Rocafort-Gootenberg_Figure3ANCOM
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library("phyloseq")
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## lowess#Load original phyloseq object output from DADA2 pipeline and pull in new metadata
ps_gg_fp_f_prevalence_filter_2019_05_26<-readRDS("ps_gg_fp_f_prevalence_filter_2019_05_26")
readr::read_csv(
"Metadata_formatted_nat_comm_add_2021_10_24.csv",
col_names = TRUE,
col_types = NULL,
col_select = NULL,
id = NULL,
locale = default_locale(),
na = c("", "NA", "empty", "EMPTY"),
quote = "\"",
comment = "",
trim_ws = TRUE,
skip = 0,
name_repair = "unique",
num_threads = readr_threads(),
progress = show_progress(),
show_col_types = should_show_types(),
skip_empty_rows = TRUE,
lazy = TRUE
) -> new_metadata## Rows: 597 Columns: 88
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (26): X, SampleID, subject_id, Race, Ethnicity, unique_id, sequencing_da...
## dbl (62): primer_used, read_count, age, height_cm, height_in, weight_kg, wei...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.### add {SampleID} as rownames
new_metadata_as_sample_data <- phyloseq::sample_data(new_metadata)
phyloseq::sample_names(new_metadata_as_sample_data) <- dplyr::pull(new_metadata, 1)
phyloseq::sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26) <- new_metadata_as_sample_data## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
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## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'#Fix randomness
set.seed(1)#Figure 3A
#FIGURE 3A
#--------------------------------------------------------------------------------------------------------------
#COMPARISON non-MSM
#Comparison NEG-ART
#Transform count data in the phyloseq object
ps_gg_fp_f_prevalence_filter_2019_05_26_proportion<-transform_sample_counts(ps_gg_fp_f_prevalence_filter_2019_05_26, function(x)(x/sum(x)))## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Select samples of interest and update phyloseq object
metadata<-sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26)
metadata<-metadata[metadata$sample_cohort=="boston",,drop=F]
metadata<-metadata[metadata$hiv_phenotype%in%c("1_hiv_negative","2_suppressed"),,drop=F]
metadata<-as.data.frame(as.matrix(metadata[metadata$sexual_orientation!="MSM",,drop=F]))
sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)<-metadata## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Run PCoA on the phyloseq object
ordination<-ordinate(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion, "PCoA", "unifrac")## Warning in matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, : data
## length [8987] is not a sub-multiple or multiple of the number of rows [4494]ordination$values[1:2,]## Eigenvalues Relative_eig Broken_stick Cumul_eig Cumul_br_stick
## 1 2.442768 0.06544973 0.05064846 0.06544973 0.05064846
## 2 1.813608 0.04859246 0.04093972 0.11404219 0.09158818metadata_ordered<-metadata[row.names(ordination$vectors),,drop=FALSE]
all.equal(row.names(metadata_ordered), row.names(ordination$vectors))## [1] TRUEmetadata_ordered$Unifrac1<-ordination$vectors[,1]
metadata_ordered$Unifrac2<-ordination$vectors[,2]
#Let's plot the data
plot_noMSM_NEG_ART<-ggplot(data=metadata_ordered, aes(x=Unifrac1, y=Unifrac2))+geom_point(color="royalblue4", aes(alpha=hiv_phenotype), size=2, shape=16)+
theme_bw()+stat_ellipse(color="royalblue4", aes(alpha=hiv_phenotype), size=1)+scale_alpha_manual(values=c(1,0.6))+ylim(c(-0.55, 0.55))+xlim(c(-0.55,0.55))+
ggtitle("neg-art noMSM")+geom_point(data=metadata_ordered %>% group_by(hiv_phenotype) %>% summarise_at(vars(matches("UniFrac")), mean),size=5, color="royalblue4", aes(alpha=hiv_phenotype))## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.#Adonis (n = 104)
ASV_table<-as.data.frame(phyloseq::otu_table(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion))
all.equal(row.names(ASV_table), row.names(metadata_ordered))## [1] TRUEunifrac.distance<-unname(phyloseq::UniFrac(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion, weighted = FALSE)) ### unname fixes error introduced by Desctools see https://github.com/joey711/phyloseq/issues/1457## Warning in matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, : data
## length [8987] is not a sub-multiple or multiple of the number of rows [4494]attributes(unifrac.distance)$Labels <- phyloseq::sample_names(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)
print(vegan::adonis2(unifrac.distance~metadata_ordered$hiv_phenotype, data=ASV_table, permutations=1000)) -> adon_hiv_phenotype_nonmsm_neg_art## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## vegan::adonis2(formula = unifrac.distance ~ metadata_ordered$hiv_phenotype, data = ASV_table, permutations = 1000)
## Df SumOfSqs R2 F Pr(>F)
## metadata_ordered$hiv_phenotype 1 0.385 0.01032 1.064 0.2637
## Residual 102 36.938 0.98968
## Total 103 37.323 1.00000###*** r2 = 0.01032 p = 0.2637
# vegan::adonis2(formula = unifrac.distance ~ metadata_ordered$hiv_phenotype, data = ASV_table, permutations = 1000)
# Df SumOfSqs R2 F Pr(>F)
# metadata_ordered$hiv_phenotype 1 0.385 0.01032 1.064 0.2637
# Residual 102 36.938 0.98968
# Total 103 37.323 1.00000
#Comparison NEG-UNSUPPRESSED
#Transform count data in the phyloseq object
ps_gg_fp_f_prevalence_filter_2019_05_26_proportion<-transform_sample_counts(ps_gg_fp_f_prevalence_filter_2019_05_26, function(x)(x/sum(x)))## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Select samples of interest and update phyloseq object
metadata<-sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26)
metadata<-metadata[metadata$sample_cohort=="boston",,drop=F]
