# # R script for ASE350 set 56 PTEH Optimal temperature DE # # 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 temp1 <- loadWorkbook("ASE350 Optimal Temperature Exp.xlsx") #temp1 <- loadWorkbook("150324 ASE350 Optimal Temperature Exp Feedstock Reactivity 2015-Set569.xlsx") PT.all <- readWorksheet(temp1,sheet="Digestion56-1",startCol=1,endCol=61) rm(temp1) PT <- PT.all [,c(1:4,7:9,11:13,23,25:29,31:35,36:42,43:52,53:56,57:58,61)] colnames(PT)<-c("Sample","Ftid","Ltid","WStid", "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("150324 ASE350 Optimal Temperature Exp Feedstock Reactivity 2015-Set569.xlsx") EH.all <- readWorksheet(temp2,sheet="Digestion56-2", header=TRUE,startCol=1,endCol=44) rm(temp2) EH <-EH.all[,c(1:3,5,9,13:16,20,27:32,33:38,39:44)] colnames(EH)<-c("Sample","Ftid","WStid","Ltid","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$X)/ 100)*cx)) # New calculated variables # pt.Gt.r PT only - total glucose release (g/g) # pt.Gt.y PT only - total glucan yield (%) # pt.tX.r PT only - total xylose release (g/g) # pt.tX.y PT only - total xylan yield (%) # pt.AX.r PT only - total acetate release (g/g) # pt.AX.y PT only - total acetyl yield (%) # pt.GXt.r PT only - total glucose+xylose release (g/g) # pt.GXt.y PT only - total glucan+xylan yield (%) # pt.GXAt.r PT only - total glucan+xylose+acetate release (g/g) # pt.GXAt.y PT only - total glucan+xylan+acetyl yield (%) # eh.G.r EH only - total glucose release (g/g) # eh.G.y EH only - total glucan yield (%) # eh.X.r EH only - total xylose release (g/g) # eh.X.y EH only - total xylan yield (%) # eh.GX.r EH only - total glucose+xylose release (g/g) # eh.GX.y EH only - total glucan+xylan yield (%) # pteh.G.r overall (PT+EH) glucose release (g/g) # pteh.G.y overall (PT+EH) glucan yield (%) # pteh.X.r overall (PT+EH) xylose release (g/g) # pteh.X.y overall (PT+EH) xylan yield (%) # pteh.GX.r overall (PT+EH) glucose+xylose release (g/g) (reactivity) # pteh.GX.y overall (PT+EH) glucan+xylane yield (%) (reactivity) data.set56<-as.data.frame(cbind(data$Sample.x, data$Ftid.x, data$Ltid.x, data$WStid, data$Temp, 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$pteh.GX.y)) colnames(data.set56)<-c("Sample", "Ftid", "Ltid", "WStid", "Temp", "EHtoPT", "pt.Gm.r", "pt.Xm.r", "pt.GXm.r", "pt.Gm.y", "pt.Xm.y", "pt.GXm.y", "pt.Gt.r", "pt.Xt.r", "pt.At.r", "pt.GXt.r", "pt.GXAt.r", "pt.Gt.y", "pt.Xt.y", "pt.At.y", "pt.GXt.y", "pt.GXAt.y", "eh.G.r", "eh.X.r", "eh.GX.r", "eh.G.y", "eh.X.y", "eh.GX.y", "pteh.G.r", "pteh.X.r", "pteh.GX.r", "pteh.G.y", "pteh.X.y", "pteh.GX.y") write.csv(data.set56, file ="ASE350-set56-data-DE.csv") eh.ref.lines <- (which(EH.all$Feedstock.Tracking.ID=="P080828CS" | EH.all$Feedstock.Tracking.ID=="P120927CS")) set56.eh.ref <- data.frame(EH.all[eh.ref.lines,]) # # # # PART One B rm(list=ls(all=TRUE)) # # # # # Load PT data temp1 <- loadWorkbook("150324 ASE350 Optimal Temperature Exp Feedstock Reactivity 2015-Set569.xlsx") PT.all <- readWorksheet(temp1,sheet="Digestion1WithOutDeacet59",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("150324 ASE350 Optimal Temperature Exp Feedstock Reactivity 2015-Set569.xlsx") EH.all <- readWorksheet(temp2,sheet="Digestion2WithOutDeacet59", 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$pteh.GX.y)) colnames(data.set59)<-c("Sample", "Ftid", "Ltid", "WStid", "Temp", "Batch", "EHtoPT", "pt.Gm.r", "pt.Xm.r", "pt.GXm.r", "pt.Gm.y", "pt.Xm.y", "pt.GXm.y", "pt.Gt.r", "pt.Xt.r", "pt.At.r", "pt.GXt.r", "pt.GXAt.r", "pt.Gt.y", "pt.Xt.y", "pt.At.y", "pt.GXt.y", "pt.GXAt.y", "eh.G.r", "eh.X.r", "eh.GX.r", "eh.G.y", "eh.X.y", "eh.GX.y", "pteh.G.r", "pteh.X.r", "pteh.GX.r", "pteh.G.y", "pteh.X.y", "pteh.GX.y") write.csv(data.set59, file ="ASE350-set59-data-DE.csv") eh.ref.lines <- (which(EH.all$Feedstock.Tracking.ID=="P080828CS" | EH.all$Feedstock.Tracking.ID=="P120927CS")) set59.eh.ref <- data.frame(EH.all[eh.ref.lines,]) # # # # PART Two A rm(list=ls(all=TRUE)) # # # # # set working directory data.wk <- read.csv("ASE350-set56-data-DE.csv", header = TRUE) data.wk$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-A" | data.wk$Sample=="Kramer 33a14-B") subset.2<-subset(data.wk, data.wk$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-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") 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$Substrate <- c("Kramer 33a14-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") data.wk_var_glu <- aggregate(data.wk$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-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") 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$Substrate <- c("Kramer 33a14-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") data.wk_lasquat_glu<-as.data.frame(tapply(data.wk$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-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") # 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$Substrate <- c("Kramer 33a14-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") data.wk_Conf_xyl <- as.data.frame((data.wk_sd_xyl$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-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") 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$Substrate <- c("Kramer 33a14-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") data.wk_firqua_xyl<-as.data.frame(tapply(data.wk$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-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") 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$Substrate <- c("Kramer 33a14-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") # Summary of data.wkset pteh.X.y summaryxylG<-merge(data.wk_n_xyl,data.wk_ave_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_med_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_sd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Rsd_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_Conf_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_var_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_min_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_max_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_ran_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_firqua_xyl, by="Substrate") summaryxylG<-merge(summaryxylG,data.wk_lasquat_xyl, by="Substrate") summaryxylG$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-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") 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$Substrate <- c("Kramer 33a14-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") data.wk_var_pteh.GX.y <- aggregate(data.wk$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-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") 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$Substrate <- c("Kramer 33a14-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") data.wk_lasquat_pteh.GX.y<-as.data.frame(tapply(data.wk$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-A_130","Kramer 33a14-B_130", "Sorghum BMR12_150", "Sorghum BMR12_160", "Sorghum BMR12_170", "Sorghum BMR12_180", "Sorghum BMR6_150", "Sorghum BMR6_160", "Sorghum BMR6_170", "Sorghum BMR6_180", "Sorghum Stacked_150", "Sorghum Stacked_160", "Sorghum Stacked_170", "Sorghum Stacked_180", "Sorghum wild_150", "Sorghum wild_160", "Sorghum wild_170", "Sorghum wild_180") # 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"), sep = "_") write.csv(summary.pteh.G, file ="ASE350-summary-pteh-G-y-56-DE.csv") # # # # PART Two B rm(list=ls(all=TRUE)) # # # # # set working file data.wk <- read.csv("ASE350-set59-data-DE.