acid ) summary(aov1.av1glustac.acid) aov1.av1glustac.acid.res=subset.stac aov1.av1glustac.acid.res$M1.Fit = fitted(aov1.av1glustac.acid) aov1.av1glustac.acid.res$M1.Resid = resid(aov1.av1glustac.acid) shapiro.test(aov1.av1glustac.acid.res$M1.Resid) #stac.deac aov1.av1glustac.deac <- aov(pteh.GX.y ~ Temp + Batch , data=subset.stac.deac ) summary(aov1.av1glustac.deac) aov1.av1glustac.deac.res=subset.stac aov1.av1glustac.deac.res$M1.Fit = fitted(aov1.av1glustac.deac) aov1.av1glustac.deac.res$M1.Resid = resid(aov1.av1glustac.deac) shapiro.test(aov1.av1glustac.deac.res$M1.Resid) # # # # PART Four rm(list=ls(all=TRUE)) # # # # # set working file data.wka <- read.csv("ASE350-summary-pteh-G-y-59-DELO.csv", header = TRUE) data.wka$PT <- c(rep("Acid only",27)) data.wkb <- read.csv("ASE350-summary-pteh-g-y-78-DELO.csv", header = TRUE) data.wkb$PT <- c(rep("Deacetylation and Acid",27)) data.wk <- rbind(data.wka , data.wkb) data.wk$ID <- paste(data.wk$Substrate, data.wk$Temp, data.wk$PT , sep="_") a<-nrow(data.wk) data.wk$row.names.g <- cbind(data.wk$row.names, 1:a) rm(a) data.wk$Confidence.interval.of.the.mean2 <- ((data.wk$SD*2.021 )/sqrt(data.wk$n)) toremove1 <- which(data.wk$Substrate=="Kramer 33a14") data <- data.wk[-toremove1,] # # Divide data according to the variable subset.glu.agg <-subset(data, data$carbohydrate=="Glucose.pteh.y" | data$carbohydrate=="Glucose.PTEH.y-DELO" ) subset.glu.agg.reord <- subset.glu.agg[c(7,15,8,16, 5,13,6,14, 3,11,4,12, 1,9,2,10),] subset.glu.agg.reord.wild <- subset.glu.agg.reord[c(1,2,3,4),] subset.glu.agg.reord.stacked <- subset.glu.agg.reord[c(5,6,7,8),] subset.glu.agg.reord.bmr6 <- subset.glu.agg.reord[c(9,10,11,12),] subset.glu.agg.reord.bmr12 <- subset.glu.agg.reord[c(13,14,15,16),] subset.glu.agg.col.mean <- cbind("Wild type"=subset.glu.agg.reord.wild$Mean, "stacked mutant"=subset.glu.agg.reord.stacked$Mean, "bmr6 mutant"=subset.glu.agg.reord.bmr6$Mean, "bmr12 mutant"=subset.glu.agg.reord.bmr12$Mean) subset.glu.agg.col.sd <- cbind("Wild type"=subset.glu.agg.reord.wild$Confidence.interval.of.the.mean2, "stacked mutant"=subset.glu.agg.reord.stacked$Confidence.interval.of.the.mean2 , "bmr6 mutant"=subset.glu.agg.reord.bmr6$Confidence.interval.of.the.mean2, "bmr12 mutant"=subset.glu.agg.reord.bmr12$Confidence.interval.of.the.mean2) subset.xyl.agg <-subset(data, data$carbohydrate=="Xylose.pteh.y" | data$carbohydrate=="Xylose.PTEH.y-DELO") subset.xyl.agg.reord <- subset.xyl.agg[c(7,15,8,16, 5,13,6,14, 3,11,4,12, 1,9,2,10),] subset.xyl.agg.reord.wild <- subset.xyl.agg.reord[c(1,2,3,4),] subset.xyl.agg.reord.stacked <- subset.xyl.agg.reord[c(5,6,7,8),] subset.xyl.agg.reord.bmr6 <- subset.xyl.agg.reord[c(9,10,11,12),] subset.xyl.agg.reord.bmr12 <- subset.xyl.agg.reord[c(13,14,15,16),] subset.xyl.agg.col.mean <- cbind("Wild type"=subset.xyl.agg.reord.wild$Mean, "stacked mutant"=subset.xyl.agg.reord.stacked$Mean, "bmr6 mutant"=subset.xyl.agg.reord.bmr6$Mean, "bmr12 mutant"=subset.xyl.agg.reord.bmr12$Mean) subset.xyl.agg.col.sd <- cbind("Wild type"=subset.xyl.agg.reord.wild$Confidence.interval.of.the.mean2, "stacked mutant"=subset.xyl.agg.reord.stacked$Confidence.interval.of.the.mean2 , "bmr6 mutant"=subset.xyl.agg.reord.bmr6$Confidence.interval.of.the.mean2, "bmr12 mutant"=subset.xyl.agg.reord.bmr12$Confidence.interval.of.the.mean2) subset.glxy.agg <-subset(data, data$carbohydrate=="Glucose.Xylose.pteh.y" | data$carbohydrate=="Glucose.Xylose.PTEH.y-DELO") subset.glxy.agg.reord <- subset.glxy.agg[c(7,15,8,16, 5,13,6,14, 3,11,4,12, 1,9,2,10),] subset.glxy.agg.reord.wild <- subset.glxy.agg.reord[c(1,2,3,4),] subset.glxy.agg.reord.stacked <- subset.glxy.agg.reord[c(5,6,7,8),] subset.glxy.agg.reord.bmr6 <- subset.glxy.agg.reord[c(9,10,11,12),] subset.glxy.agg.reord.bmr12 <- subset.glxy.agg.reord[c(13,14,15,16),] subset.glxy.agg.col.mean <- cbind("Wild