metadata<-metadata[metadata$hiv_phenotype%in%c("1_hiv_negative","4_unsuppressed"),,drop=F]
metadata<-as.data.frame(as.matrix(metadata[metadata$sexual_orientation!="MSM",,drop=F]))
sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)<-metadata## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Run PCoA on the phyloseq object
ordination<-ordinate(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion, "PCoA", "unifrac")## Warning in matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, : data
## length [8987] is not a sub-multiple or multiple of the number of rows [4494]ordination$values[1:2,]## Eigenvalues Relative_eig Broken_stick Cumul_eig Cumul_br_stick
## 1 2.399651 0.06943654 0.05406681 0.06943654 0.05406681
## 2 1.759546 0.05091440 0.04354049 0.12035094 0.09760729metadata_ordered<-metadata[row.names(ordination$vectors),,drop=FALSE]
all.equal(row.names(metadata_ordered), row.names(ordination$vectors))## [1] TRUEmetadata_ordered$Unifrac1<-ordination$vectors[,1]
metadata_ordered$Unifrac2<-ordination$vectors[,2]
#Let's plot the data
plot_noMSM_NEG_UNSUP<-ggplot(data=metadata_ordered, aes(x=Unifrac1, y=Unifrac2))+geom_point(color="royalblue4", aes(alpha=hiv_phenotype), size=2, shape=16)+
theme_bw()+stat_ellipse(color="royalblue4", aes(alpha=hiv_phenotype), size=1)+scale_alpha_manual(values=c(1,0.3))+ylim(c(-0.55, 0.55))+xlim(c(-0.55,0.55))+
ggtitle("neg-unsuppressed noMSM")+geom_point(data=metadata_ordered %>% group_by(hiv_phenotype) %>% summarise_at(vars(matches("UniFrac")), mean),size=5, color="royalblue4", aes(alpha=hiv_phenotype))
#Adonis
ASV_table<-as.data.frame(phyloseq::otu_table(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion))
all.equal(row.names(ASV_table), row.names(metadata_ordered))## [1] TRUEunifrac.distance<-unname(phyloseq::UniFrac(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion, weighted = FALSE)) ### unname fixes error introduced by Desctools see https://github.com/joey711/phyloseq/issues/1457## Warning in matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, : data
## length [8987] is not a sub-multiple or multiple of the number of rows [4494]attributes(unifrac.distance)$Labels <- phyloseq::sample_names(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)
print(vegan::adonis2(unifrac.distance~metadata_ordered$hiv_phenotype, data=ASV_table, permutations=1000)) -> adon_hiv_phenotype_nonmsm_neg_unsup## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## vegan::adonis2(formula = unifrac.distance ~ metadata_ordered$hiv_phenotype, data = ASV_table, permutations = 1000)
## Df SumOfSqs R2 F Pr(>F)
## metadata_ordered$hiv_phenotype 1 0.393 0.01138 1.0819 0.2468
## Residual 94 34.166 0.98862
## Total 95 34.559 1.00000###*** r2 = 0.01138 p = 0.2468
# vegan::adonis2(formula = unifrac.distance ~ metadata_ordered$hiv_phenotype, data = ASV_table, permutations = 1000)
# Df SumOfSqs R2 F Pr(>F)
# metadata_ordered$hiv_phenotype 1 0.393 0.01138 1.0819 0.2468
# Residual 94 34.166 0.98862
# Total 95 34.559 1.00000
#COMPARISON MSM
#Comparison NEG-ART
#Transform count data in the phyloseq object
ps_gg_fp_f_prevalence_filter_2019_05_26_proportion<-transform_sample_counts(ps_gg_fp_f_prevalence_filter_2019_05_26, function(x)(x/sum(x)))## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Select samples of interest and update phyloseq object
metadata<-phyloseq::sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26)
metadata<-metadata[metadata$sample_cohort=="boston",,drop=F]
metadata<-metadata[metadata$hiv_phenotype%in%c("1_hiv_negative","2_suppressed"),,drop=F]
metadata<-as.data.frame(as.matrix(metadata[metadata$sexual_orientation=="MSM",,drop=F]))
phyloseq::sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)<-metadata## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Run PCoA on the phyloseq object
ordination<-phyloseq::ordinate(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion, "PCoA", "unifrac")## Warning in matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, : data
## length [8987] is not a sub-multiple or multiple of the number of rows [4494]ordination$values[1:2,]## Eigenvalues Relative_eig Broken_stick Cumul_eig Cumul_br_stick
## 1 3.021341 0.11143795 0.06677409 0.1114380 0.06677409
## 2 1.840634 0.06788921 0.05307546 0.1793272 0.11984956metadata_ordered<-metadata[row.names(ordination$vectors),,drop=FALSE]
all.equal(row.names(metadata_ordered), row.names(ordination$vectors))## [1] TRUEmetadata_ordered$Unifrac1<-ordination$vectors[,1]
metadata_ordered$Unifrac2<-ordination$vectors[,2]
#Let's plot the data
plot_MSM_NEG_ART<-ggplot(data=metadata_ordered, aes(x=Unifrac1, y=Unifrac2))+geom_point(color="royalblue4", aes(alpha=hiv_phenotype), size=2, shape=16)+
theme_bw()+stat_ellipse(color="royalblue4", aes(alpha=hiv_phenotype), size=1)+scale_alpha_manual(values=c(1,0.6))+ylim(c(-0.55, 0.55))+xlim(c(-0.55,0.55))+
ggtitle("neg-art MSM")+geom_point(data=metadata_ordered %>% group_by(hiv_phenotype) %>% summarise_at(vars(matches("UniFrac")), mean),size=5, color="royalblue4", aes(alpha=hiv_phenotype))
#Adonis (n=74)
ASV_table<-as.data.frame(phyloseq::otu_table(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion))
all.equal(row.names(ASV_table), row.names(metadata_ordered))## [1] TRUEunifrac.distance<-unname(phyloseq::UniFrac(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion, weighted = FALSE)) ### unname fixes error introduced by Desctools see https://github.com/joey711/phyloseq/issues/1457## Warning in matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, : data