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-DE.csv") # # # # PART Three rm(list=ls(all=TRUE)) # # # # # set working file 1 data.wk.1 <- read.csv("ASE350-set56-data-DE.csv", header = TRUE) data.wk.1$ID <- paste(data.wk.1$Sample, data.wk.1$Temp, "Acid only" , sep="_") a.1<-nrow(data.wk.1) data.wk.1$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$Sample=="Kramer 33a14-A" | data.wk.1$Sample=="Kramer 33a14-B") 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) data.sor.all.1.150.160 <- which(data.sor.all.1$Temp=="150" | data.sor.all.1$Temp=="160") data.sor.all.1.170.180 <- data.sor.all.1 [-data.sor.all.1.150.160,] data.sor.sel.1.150.160 <- which(data.sor.sel.1$Temp=="160" | data.sor.sel.1$Temp=="160") data.sor.sel.1.170.180 <- data.sor.sel.1 [-data.sor.sel.1.150.160,] # # set working file 2 data.wk.2 <- read.csv("ASE350-summary-pteh-G-y-56-DE.csv", header = TRUE) data.wk.2$ID <- paste(data.wk.2$Substrate, data.wk.2$Temp, "Acid only" , sep="_") a.2<-nrow(data.wk.2) data.wk.2$row.names.g <- cbind(data.wk.2$row.names, 1:a.2) rm(a.2) toremove1.2 <- which(data.wk.2$Substrate=="Kramer 33a14-A" | data.wk.2$Substrate=="Kramer 33a14-B" | data.wk.2$Temp=="150" | data.wk.2$Temp=="160") data.2 <- data.wk.2[-toremove1.2,] # # Divide data according to the variable subset.glu.agg.2 <-subset(data.2, data.2$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 file 3 data.wk.3 <- read.csv("ASE350-set59-data-DE.csv", header = TRUE) data.wk.3$ID <- paste(data.wk.3$Sample, data.wk.3$Temp, "Acid only" , sep="_") a.3<-nrow(data.wk.3) data.wk.3$row.names.g <- cbind(data.wk.3$row.names, 1:a.3) rm(a.3) # # Outlier #outlier.3 <- which(data.wk.3$WStid=="18767") #data.wk.3 <- data.wk.3[-outlier.3,] # # 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$Sample=="Sorghum BMR12") data.sor.all.2 <- rbind(subset.2.2,subset.3.2,subset.4.2,subset.5.2) data.sor.sel.2 <- rbind(subset.2.2,subset.3.2) data.sor.all.2.150.160 <- data.sor.all.2 data.sor.sel.2.150.160 <- data.sor.sel.2 # # set working file 4 data.wk.4 <- read.csv("ASE350-summary-pteh-G-y-59-DE.csv", header = TRUE) data.wk.4$ID <- paste(data.wk.4$Substrate, data.wk.4$Temp, "Acid only" , 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) toremove4.2 <- which(data.wk.4$Substrate=="Kramer 33a14") data.4 <- data.wk.4[-toremove4.2,] # # Divide data according to the variable subset.glu.agg.2 <-subset(data.2, data.2$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") # # Divide data according to the variable subset.glu.agg.4 <-subset(data.4, data.4$carbohydrate=="Glucose.pteh.y") subset.xyl.agg.4 <-subset(data.4, data.4$carbohydrate=="Xylose.pteh.y") subset.glxy.agg.4 <-subset(data.4, data.4$carbohydrate=="Glucose.Xylose.pteh.y") # # set working file 5 data.sor.all.1a <- data.sor.all.1.170.180[,c(1:4,4,5:6,6,7,14:23,24:29,30:35,36)] colnames(data.sor.all.1a)<-c("X","Sample","Ftid","LtidA","LtidC","WStid","Temp","Batch","EHtoPT", "pt.Gt.r","pt.Xt.r","pt.At.r","pt.GXt.r","pt.GXAt.r","pt.Gt.y","pt.Xt.y","pt.At.y","pt.GXt.y","pt.GXAt.y", "eh.G.r","eh.X.r","eh.GX.r","eh.G.y","eh.X.y","eh.GX.y", "pteh.G.r","pteh.X.r","pteh.GX.r","pteh.G.y","pteh.X.y","pteh.GX.y", "ID") data.sor.all.1a$Batch <- "A" data.sor.all.2a <- data.sor.all.2.150.160[,c(1:4,4,5:8,15:24,25:30,31:36,37)] 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$PT <- c(rep("Acid only",47)) a.5<-nrow(data.sor.all.raw) data.sor.all.raw$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$Sample=="Sorghum BMR12") data.sor.all <- rbind(subset.2,subset.3,subset.4,subset.5) data.sor.sel <- rbind(subset.2,subset.3) subset.glu.agg <- rbind(subset.glu.agg.2,subset.glu.agg.4) subset.glu.agg <- subset.glu.agg[,-19] a.6<-nrow(subset.glu.agg) subset.glu.agg$row.names.g <- cbind(subset.glu.agg$row.names, 1:a.6) rm(a.6) subset.glu.agg$PT <- c(rep("Acid only",16)) subset.xyl.agg <- rbind(subset.xyl.agg.2,subset.xyl.agg.4) subset.xyl.agg <- subset.xyl.agg[,-19] 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$PT <- c(rep("Acid only",16)) subset.glxy.agg <- rbind(subset.glxy.agg.2,subset.glxy.agg.4) subset.glxy.agg <- subset.glxy.agg[,-19] a.8<-nrow(subset.glxy.agg) subset.glxy.agg$row.names.g <- cbind(subset.glxy.agg$row.names, 1:a.8) rm(a.8) subset.glxy.agg$PT <- c(rep("Acid only",16)) # # 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, "160") # # 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 2 - pteh.G.y aov2.av1glu <- lm(pteh.G.y ~ Sample + as.factor(Temp) + Sample:as.factor(Temp), data=data.sor.sel) summary(aov2.av1glu) Anova(aov2.av1glu, type="2") aov2.av1glu.res=data.sor.sel aov2.av1glu.res$M1.Fit = fitted(aov2.av1glu) aov2.av1glu.res$M1.Resid = resid(aov2.av1glu) shapiro.test(aov2.av1glu.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov2.av1glu.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose PTEH yield", col=as.factor(aov2.av1glu.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov2.av1glu.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov2.av1glu.res$M1.Resid), sd=sd(aov2.av1glu.res$M1.Resid)), add=TRUE) qqnorm(aov2.av1glu.res$M1.Resid, main="Q-Q Plot - Glucose PTEH yield", cex.main=0.7) qqline(aov2.av1glu.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov2.av1glu.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose PTH yield", col=as.factor(aov2.av1glu.res$ID), pch=20, cex.main=0.7) # # anova 2 - pteh.X.y aov2.av1xyl <- lm(pteh.X.y ~ Sample + as.factor(Temp) + Sample*as.factor(Temp), data=data.sor.sel) summary(aov2.av1xyl) Anova(aov2.av1xyl, type="2") aov2.av1xyl.res=data.sor.sel aov2.av1xyl.res$M1.Fit = fitted(aov2.av1xyl) aov2.av1xyl.res$M1.Resid = resid(aov2.av1xyl) shapiro.test(aov2.av1xyl.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov2.av1xyl.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose PTEH yield", col=as.factor(aov2.av1xyl.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov2.av1xyl.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov2.av1xyl.res$M1.Resid), sd=sd(aov2.av1xyl.res$M1.Resid)), add=TRUE) qqnorm(aov2.av1xyl.res$M1.Resid, main="Q-Q Plot - Xylose PTEH yield", cex.main=0.7) qqline(aov2.av1xyl.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov2.av1xyl.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose PTEH yield", col=as.factor(aov2.av1xyl.res$ID), pch=20, cex.main=0.7) dev.off() # # anova 2 - pteh.GX.y aov2.av1glxy <- lm(pteh.GX.y ~ Sample + as.factor(Temp) + Sample*as.factor(Temp), data=data.sor.sel) summary(aov2.av1glxy) Anova(aov2.av1glxy, type="2") aov2.av1glxy.res=data.sor.sel aov2.av1glxy.res$M1.Fit = fitted(aov2.av1glxy) aov2.av1glxy.res$M1.Resid = resid(aov2.av1glxy) shapiro.test(aov2.av1glxy.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov2.av1glxy.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose.Xylose PTEH yield", col=as.factor(aov2.av1glxy.res$ID), pch=20, cex.main=0.7) abline(h=0, lty=1) hist(aov2.av1glxy.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose.Xylose PTEH yield",col="grey", cex.main=0.7) curve(dnorm(x, mean=mean(aov2.av1glxy.res$M1.Resid), sd=sd(aov2.av1glxy.res$M1.Resid)), add=TRUE) qqnorm(aov2.av1glxy.res$M1.Resid, main="Q-Q Plot - Glucose.Xylose PTEH yield", cex.main=0.7) qqline(aov2.av1glxy.