type"=subset.glxy.agg.reord.wild$Mean, "stacked mutant"=subset.glxy.agg.reord.stacked$Mean, "bmr6 mutant"=subset.glxy.agg.reord.bmr6$Mean, "bmr12 mutant"=subset.glxy.agg.reord.bmr12$Mean) subset.glxy.agg.col.sd <- cbind("Wild type"=subset.glxy.agg.reord.wild$Confidence.interval.of.the.mean2, "stacked mutant"=subset.glxy.agg.reord.stacked$Confidence.interval.of.the.mean2 , "bmr6 mutant"=subset.glxy.agg.reord.bmr6$Confidence.interval.of.the.mean2, "bmr12 mutant"=subset.glxy.agg.reord.bmr12$Confidence.interval.of.the.mean2) # # Plots color1 <- rep(c("bisque2","bisque4","chartreuse2","chartreuse4"),4) color2 <- c("bisque2","bisque4","chartreuse2","chartreuse4") #density1 <- rep(c(30,30),8) #angle1 <- rep(c(0,90),8) arrows1 <- c(1.5, 2.5, 3.5, 4.5, 6.5, 7.5, 8.5, 9.5, 11.5, 12.5, 13.5, 14.5, 16.5, 17.5, 18.5 ,19.5) labels1 <- 1:16 labels2 <- 1:16 pdf("ASE350-barplots-PTEH-g-y-DELO-2.pdf") par(mfrow=c(2,2)) barplot(subset.glu.agg.col.mean , beside=TRUE, main="Glucose", cex=0.6, cex.lab=0.8, cex.axis=0.8, ylab="Reactivity - Glucose yield (g/g)", ylim=c(0,1.1), names.arg=c("Wild type","Stacked mutant","bmr6 mutant","bmr12 mutant"), col=color1) #legend("topleft",horiz=TRUE,legend=c("150°C DA PT without deacetylation", "150°C DA PT with deacetylation", "160°C DA PT without deacetylation" , "160°C DA PT with deacetylation"), cex = 0.4, pch=15, col=color2) arrows(arrows1 , subset.glu.agg.reord$Mean - subset.glu.agg.reord$Confidence.interval.of.the.mean2 , arrows1 , subset.glu.agg.reord$Mean + subset.glu.agg.reord$Confidence.interval.of.the.mean2 , code=3, length=0.04, angle=90, col='black') barplot(subset.xyl.agg.col.mean , beside=TRUE, main="Xylose", cex=0.6, cex.lab=0.8, cex.axis=0.8, ylab="Reactivity - Xylose yield (g/g)", ylim=c(0,1.1), names.arg=c("Wild type","Stacked mutant","bmr6 mutant","bmr12 mutant"), col=color1) #legend("topleft",horiz=TRUE,legend=c("150°C DA PT without deacetylation", "150°C DA PT with deacetylation", "160°C DA PT without deacetylation" , "160°C DA PT with deacetylation"), cex = 0.4, pch=15, col=color2) arrows(arrows1 , subset.xyl.agg.reord$Mean - subset.xyl.agg.reord$Confidence.interval.of.the.mean2 , arrows1 , subset.xyl.agg.reord$Mean + subset.xyl.agg.reord$Confidence.interval.of.the.mean2 , code=3, length=0.04, angle=90, col='black') barplot(subset.glxy.agg.col.mean , beside=TRUE, main="Glucose and Xylose", cex=0.6, cex.lab=0.8, cex.axis=0.8, ylab="Reactivity - Glucose and Xylose yield (g/g)", ylim=c(0,1.1), names.arg=c("Wild type","Stacked mutant","bmr6 mutant","bmr12 mutant"), col=color1) #legend("topleft",horiz=TRUE,legend=c("150°C DA PT without deacetylation", "150°C DA PT with deacetylation", "160°C DA PT without deacetylation" , "160°C DA PT with deacetylation"), cex = 0.4, pch=15, col=color2) arrows(arrows1 , subset.glxy.agg.reord$Mean - subset.glxy.agg.reord$Confidence.interval.of.the.mean2 , arrows1 , subset.glxy.agg.reord$Mean + subset.glxy.agg.reord$Confidence.interval.of.the.mean2 , code=3, length=0.04, angle=90, col='black') dev.off() pdf("ASE350-barplots-PTEH-g-y-DELO-pub-2.pdf") par(mar=par()$mar+c(4,0,-2.5,-2)) barplot(subset.glxy.agg.col.mean , beside=TRUE, main=NULL, cex=1.2, cex.lab=1.2, cex.axis=1.2, xlab = "Sorghum feedstock", ylab="Total sugar yield (g/g)", ylim=c(0,1.05), names.arg=c("Wild type","Stacked mutant","bmr6 mutant","bmr12 mutant"), col=color1) arrows(arrows1 , subset.glxy.agg.reord$Mean - subset.glxy.agg.reord$Confidence.interval.of.the.mean2 , arrows1 , subset.glxy.agg.reord$Mean + subset.glxy.agg.reord$Confidence.interval.of.the.mean2 , code=3, length=0.04, angle=90, col='black') legend("bottom",inset=c(0.0,-0.37),horiz=FALSE, legend=c("150°C DA PT without deacetylation", "150°C DA PT with deacetylation", "160°C DA PT without deacetylation" , "160°C DA PT with deacetylation"), cex = 1, pch=15, col=color2, xpd=TRUE, bty='n') dev.off()