## length [8987] is not a sub-multiple or multiple of the number of rows [4494]attributes(unifrac.distance)$Labels <- phyloseq::sample_names(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)
print(vegan::adonis2(unifrac.distance~metadata_ordered$hiv_phenotype, data=ASV_table, permutations=1000)) -> adon_hiv_phenotype_msm_neg_art## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## vegan::adonis2(formula = unifrac.distance ~ metadata_ordered$hiv_phenotype, data = ASV_table, permutations = 1000)
## Df SumOfSqs R2 F Pr(>F)
## metadata_ordered$hiv_phenotype 1 1.6479 0.06078 4.6594 0.000999 ***
## Residual 72 25.4644 0.93922
## Total 73 27.1123 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###*** r2 = 0.06078 p = 0.000999
# vegan::adonis2(formula = unifrac.distance ~ metadata_ordered$hiv_phenotype, data = ASV_table, permutations = 1000)
# Df SumOfSqs R2 F Pr(>F)
# metadata_ordered$hiv_phenotype 1 1.6479 0.06078 4.6594 0.000999 ***
# Residual 72 25.4644 0.93922
# Total 73 27.1123 1.00000
### COMPARISON MSM NEG-ART: Adonis: controlling for additional metadata
metadata_ordered$age <- as.numeric(metadata_ordered$age)
metadata_ordered$BMI <- as.numeric(metadata_ordered$BMI)
metadata_ordered$days_on_art <- as.numeric(metadata_ordered$days_on_art)
### control for metadata with full n
covars_full_n <- c("Ethnicity", "age", "Race", "hiv_phenotype")
print(vegan::adonis2(as.formula(paste("unifrac.distance~metadata_ordered$", paste(covars_full_n, collapse = "+metadata_ordered$"), sep = "")), data=ASV_table, permutations=1000)) -> adon_hiv_phenotype_msm_neg_art_full_n## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## vegan::adonis2(formula = as.formula(paste("unifrac.distance~metadata_ordered$", paste(covars_full_n, collapse = "+metadata_ordered$"), sep = "")), data = ASV_table, permutations = 1000)
## Df SumOfSqs R2 F Pr(>F)
## metadata_ordered$Ethnicity 1 0.3770 0.01390 1.0761 0.259740
## metadata_ordered$age 1 0.3670 0.01354 1.0476 0.311688
## metadata_ordered$Race 4 1.9088 0.07040 1.3621 0.000999 ***
## metadata_ordered$hiv_phenotype 1 1.3367 0.04930 3.8155 0.000999 ***
## Residual 66 23.1228 0.85285
## Total 73 27.1123 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###*** hiv_phenotype r2 = 0.04930 p = 0.000999
# Df SumOfSqs R2 F Pr(>F)
# metadata_ordered$Ethnicity 1 0.3770 0.01390 1.0761 0.259740
# metadata_ordered$age 1 0.3670 0.01354 1.0476 0.311688
# metadata_ordered$Race 4 1.9088 0.07040 1.3621 0.000999 ***
# metadata_ordered$hiv_phenotype 1 1.3367 0.04930 3.8155 0.000999 ***
# Residual 66 23.1228 0.85285
# Total 73 27.1123 1.00000
### COMPARISON MSM NEG-ART: Adonis: controlling for additional metadata - removing samples w/o values - BMI/comorbidities/days_on_art/ART regimen
### remove samples with BMI/comorbidities/days_on_art/ART regimen is NA (n=74 -> n=62)
ps_gg_fp_f_prevalence_filter_2019_05_26_proportion_bcad <- ps_gg_fp_f_prevalence_filter_2019_05_26_proportion
metadata_ordered_bcad <- metadata_ordered[!is.na(metadata_ordered$hld_hx) & !is.na(metadata_ordered$current_art_class_consolid2) & !is.na(metadata_ordered$BMI) & !is.na(metadata_ordered$days_on_art),,drop=FALSE]
sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion_bcad) <- metadata_ordered_bcad## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'### subset ASV table and distance matrix
ASV_table_bcad<-as.data.frame(otu_table(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion_bcad))
all.equal(row.names(ASV_table_bcad), row.names(metadata_ordered_bcad))## [1] TRUEunifrac.distance_bcad <- unname(usedist::dist_subset(unifrac.distance, sample_names(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion_bcad))) ### unname fixes error introduced by Desctools see https://github.com/joey711/phyloseq/issues/1457
### run adonis
covars_bcad <- c("BMI", "hld_hx", "htn_hx", "cvd_hx", "dm2hx_dx", "hldhx_dx", "cvdhx_dx", "days_on_art", covars_full_n, "current_art_class_consolid2")
print(vegan::adonis2(as.formula(paste("unifrac.distance_bcad~metadata_ordered_bcad$", paste(covars_bcad, collapse = "+metadata_ordered_bcad$"), sep = "")), data=ASV_table_bcad, permutations=1000)) -> adon_hiv_phenotype_msm_neg_art_full_n_bcad## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## vegan::adonis2(formula = as.formula(paste("unifrac.distance_bcad~metadata_ordered_bcad$", paste(covars_bcad, collapse = "+metadata_ordered_bcad$"), sep = "")), data = ASV_table_bcad, permutations = 1000)
## Df SumOfSqs R2 F
## metadata_ordered_bcad$BMI 1 0.4333 0.01909 1.2523
## metadata_ordered_bcad$hld_hx 1 0.4858 0.02141 1.4041
## metadata_ordered_bcad$htn_hx 1 0.4440 0.01957 1.2833
## metadata_ordered_bcad$cvd_hx 1 0.3829 0.01687 1.1066
## metadata_ordered_bcad$dm2hx_dx 1 0.4034 0.01777 1.1657
## metadata_ordered_bcad$days_on_art 1 0.9015 0.03972 2.6053
## metadata_ordered_bcad$Ethnicity 1 0.3157 0.01391 0.9124
## metadata_ordered_bcad$age 1 0.3921 0.01728 1.1332
## metadata_ordered_bcad$Race 4 1.5540 0.06847 1.1228
## metadata_ordered_bcad$hiv_phenotype 1 0.7449 0.03282 2.1529
## metadata_ordered_bcad$current_art_class_consolid2 2 0.7212 0.03178 1.0421
## Residual 46 15.9165 0.70131
## Total 61 22.6952 1.00000
## Pr(>F)
## metadata_ordered_bcad$BMI 0.081918 .
## metadata_ordered_bcad$hld_hx 0.040959 *
## metadata_ordered_bcad$htn_hx 0.074925 .
## metadata_ordered_bcad$cvd_hx 0.238761
## metadata_ordered_bcad$dm2hx_dx 0.161838
## metadata_ordered_bcad$days_on_art 0.000999 ***
## metadata_ordered_bcad$Ethnicity 0.609391
## metadata_ordered_bcad$age 0.195804
## metadata_ordered_bcad$Race 0.093906 .
## metadata_ordered_bcad$hiv_phenotype 0.001998 **
## metadata_ordered_bcad$current_art_class_consolid2 0.338661
## Residual
## Total
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###*** hiv_phenotype r2 = 0.03282 p = 0.001998