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov2.av1glxy.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose.Xylose PTEH yield", col=as.factor(aov2.av1glxy.res$ID), pch=20, cex.main=0.7) dev.off() # # 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") subset.170 <-subset(data.sor.sel, data.sor.sel$Temp=="170") subset.180 <-subset(data.sor.sel, data.sor.sel$Temp=="180") # 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) leveneTest(subset.170$pteh.G.y, as.factor(subset.170$ID), mean) leveneTest(subset.170$pteh.G.y, as.factor(subset.170$ID), median) leveneTest(subset.180$pteh.G.y, as.factor(subset.180$ID), mean) leveneTest(subset.180$pteh.G.y, as.factor(subset.180$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) leveneTest(subset.170$pteh.X.y, as.factor(subset.170$ID), mean) leveneTest(subset.170$pteh.X.y, as.factor(subset.170$ID), median) leveneTest(subset.180$pteh.X.y, as.factor(subset.180$ID), mean) leveneTest(subset.180$pteh.X.y, as.factor(subset.180$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) leveneTest(subset.170$pteh.GX.y, as.factor(subset.170$ID), mean) leveneTest(subset.170$pteh.GX.y, as.factor(subset.170$ID), median) leveneTest(subset.180$pteh.GX.y, as.factor(subset.180$ID), mean) leveneTest(subset.180$pteh.GX.y, as.factor(subset.180$ID), median) # # Glucose #150 aov1.av1glu150 <- aov(pteh.G.y ~ Sample, data=subset.150) summary(aov1.av1glu150) 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$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glu150.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose 150 PTEH yield", col=as.factor(aov1.av1glu150.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glu150.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose 150 PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glu150.res$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) #160 aov1.av1glu160 <- aov(pteh.G.y ~ 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$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glu160.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose 160 pteh yield", col=as.factor(aov1.av1glu160.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glu160.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose 160 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glu160.res$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) #170 aov1.av1glu170 <- aov(pteh.G.y ~ Sample, data=subset.170) summary(aov1.av1glu170) aov1.av1glu170.res=subset.170 aov1.av1glu170.res$M1.Fit = fitted(aov1.av1glu170) aov1.av1glu170.res$M1.Resid = resid(aov1.av1glu170) shapiro.test(aov1.av1glu170.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glu170.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose 170 pteh yield", col=as.factor(aov1.av1glu170.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glu170.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose 170 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glu170.res$M1.Resid, main="Q-Q Plot - Glucose 170 pteh yield", cex.main=0.7) qqline(aov1.av1glu170.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glu170.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose 170 pteh yield", col=as.factor(aov1.av1glu170.res$ID), pch=20, cex.main=0.7) #180 aov1.av1glu180 <- aov(pteh.G.y ~ Sample, data=subset.180) summary(aov1.av1glu180) aov1.av1glu180.res=subset.180 aov1.av1glu180.res$M1.Fit = fitted(aov1.av1glu180) aov1.av1glu180.res$M1.Resid = resid(aov1.av1glu180) shapiro.test(aov1.av1glu180.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glu180.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose 180 pteh yield", col=as.factor(aov1.av1glu180.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glu180.