# Df SumOfSqs R2 F Pr(>F)
# metadata_ordered_bcad$BMI 1 0.4333 0.01909 1.2523 0.106893
# metadata_ordered_bcad$hld_hx 1 0.4858 0.02141 1.4041 0.041958 *
# metadata_ordered_bcad$htn_hx 1 0.4440 0.01957 1.2833 0.086913 .
# metadata_ordered_bcad$cvd_hx 1 0.3829 0.01687 1.1066 0.224775
# metadata_ordered_bcad$dm2hx_dx 1 0.4034 0.01777 1.1657 0.176823
# metadata_ordered_bcad$days_on_art 1 0.9015 0.03972 2.6053 0.000999 ***
# metadata_ordered_bcad$Ethnicity 1 0.3157 0.01391 0.9124 0.632368
# metadata_ordered_bcad$age 1 0.3921 0.01728 1.1332 0.186813
# metadata_ordered_bcad$Race 4 1.5540 0.06847 1.1228 0.097902 .
# metadata_ordered_bcad$hiv_phenotype 1 0.7449 0.03282 2.1529 0.000999 ***
# metadata_ordered_bcad$current_art_class_consolid2 2 0.7212 0.03178 1.0421 0.313686
# Residual 46 15.9165 0.70131
# Total 61 22.6952 1.00000
#COMPARISON MSM
#Comparison NEG-UNSUPPRESSED
#Transform count data in the phyloseq object
ps_gg_fp_f_prevalence_filter_2019_05_26_proportion<-transform_sample_counts(ps_gg_fp_f_prevalence_filter_2019_05_26, function(x)(x/sum(x)))## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Select samples of interest and update phyloseq object
metadata<-phyloseq::sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26)
metadata<-metadata[metadata$sample_cohort=="boston",,drop=F]
metadata<-metadata[metadata$hiv_phenotype%in%c("1_hiv_negative","4_unsuppressed"),,drop=F]
metadata<-as.data.frame(as.matrix(metadata[metadata$sexual_orientation=="MSM",,drop=F]))
phyloseq::sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)<-metadata## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Run PCoA on the phyloseq object
ordination<-phyloseq::ordinate(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion, "PCoA", "unifrac")## Warning in matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, : data
## length [8987] is not a sub-multiple or multiple of the number of rows [4494]ordination$values[1:2,]## Eigenvalues Relative_eig Broken_stick Cumul_eig Cumul_br_stick
## 1 2.776310 0.09857079 0.06535141 0.09857079 0.06535141
## 2 2.131222 0.07566743 0.05201808 0.17423822 0.11736948metadata_ordered<-metadata[row.names(ordination$vectors),,drop=FALSE]
all.equal(row.names(metadata_ordered), row.names(ordination$vectors))## [1] TRUEmetadata_ordered$Unifrac1<-ordination$vectors[,1]
metadata_ordered$Unifrac2<-ordination$vectors[,2]
#Let's plot the data
plot_MSM_NEG_UNSUP<-ggplot2::ggplot(data=metadata_ordered, aes(x=Unifrac1, y=Unifrac2))+geom_point(color="royalblue4", aes(alpha=hiv_phenotype), size=2, shape=16)+
theme_bw()+stat_ellipse(color="royalblue4", aes(alpha=hiv_phenotype), size=1)+scale_alpha_manual(values=c(1,0.3))+ylim(c(-0.55, 0.55))+xlim(c(-0.55,0.55))+
ggtitle("neg-unsuppressed MSM")+geom_point(data=metadata_ordered %>% group_by(hiv_phenotype) %>% summarise_at(vars(matches("UniFrac")), mean),size=5, color="royalblue4", aes(alpha=hiv_phenotype))
#Adonis (n=76)
ASV_table<-as.data.frame(phyloseq::otu_table(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion))
all.equal(row.names(ASV_table), row.names(metadata_ordered))## [1] TRUEunifrac.distance<-unname(phyloseq::UniFrac(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion, weighted = FALSE)) ### unname fixes error introduced by Desctools see https://github.com/joey711/phyloseq/issues/1457## Warning in matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, : data
## length [8987] is not a sub-multiple or multiple of the number of rows [4494]attributes(unifrac.distance)$Labels <- phyloseq::sample_names(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)
print(vegan::adonis2(unifrac.distance~metadata_ordered$hiv_phenotype, data=ASV_table, permutations=1000)) -> adon_hiv_phenotype_msm_neg_unsup## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## vegan::adonis2(formula = unifrac.distance ~ metadata_ordered$hiv_phenotype, data = ASV_table, permutations = 1000)
## Df SumOfSqs R2 F Pr(>F)
## metadata_ordered$hiv_phenotype 1 1.3672 0.04854 3.7753 0.000999 ***
## Residual 74 26.7985 0.95146
## Total 75 28.1657 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###*** r2 = 0.04854 p = 0.000999
# vegan::adonis2(formula = unifrac.distance ~ metadata_ordered$hiv_phenotype, data = ASV_table, permutations = 1000)
# Df SumOfSqs R2 F Pr(>F)
# metadata_ordered$hiv_phenotype 1 1.3672 0.04854 3.7753 0.000999 ***
# Residual 74 26.7985 0.95146
# Total 75 28.1657 1.00000
### COMPARISON MSM NEG-UNSUPPRESSED: Adonis: controlling for additional metadata
### Extra metadata that have full n: Race, Ethnicity, age, sex, tmp_smx_active
metadata_ordered$age <- as.numeric(metadata_ordered$age)
metadata_ordered$BMI <- as.numeric(metadata_ordered$BMI)
### control for metadata with full n
covars_full_n <- c("Ethnicity", "age", "Race", "tmp_smx_active", "hiv_phenotype")
print(vegan::adonis2(as.formula(paste("unifrac.distance~metadata_ordered$", paste(covars_full_n, collapse = "+metadata_ordered$"), sep = "")), data=ASV_table, permutations=1000)) -> adon_hiv_phenotype_msm_neg_unsup_full_n## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## vegan::adonis2(formula = as.formula(paste("unifrac.distance~metadata_ordered$", paste(covars_full_n, collapse = "+metadata_ordered$"), sep = "")), data = ASV_table, permutations = 1000)
## Df SumOfSqs R2 F Pr(>F)
## metadata_ordered$Ethnicity 1 0.3394 0.01205 0.9447 0.567433
## metadata_ordered$age 1 0.4764 0.01691 1.3260 0.064935 .
## metadata_ordered$Race 4 1.7162 0.06093 1.1942 0.025974 *
## metadata_ordered$tmp_smx_active 1 0.3408 0.01210 0.9487 0.574426
## metadata_ordered$hiv_phenotype 1 1.2212 0.04336 3.3990 0.000999 ***
## Residual 67 24.0716 0.85465
## Total 75 28.1657 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###*** hiv_phenotype r2 = 0.04336 p = 0.000999
# Df SumOfSqs R2 F Pr(>F)
# metadata_ordered$Ethnicity 1 0.3394 0.01205 0.9447 0.567433
# metadata_ordered$age 1 0.4764 0.01691 1.3260 0.064935 .
# metadata_ordered$Race 4 1.7162 0.06093 1.1942 0.025974 *
# metadata_ordered$tmp_smx_active 1 0.3408 0.01210 0.9487 0.574426
# metadata_ordered$hiv_phenotype 1 1.2212 0.04336 3.3990 0.000999 ***
# Residual 67 24.0716 0.85465
# Total 75 28.1657 1.00000
### COMPARISON MSM NEG-UNSUPPRESSED: Adonis: controlling for additional metadata - removing samples w/o values - BMI/comorbidities
### remove samples with BMI/comorbidities is NA (n=76 -> n=65)
ps_gg_fp_f_prevalence_filter_2019_05_26_proportion_bc <- ps_gg_fp_f_prevalence_filter_2019_05_26_proportion
metadata_ordered_bc <- metadata_ordered[!is.na(metadata_ordered$hld_hx) &!is.na(metadata_ordered$BMI),,drop=FALSE]
sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion_bc) <- metadata_ordered_bc## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'### subset ASV table and distance matrix
ASV_table_bc<-as.data.frame(otu_table(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion_bc))
all.equal(row.names(ASV_table_bc), row.names(metadata_ordered_bc))## [1] TRUEunifrac.distance_bc <- unname(usedist::dist_subset(unifrac.distance, sample_names(ps_gg_fp_f_prevalence_filter_2019_05_26_proportion_bc))) ### unname fixes error introduced by Desctools see https://github.com/joey711/phyloseq/issues/1457
### run adonis
covars_bc <- c("BMI", "hld_hx", "htn_hx", "cvd_hx", "dm2hx_dx", "hldhx_dx", "cvdhx_dx", covars_full_n)
print(vegan::adonis2(as.formula(paste("unifrac.distance_bc~metadata_ordered_bc$", paste(covars_bc, collapse = "+metadata_ordered_bc$"), sep = "")), data=ASV_table_bc, permutations=1000)) -> adon_hiv_phenotype_msm_neg_unsup_full_n_bc## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## vegan::adonis2(formula = as.formula(paste("unifrac.distance_bc~metadata_ordered_bc$", paste(covars_bc, collapse = "+metadata_ordered_bc$"), sep = "")), data = ASV_table_bc, permutations = 1000)
## Df SumOfSqs R2 F Pr(>F)
## metadata_ordered_bc$BMI 1 0.4485 0.01880 1.2756 0.085914 .
## metadata_ordered_bc$hld_hx 1 0.4089 0.01714 1.1629 0.165834
## metadata_ordered_bc$htn_hx 1 0.3636 0.01524 1.0341 0.347652
## metadata_ordered_bc$cvd_hx 1 0.4794 0.02009 1.3634 0.041958 *
## metadata_ordered_bc$dm2hx_dx 1 0.3553 0.01489 1.0107 0.405594
## metadata_ordered_bc$Ethnicity 1 0.2665 0.01117 0.7579 0.960040
## metadata_ordered_bc$age 1 0.4674 0.01959 1.3294 0.043956 *
## metadata_ordered_bc$Race 4 1.7139 0.07183 1.2187 0.023976 *
## metadata_ordered_bc$tmp_smx_active 1 0.3446 0.01444 0.9801 0.507493
## metadata_ordered_bc$hiv_phenotype 1 1.0810 0.04531 3.0748 0.000999 ***
## Residual 51 17.9309 0.75151
## Total 64 23.8600 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###*** hiv_phenotype r2 = 0.04531 p = 0.000999
# Df SumOfSqs R2 F Pr(>F)
# metadata_ordered_bc$BMI 1 0.4485 0.01880 1.2756 0.067932 .
# metadata_ordered_bc$hld_hx 1 0.4089 0.01714 1.1629 0.150849
# metadata_ordered_bc$htn_hx 1 0.3636 0.01524 1.0341 0.368631
# metadata_ordered_bc$cvd_hx 1 0.4794 0.02009 1.3634 0.036963 *
# metadata_ordered_bc$dm2hx_dx 1 0.3553 0.01489 1.0107 0.371628
# metadata_ordered_bc$Ethnicity 1 0.2665 0.01117 0.7579 0.967033
# metadata_ordered_bc$age 1 0.4674 0.01959 1.3294 0.045954 *
# metadata_ordered_bc$Race 4 1.7139 0.07183 1.2187 0.007992 **
# metadata_ordered_bc$tmp_smx_active 1 0.3446 0.01444 0.9801 0.477522
# metadata_ordered_bc$hiv_phenotype 1 1.0810 0.04531 3.0748 0.000999 ***
# Residual 51 17.9309 0.75151
# Total 64 23.8600 1.00000
#Let's plot all 4 PCoA
ggsave("Figure3A_v1.pdf", grid.arrange(plot_noMSM_NEG_ART, plot_noMSM_NEG_UNSUP, plot_MSM_NEG_ART, plot_MSM_NEG_UNSUP,ncol=2, nrow=2),
width=15, height=10)
#--------------------------------------------------------------------------------------------------------------#Figure 3C: Figure 3B uses agricolae which conflicts with ancom, so Figure 3C is run first
#FIGURE 3C: Figure 3B uses agricolae which conflicts with ancom, so Figure 3C is run first
#--------------------------------------------------------------------------------------------------------------
###RUN ANCOM MSM NEG vs UNTREATED###
#Transform count data in the phyloseq object
ps_gg_fp_f_prevalence_filter_2019_05_26_proportion <- phyloseq::transform_sample_counts(ps_gg_fp_f_prevalence_filter_2019_05_26, function(x)(x/sum(x)))## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Select samples of interest and update phyloseq object
dataset<-ps_gg_fp_f_prevalence_filter_2019_05_26
metadata<-as.data.frame(sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26))
metadata<-metadata[metadata$hiv_phenotype %in% c("1_hiv_negative", "4_unsuppressed"), , drop=F]
metadata<-as.data.frame(as.matrix(metadata[metadata$sexual_orientation == "MSM" | is.na(metadata$sexual_orientation), , drop=F]))
metadata_boston<-metadata[metadata$sample_cohort == "boston", , drop=F]
sample_data(dataset)<-metadata_boston## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'ps.taxa.sub <- phyloseq::prune_taxa(taxa_sums(dataset) > 0, dataset)## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'out <- ANCOMBC::ancombc2(data = ps.taxa.sub, assay_name = "counts", tax_level = NULL, fix_formula = "hiv_phenotype",
p_adj_method = "BH", pseudo = 0, pseudo_sens = FALSE, prv_cut = 0.05, lib_cut = 1000,
group = "hiv_phenotype", struc_zero = TRUE, neg_lb = FALSE, alpha = 0.05, n_cl = 6, global = TRUE,
em_control = list(tol = 1e-05, max_iter = 100), mdfdr_control = list(fwer_ctrl_method = "holm", B = 100))## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## `tax_level` is not speficified
## No agglomeration will be performed
## Otherwise, please speficy `tax_level` by one of the following:
## Kingdom, Phylum, Class, Order, Family, Genus, Species## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Warning: The group variable has < 3 categories
## The multi-group comparisons (global/pairwise/dunnet/trend) will be deactivatedres_df <- out$res
res_df <- dplyr::rename(res_df, rowname = taxon)
colnames(res_df) <- stringr::str_replace(colnames(res_df), "hiv_phenotype.+", "hiv_phenotype")
res_df_taxa <- dplyr::left_join(res_df, tibble::rownames_to_column(as.data.frame(phyloseq::tax_table(ps.taxa.sub))), by = "rowname")
res_df_taxa[["index_num"]] <- 1:nrow(res_df_taxa)
res_df_taxa[["cohort"]] <- "boston"
res_df_taxa[["cohort_2"]] <- "boston_msm_neg_untreat"
res_df_taxa[["method"]] <- "ancom"
res_df_taxa <- tidyr::unite(res_df_taxa, col = "Genus_Species", Genus, Species, index_num, remove = FALSE)
alpha = 0.05
taxa_sig <- dplyr::filter(res_df_taxa, q_hiv_phenotype < 0.05)
taxa_sig$Genus_Species <- forcats::fct_reorder(taxa_sig$Genus_Species, taxa_sig$lfc_hiv_phenotype, min)
taxa_sig$rowname_short <- stringr::str_sub(taxa_sig$rowname, 1, 4)
ps.taxa.rel.sig <- phyloseq::prune_taxa(taxa_sig[["rowname"]], ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'# Only keep filtered samples
ps.taxa.rel.sig <- phyloseq::prune_samples(rownames(phyloseq::otu_table(ps.taxa.sub)), ps.taxa.rel.sig)
sigtab_dataset_neg_untreat <- taxa_sig
write.csv(sigtab_dataset_neg_untreat, "ANCOM_MSM_NEG_UNTREATED.csv")