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose 180 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glu180.res$M1.Resid, main="Q-Q Plot - Glucose 180 pteh yield", cex.main=0.7) qqline(aov1.av1glu180.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glu180.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose 180 pteh yield", col=as.factor(aov1.av1glu180.res$ID), pch=20, cex.main=0.7) # # Xylose #150 aov1.av1xyl150 <- aov(pteh.X.y ~ Sample, data=subset.150) summary(aov1.av1xyl150) 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$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xyl150.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose 150 PTEH yield", col=as.factor(aov1.av1xyl150.res$ID), pch=20, cex.main=0.7) hist(aov1.av1xyl150.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose 150 PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1xyl150.res$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) #160 aov1.av1xyl160 <- aov(pteh.X.y ~ Sample, data=subset.160) summary(aov1.av1xyl160) 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$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xyl160.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose 160 pteh yield", col=as.factor(aov1.av1xyl160.res$ID), pch=20, cex.main=0.7) hist(aov1.av1xyl160.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose 160 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1xyl160.res$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) #170 aov1.av1xyl170 <- aov(pteh.X.y ~ Sample, data=subset.170) summary(aov1.av1xyl170) aov1.av1xyl170.res=subset.170 aov1.av1xyl170.res$M1.Fit = fitted(aov1.av1xyl170) aov1.av1xyl170.res$M1.Resid = resid(aov1.av1xyl170) shapiro.test(aov1.av1xyl170.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xyl170.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose 170 pteh yield", col=as.factor(aov1.av1xyl170.res$ID), pch=20, cex.main=0.7) hist(aov1.av1xyl170.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose 170 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1xyl170.res$M1.Resid, main="Q-Q Plot - Xylose 170 pteh yield", cex.main=0.7) qqline(aov1.av1xyl170.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1xyl170.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose 170 pteh yield", col=as.factor(aov1.av1xyl170.res$ID), pch=20, cex.main=0.7) #180 aov1.av1xyl180 <- aov(pteh.X.y ~ Sample, data=subset.180) summary(aov1.av1xyl180) aov1.av1xyl180.res=subset.180 aov1.av1xyl180.res$M1.Fit = fitted(aov1.av1xyl180) aov1.av1xyl180.res$M1.Resid = resid(aov1.av1xyl180) shapiro.test(aov1.av1xyl180.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1xyl180.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Xylose 180 pteh yield", col=as.factor(aov1.av1xyl180.res$ID), pch=20, cex.main=0.7) hist(aov1.av1xyl180.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Xylose 180 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1xyl180.res$M1.Resid, main="Q-Q Plot - Xylose 180 pteh yield", cex.main=0.7) qqline(aov1.av1xyl180.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1xyl180.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Xylose 180 pteh yield", col=as.factor(aov1.av1xyl180.res$ID), pch=20, cex.main=0.7) # # Glucose+Xylose #150 aov1.av1glxy150 <- aov(pteh.GX.y ~ Sample, data=subset.150) summary(aov1.av1glxy150) 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$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxy150.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 150 PTEH yield", col=as.factor(aov1.av1glxy150.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glxy150.