ggsave("Figure3C_NEG_UNTREATANCOM_v5.pdf", ggplot(data = sigtab_dataset_neg_untreat, aes(x = Genus_Species, y = lfc_hiv_phenotype)) + theme_bw() + coord_flip() + geom_bar(stat="identity", aes(fill = Genus)) + ggtitle("MSM NEG-UNTREAT") + theme(legend.position = "bottom"), width = 10, height = 10, units = "in", dpi = 300)
###RUN ANCOM MSM NEG vs ART###
#Transform count data in the phyloseq object
ps_gg_fp_f_prevalence_filter_2019_05_26_proportion<-transform_sample_counts(ps_gg_fp_f_prevalence_filter_2019_05_26, function(x)(x/sum(x)))## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'#Select samples of interest and update phyloseq object
dataset<-ps_gg_fp_f_prevalence_filter_2019_05_26
metadata<-as.data.frame(sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26))
metadata<-metadata[metadata$hiv_phenotype %in% c("1_hiv_negative", "2_suppressed"), , drop=F]
metadata<-as.data.frame(as.matrix(metadata[metadata$sexual_orientation == "MSM" | is.na(metadata$sexual_orientation), , drop=F]))
metadata_boston<-metadata[metadata$sample_cohort=="boston", , drop=F]
sample_data(dataset)<-metadata_boston## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'ps.taxa.sub <- phyloseq::prune_taxa(taxa_sums(dataset) > 0, dataset)## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'out <- ANCOMBC::ancombc2(data = ps.taxa.sub, assay_name = "counts", tax_level = NULL, fix_formula = "hiv_phenotype",
p_adj_method = "BH", pseudo = 0, pseudo_sens = FALSE, prv_cut = 0.05, lib_cut = 1000,
group = "hiv_phenotype", struc_zero = TRUE, neg_lb = FALSE, alpha = 0.05, n_cl = 6, global = TRUE,
em_control = list(tol = 1e-05, max_iter = 100), mdfdr_control = list(fwer_ctrl_method = "holm", B = 100))## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## `tax_level` is not speficified
## No agglomeration will be performed
## Otherwise, please speficy `tax_level` by one of the following:
## Kingdom, Phylum, Class, Order, Family, Genus, Species## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Warning: The group variable has < 3 categories
## The multi-group comparisons (global/pairwise/dunnet/trend) will be deactivatedres_df <- out$res
res_df <- dplyr::rename(res_df, rowname = taxon)
colnames(res_df) <- stringr::str_replace(colnames(res_df), "hiv_phenotype.+", "hiv_phenotype")
res_df_taxa <- dplyr::left_join(res_df, tibble::rownames_to_column(as.data.frame(phyloseq::tax_table(ps.taxa.sub))), by = "rowname")
res_df_taxa[["index_num"]] <- 1:nrow(res_df_taxa)
res_df_taxa[["cohort"]] <- "boston"
res_df_taxa[["cohort_2"]] <- "boston_msm_neg_art"
res_df_taxa[["method"]] <- "ancom"
res_df_taxa <- tidyr::unite(res_df_taxa, col = "Genus_Species", Genus, Species, index_num, remove = FALSE)
alpha = 0.05
taxa_sig <- dplyr::filter(res_df_taxa, q_hiv_phenotype < 0.05)
taxa_sig$Genus_Species <- forcats::fct_reorder(taxa_sig$Genus_Species, taxa_sig$lfc_hiv_phenotype, min)
taxa_sig$rowname_short <- stringr::str_sub(taxa_sig$rowname, 1, 4)
ps.taxa.rel.sig <- phyloseq::prune_taxa(taxa_sig[["rowname"]], ps_gg_fp_f_prevalence_filter_2019_05_26_proportion)## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'
## Also defined by 'tidytree'# Only keep filtered samples
ps.taxa.rel.sig <- phyloseq::prune_samples(rownames(phyloseq::otu_table(ps.taxa.sub)), ps.taxa.rel.sig)
sigtab_dataset_neg_art <- taxa_sig
write.csv(sigtab_dataset_neg_art, "ANCOM_MSM_NEG_ART.csv")
ggsave("Figure3C_NEG_ARTANCOM_v5.pdf", ggplot(data = sigtab_dataset_neg_art, aes(x = Genus_Species, y = lfc_hiv_phenotype)) + theme_bw() + coord_flip() + geom_bar(stat="identity", aes(fill = Genus)) + ggtitle("MSM NEG-ART") + theme(legend.position = "bottom"), width = 10, height = 10, units = "in", dpi = 300)
#Merge results from both sample cohorts:
sigtab_dataset<-rbind(sigtab_dataset_neg_art, sigtab_dataset_neg_untreat)
sigtab_dataset[["index_num_all"]] <- 1:nrow(sigtab_dataset)
sigtab_dataset <- tidyr::unite(sigtab_dataset, col = "Genus_Species_all", Genus, Species, index_num_all, sep = "_", remove = FALSE)
sigtab_dataset <- tidyr::unite(sigtab_dataset, col = "Genus_Species_all_for_color", Genus, Species, sep = " ", remove = FALSE)
# Remove extra brackets around taxonomic name for labels
sigtab_dataset$Genus_label <- stringr::str_replace(sigtab_dataset$Genus, "^\\[([^\\]]+)\\]", "\\1")
sigtab_dataset$Species_label <- stringr::str_replace(sigtab_dataset$Species, "^\\[([^\\]]+)\\]", "\\1")
sigtab_dataset %>% dplyr::mutate(Genus_species_label =
dplyr::case_when(is.na(stringr::str_extract(Genus_label, "\\[")) & is.na(stringr::str_extract(Species_label, "\\[")) ~ paste(Genus_label,Species_label),
TRUE ~ paste(Genus_label,"sp"))) -> sigtab_dataset
sigtab_dataset$Genus_species_label <- stringr::str_replace(sigtab_dataset$Genus_species_label, "\\[([^\\]]+)\\]", "")
sigtab_dataset$Genus_species_label <- stringr::str_replace(sigtab_dataset$Genus_species_label, "\\ +", " ")
sigtab_dataset<-sigtab_dataset[order(sigtab_dataset$lfc_hiv_phenotype, decreasing = FALSE),,drop=FALSE]
sigtab_dataset$Genus_Species_all<-factor(sigtab_dataset$Genus_Species_all, levels = c(sigtab_dataset$Genus_Species_all))
# Make lookup table for Genus_species_label
Genus_species_label_lookup <- dplyr::distinct(sigtab_dataset, Genus_Species_all, .keep_all = TRUE)$Genus_species_label
names(Genus_species_label_lookup) <- dplyr::distinct(sigtab_dataset, Genus_Species_all, .keep_all = TRUE)$Genus_Species_all
#Load color dictionary and construct figure:
dictionary_unique <- read.csv("COLOR_DICTIONARY3.csv", sep=";")
sigtab_dataset$Genus_species_label <- factor(sigtab_dataset$Genus_species_label, levels = unique(sigtab_dataset$Genus_species_label))
dictionary_plot <- dictionary_unique[dictionary_unique$Genus_species_label%in%sigtab_dataset$Genus_species_label,,drop=F]
rownames(dictionary_plot)<-dictionary_plot$Genus_species_label
dictionary_plot<-dictionary_plot[as.character(unique(sigtab_dataset$Genus_species_label)),,drop=F]
sigtab_dataset$cohort_2 <- fct_relevel(as.factor(sigtab_dataset$cohort_2), "boston_msm_neg_untreat", "boston_msm_neg_art")
header_namer <- as_labeller(c(`boston_msm_neg_art` = "HIV-uninfected HIV+ ART-treated", `boston_msm_neg_untreat` = "HIV-uninfected HIV+ untreated"))
ggsave("Figure3C_v10.pdf",
ggplot(data = sigtab_dataset, aes(x = Genus_Species_all, y = lfc_hiv_phenotype)) +
geom_bar(stat = "identity", aes(fill = Genus_species_label)) +
scale_fill_manual(values = as.character(dictionary_plot$Color)) +
coord_flip() + scale_x_discrete(label = as_labeller(Genus_species_label_lookup)) + theme_bw() +
theme(legend.position = "bottom", axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 10, face = "plain"), axis.ticks.y = element_blank(),
panel.border = element_rect(linetype = "solid", fill = NA, linewidth = 1),
strip.background = element_rect(colour = "white", fill = "white"), strip.text = element_text(colour = "black", face = "bold", size = rel(1.2))) +
geom_hline(yintercept = 0) + ggtitle("MSM") + ylab("log2FoldChange") +
facet_wrap(vars(cohort_2), scales = "free", ncol = 3, labeller = labeller(cohort_2 = header_namer)), width = 15, height = 20, units = "in", dpi = 300)
#--------------------------------------------------------------------------------------------------------------#Figure 3B
#--------------------------------------------------------------------------------------------------------------
require("agricolae")## Loading required package: agricolae## Registered S3 methods overwritten by 'klaR':
## method from
## predict.rda vegan
## print.rda vegan
## plot.rda vegan#Prepare OTU table and taxonomy files out from the phyloseq object:
OTU_table<-as.data.frame(otu_table(ps_gg_fp_f_prevalence_filter_2019_05_26))
Taxonomy<-as.data.frame(tax_table(ps_gg_fp_f_prevalence_filter_2019_05_26))
metadata<-as.data.frame(sample_data(ps_gg_fp_f_prevalence_filter_2019_05_26))
#Find the sample with the smallest number of reads. It will be the value used to normalize all data so all samples will be comparable among them:
rowSums(OTU_table)## 105574.boston1.0139.2014.12.08 108777.boston1.0140.2014.12.08
## 72609 64328
## 112993.boston1.0141.2014.12.08 123656.boston1.0005.2014.12.08
## 79920 36479
## 143200.boston1.0006.2014.12.08 153724.boston1.0007.2014.12.08
## 81427 85980
## 165642.boston1.0008.2014.12.08 194317.boston1.0010.2014.12.08
## 98119 53263
## 196203.boston1.0011.2014.12.08 205120.boston1.0013.2014.12.08
## 36651 59443
## 211774.boston1.0014.2014.12.08 228437.boston1.0017.2014.12.08
## 57586 52936
## 229969.boston1.0018.2014.12.08 237983.boston1.0019.2014.12.08
## 62357 37574
## 258085.boston1.0142.2014.12.08 273479.boston1.0143.2014.12.08
## 59728 102691
## 315504.boston1.0028.2014.12.08 330183.boston1.0144.2014.12.08
## 21399 68760
## 337016.boston1.0030.2014.12.08 365685.boston1.0032.2014.12.08
## 43655 41183
## 386576.boston1.0035.2014.12.08 389876.boston1.0036.2014.12.08
## 36454 66739
## 410644.boston1.0125.2014.12.08 410932.boston1.0039.2014.12.08
## 73269 27289
## 413736.boston1.0126.2014.12.08 427838.boston1.0127.2014.12.08
## 39912 66847
## 453548.boston1.0045.2014.12.08 460380.boston1.0046.2014.12.08
## 34382 31329
## 473516.boston1.0047.2014.12.08 479693.boston1.0048.2014.12.08
## 46039 18765
## 485548.boston1.0049.2014.12.08 498553.boston1.0050.2014.12.08
## 35582 56405
## 505402.boston1.0051.2014.12.08 516980.boston1.0130.2014.12.08
## 59815 14678
## 522458.boston1.0132.2014.12.08 526318.boston1.0133.2014.12.08
## 68609 27811
## 527968.boston1.0057.2014.12.08 529516.boston1.0136.2014.12.08
## 26204 20681
## 533586.boston1.0059.2014.12.08 534694.boston1.0060.2014.12.08
## 57983 51661
## 604772.boston1.0071.2014.12.08 614225.boston1.0073.2014.12.08
## 52199 47445
## 615167.boston1.0074.2014.12.08 616147.boston1.0075.2014.12.08
## 37085 50711
## 653425.boston1.0077.2014.12.08 666207.boston1.0079.2014.12.08
## 42115 70102
## 694413.boston1.0080.2014.12.08 708968.boston1.0083.2014.12.08
## 44825 28721
## 734962.boston1.0085.2014.12.08 745577.boston1.0086.2014.12.08
## 43153 75694
## 758572.boston1.0088.2014.12.08 775609.boston1.0091.2014.12.08
## 63237 64948
## 813341.boston1.0095.2014.12.08 819622.boston1.0096.2014.12.08
## 74295 34279
## 842279.boston1.0097.2014.12.08 847041.boston1.0098.2014.12.08
## 61932 59051
## 862898.boston1.0103.2014.12.08 874612.boston1.0105.2014.12.08
## 38913 21417
## 