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose 150 PTEH yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glxy150.res$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) #160 aov1.av1glxy160 <- aov(pteh.GX.y ~ Sample, data=subset.160) summary(aov1.av1glxy160) 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$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxy160.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 160 pteh yield", col=as.factor(aov1.av1glxy160.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glxy160.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose 160 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glxy160.res$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) #170 aov1.av1glxy170 <- aov(pteh.GX.y ~ Sample, data=subset.170) summary(aov1.av1glxy170) aov1.av1glxy170.res=subset.170 aov1.av1glxy170.res$M1.Fit = fitted(aov1.av1glxy170) aov1.av1glxy170.res$M1.Resid = resid(aov1.av1glxy170) shapiro.test(aov1.av1glxy170.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxy170.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 170 pteh yield", col=as.factor(aov1.av1glxy170.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glxy170.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose 170 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glxy170.res$M1.Resid, main="Q-Q Plot - Glucose+Xylose 170 pteh yield", cex.main=0.7) qqline(aov1.av1glxy170.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glxy170.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 170 pteh yield", col=as.factor(aov1.av1glxy170.res$ID), pch=20, cex.main=0.7) #180 aov1.av1glxy180 <- aov(pteh.GX.y ~ Sample, data=subset.180) summary(aov1.av1glxy180) aov1.av1glxy180.res=subset.180 aov1.av1glxy180.res$M1.Fit = fitted(aov1.av1glxy180) aov1.av1glxy180.res$M1.Resid = resid(aov1.av1glxy180) shapiro.test(aov1.av1glxy180.res$M1.Resid) windows(record=TRUE) par(mfrow=c(2,2)) plot(M1.Resid ~ M1.Fit, data=aov1.av1glxy180.res, xlab="Fitted value", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 180 pteh yield", col=as.factor(aov1.av1glxy180.res$ID), pch=20, cex.main=0.7) hist(aov1.av1glxy180.res$M1.Resid, xlab="Residual", ylab="Frequency", main="Histrograms of the residuals - Glucose+Xylose 180 pteh yield",col="grey", cex.main=0.7) qqnorm(aov1.av1glxy180.res$M1.Resid, main="Q-Q Plot - Glucose+Xylose 180 pteh yield", cex.main=0.7) qqline(aov1.av1glxy180.res$M1.Resid) plot(M1.Resid ~ row.names.g, data=aov1.av1glxy180.res, xlab="Row number", ylab="Residual", main="Residual versus fitted value - Glucose+Xylose 180 pteh yield", col=as.factor(aov1.av1glxy180.res$ID), pch=20, cex.main=0.7) # # # # PART Four rm(list=ls(all=TRUE)) # # # # # set working file data.wka <- read.csv("ASE350-summary-pteh-G-y-56-DE.csv", header = TRUE) remove.150.160.130 <- which(data.wka$Temp=="150" | data.wka$Temp=="160" | data.wka$Temp=="130") data.a <- data.wka[-remove.150.160.130,] data.a$PT <- c(rep("Acid only",24)) data.wkb <- read.csv("ASE350-summary-pteh-G-y-59-DE.csv", header = TRUE) remove.130 <- which(data.wkb$Temp=="130") data.b <- data.wkb[-remove.130,] data.b$PT <- c(rep("Acid only",24)) data <- rbind(data.a , data.b) data$ID <- paste(data$Substrate, data$Temp, data$PT , sep="_") a<-nrow(data) data$row.names.g <- cbind(data$row.names, 1:a) rm(a) data$Confidence.interval.of.the.mean2 <- ((data$SD*2.052 )/sqrt(data$n)) # # Divide data according to the variable subset.glu.agg <-subset(data, data$carbohydrate=="Glucose.pteh.y" | data$carbohydrate=="Glucose.pteh.y-NL") subset.glu.agg.reord <- subset.glu.agg[c(15,16,7,8, 13,14,5,6, 11,12,3,4, 9,10,1,2),] subset.glu.agg.reord.wild <- subset.glu.agg.reord[c(1,2,3,4),] subset.glu.agg.reord.stacked <- subset.glu.agg.reord[c(5,6,7,8),] subset.glu.agg.reord.bmr6 <- subset.glu.agg.reord[c(9,10,11,12),] subset.glu.agg.reord.bmr12 <- subset.glu.agg.reord[c(13,14,15,16),] subset.glu.agg.col.mean <- cbind("Wild type"=subset.glu.agg.reord.wild$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$carbohydrate=="Xylose.pteh.y-NL") subset.xyl.agg.reord <- subset.xyl.agg[c(15,16,7,8, 13,14,5,6, 11,12,3,4, 9,10,1,2),] subset.xyl.agg.reord.wild <- subset.xyl.agg.reord[c(1,2,3,4),] subset.xyl.agg.reord.stacked <- subset.xyl.agg.reord[c(5,6,7,8),] subset.xyl.agg.reord.bmr6 <- subset.xyl.agg.reord[c(9,10,11,12),] subset.xyl.agg.reord.bmr12 <- subset.xyl.agg.reord[c(13,14,15,16),] subset.xyl.agg.col.mean <- cbind("Wild type"=subset.xyl.agg.reord.wild$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$carbohydrate=="Glucose.Xylose.pteh.y-NL") subset.glxy.agg.reord <- subset.glxy.agg[c(15,16,7,8, 13,14,5,6, 11,12,3,4, 9,10,1,2),] subset.glxy.agg.reord.wild <- subset.glxy.agg.reord[c(1,2,3,4),] subset.glxy.agg.reord.stacked <- subset.glxy.agg.reord[c(5,6,7,8),] subset.glxy.agg.reord.bmr6 <- subset.glxy.agg.reord[c(9,10,11,12),] subset.glxy.agg.reord.bmr12 <- subset.glxy.agg.reord[c(13,14,15,16),] subset.glxy.agg.col.mean <- cbind("Wild type"=subset.glxy.agg.reord.wild$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$Confidence.interval.of.the.mean2) # # Plots color1 <- rep(c("goldenrod1","goldenrod3","darkorange3","darkorange4"),4) color2 <- c("goldenrod1","goldenrod3","darkorange3","darkorange4") arrows1 <- c(1.5, 2.5, 3.5, 4.5, 6.5, 7.5, 8.5, 9.5, 11.5, 12.5, 13.5, 14.5, 16.5, 17.5, 18.5 ,19.5) labels1 <- 1:16 labels2 <- 1:16 pdf("ASE350-barplots-PTEH-g-y-DE-2.pdf") par(mfrow=c(2,2)) barplot(subset.glu.agg.col.mean , beside=TRUE, main="Glucose PTEH yield DE", cex=0.6, cex.lab=0.8, cex.axis=0.8, ylab="Total sugar yield (g/g)", ylim=c(0,1.1), names.arg=c("Wild type","Stacked mutant","bmr6 mutant","bmr12 mutant"), col=color1) legend("top",horiz=TRUE,legend=c("150°C", "160°C", "170°C" , "180°C"), cex = 0.4, pch=15, col=color2) arrows(arrows1 , subset.glu.agg.reord$Mean - subset.glu.agg.reord$Confidence.interval.of.the.mean2 , arrows1 , subset.glu.agg.reord$Mean + subset.glu.agg.reord$Confidence.interval.of.the.mean2 , code=3, length=0.04, angle=90, col='black') barplot(subset.xyl.agg.col.mean , beside=TRUE, main="Xylose PTEH yield DE", cex=0.6, cex.lab=0.8, cex.axis=0.8, ylab="Total sugar yield (g/g)", ylim=c(0,1.1), names.arg=c("Wild type","Stacked mutant","bmr6 mutant","bmr12 mutant"), col=color1) legend("top",horiz=TRUE,legend=c("150°C", "160°C", "170°C" , "180°C"), cex = 0.4, pch=15, col=color2) arrows(arrows1 , subset.xyl.agg.reord$Mean - subset.xyl.agg.reord$Confidence.interval.of.the.mean2 , arrows1 , subset.xyl.agg.reord$Mean + subset.xyl.agg.reord$Confidence.interval.of.the.mean2 , code=3, length=0.04, angle=90, col='black') barplot(subset.glxy.agg.col.mean , beside=TRUE, main="Glucose+Xylose PTEH yield DE", cex=0.6, cex.lab=0.8, cex.axis=0.8, ylab="Total sugar yield (g/g)", ylim=c(0,1.1), names.arg=c("Wild type","Stacked mutant","bmr6 mutant","bmr12 mutant"), col=color1) legend("top",horiz=TRUE,legend=c("150°C", "160°C", "170°C" , "180°C"), cex = 0.4, pch=15, col=color2) arrows(arrows1 , subset.glxy.agg.reord$Mean - subset.glxy.agg.reord$Confidence.interval.of.the.mean2 , arrows1 , subset.glxy.agg.reord$Mean + subset.glxy.agg.reord$Confidence.interval.of.the.mean2 , code=3, length=0.04, angle=90, col='black') dev.off() pdf("ASE350-barplots-PTEH-g-y-DE-pub-2.pdf") par(mar=par()$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=TRUE, legend=c("150°C DA PT", "160°C DA PT ", "170°C DA PT", "180°C DA PT"), cex = 1.2, pch=15, col=color2, xpd=TRUE, bty='n') dev.off()