880160.boston1.0106.2014.12.08 899025.boston1.0107.2014.12.08
## 61338 51550
## 900158.boston1.0108.2014.12.08 911594.boston1.0109.2014.12.08
## 18175 54168
## 923358.boston1.0111.2014.12.08 950965.boston1.0116.2014.12.08
## 34979 66238
## 953586.boston1.0117.2014.12.08 958793.boston1.0118.2014.12.08
## 54430 66440
## 966971.boston1.0120.2014.12.08 970489.boston1.0121.2014.12.08
## 44109 48095
## 995725.boston1.0123.2014.12.08 529863.boston.0165.2017.04.06
## 58954 42388
## 608647.boston.0072.2017.04.06 686039.0040.0323.2017.04.06
## 31028 374932
## WT24922.0093.0468.2017.04.06 102438.0086.0363.2017.03.15
## 107819 25113
## 106085.0054.0333.2017.03.15 122897.0017.0307.2017.03.15
## 19135 80414
## 129226.0089.0367.2017.03.15 136376.0013.0302.2017.03.15
## 33236 27232
## 148342.0027.0314.2017.03.15 157072.0043.0244.2017.03.15
## 45438 22883
## 175067.0033.0231.2017.03.15 181090.0091.0369.2017.03.15
## 15936 22823
## 189326.0070.0343.2017.03.15 191447.0008.0296.2017.03.15
## 61451 62556
## 207295.0010.0298.2017.03.15 211578.0032.0230.2017.03.15
## 30235 23193
## 228516.0076.0350.2017.03.15 236532.0078.0379.2017.03.15
## 39086 66657
## 238426.0046.0247.2017.03.15 249768.0083.0359.2017.03.15
## 15578 65769
## 251073.0025.0221.2017.03.15 285803.0064.0338.2017.03.15
## 27143 63788
## 293340.0035.0233.2017.03.15 305385.0051.0330.2017.03.15
## 24885 20588
## 310817.0006.0293.2017.03.15 331904.0098.0430.2017.03.15
## 32927 35529
## 347964.0061.0336.2017.03.15 350103.0012.0301.2017.03.15
## 66932 34897
## 380272.0044.0245.2017.03.15 387879.0030.0228.2017.03.15
## 55368 51686
## 400609.0058.0262.2017.03.15 408044.0052.0331.2017.03.15
## 24415 57931
## 419034.0081.0382.2017.03.15 432158.0045.0246.2017.03.15
## 57230 37797
## 442916.0037.0319.2017.03.15 444991.0047.0327.2017.03.15
## 24253 17042
## 460929.0041.0324.2017.03.15 466105.0018.0377.2017.03.15
## 18796 48350
## 470588.0066.0340.2017.03.15 481066.boston.0168.2017.03.15
## 52619 14233
## 487268.0057.0261.2017.03.15 498229.0036.0318.2017.03.15
## 17799 38925
## 498554.0062.0337.2017.03.15 502743.0038.0320.2017.03.15
## 47910 29154
## 515591.0056.0334.2017.03.15 516035.0020.0310.2017.03.15
## 16026 41843
## 521471.0067.0341.2017.03.15 524541.0024.0313.2017.03.15
## 37753 34986
## 560575.0080.0381.2017.03.15 564855.0053.0332.2017.03.15
## 76936 32030
## 565723.0005.0292.2017.03.15 588311.0072.0346.2017.03.15
## 33565 61200
## 596527.0095.0374.2017.03.15 614631.0065.0339.2017.03.15
## 49031 23279
## 629358.0009.0297.2017.03.15 637837.0021.0312.2017.03.15
## 63706 33770
## 651433.0099.0431.2017.03.15 658217.0055.0258.2017.03.15
## 27060 34249
## 711750.0082.0383.2017.03.15 721154.0060.0335.2017.03.15
## 83786 16367
## 721729.0088.0366.2017.03.15 722188.0063.0268.2017.03.15
## 23928 26981
## 725896.0022.0218.2017.03.15 732425.0039.0378.2017.03.15
## 18124 48694
## 735345.0071.0345.2017.03.15 768392.0096.0375.2017.03.15
## 46984 41304
## 805457.0094.0373.2017.03.15 805641.0015.0305.2017.03.15
## 65857 38997
## 839338.0011.0300.2017.03.15 848334.0077.0351.2017.03.15
## 70395 45711
## 885614.0048.0328.2017.03.15 888751.0019.0309.2017.03.15
## 18203 47832
## 893231.0074.0348.2017.03.15 902901.0029.0226.2017.03.15
## 28255 18740
## 905350.0085.0362.2017.03.15 908782.0092.0371.2017.03.15
## 42388 28907
## 909824.boston.0174.2017.03.15 910641.0031.0316.2017.03.15
## 12820 38990
## 916034.0034.0232.2017.03.15 919901.0026.0222.2017.03.15
## 21405 19714
## 940622.0075.0349.2017.03.15 959714.0004.0291.2017.03.15
## 42914 30169
## 959734.0090.0368.2017.03.15 968359.0087.0365.2017.03.15
## 37955 33934
## 968902.0073.0347.2017.03.15 972684.0028.0224.2017.03.15
## 34542 22469
## 975240.0016.0306.2017.03.15 976183.0050.0329.2017.03.15
## 30867 31908
## 979196.0003.0290.2017.03.15 989517.0068.0274.2017.03.15
## 34682 45375
## WT09782.0159.0121.2017.02.01 WT15101.0174.0106.2017.02.01
## 52918 17604
## WT42336.0160.0122.2017.02.01 WT44778.0158.0120.2017.02.01
## 17747 80361
## XE17833.0092.0100.2017.02.01 XE22903.0043.0125.2017.02.01
## 10751 92243
## XE28163.0194.0127.2017.02.01 WM26348.0100.0113.2017.01.11
## 77437 52751
## WM26354.0139.0315.2017.01.11 WQ64001.0145.0143.2017.01.11
## 45678 39753
## WS20813.0155.0153.2017.01.11 WS21384.0094.0102.2017.01.11
## 29351 12873
## WS21401.0047.0283.2017.01.11 WS21556.0141.0317.2017.01.11
## 65243 57521
## WS21562.0055.0438.2017.01.11 WS21578.0125.0134.2017.01.11
## 13616 46331
## WS21584.0066.0321.2017.01.11 WS22205.0097.0110.2017.01.11
## 28249 65967
## WS74808.0165.0347.2017.01.11 WS74858.0098.0111.2017.01.11
## 32886 18675
## WS75418.0032.0279.2017.01.11 WS76117.0102.0115.2017.01.11
## 66488 78753
## WS76840.0117.0126.2017.01.11 WS77050.0026.0276.2017.01.11
## 39459 15102
## WT02695.0166.0104.2017.01.11 WT02712.0078.0323.2017.01.11
## 20141 38952
## WT05558.0190.0378.2017.01.11 WT05564.0116.0125.2017.01.11
## 17973 33463
## WT06263.0127.0135.2017.01.11 WT07439.0058.0440.2017.01.11
## 35147 43263
## WT08061.0090.0098.2017.01.11 WT09760.0053.0437.2017.01.11
## 28736 34666
## WT09798.0057.0285.2017.01.11 WT10373.0080.0325.2017.01.11
## 78499 37600
## WT10389.0036.0270.2017.01.11 WT10395.0035.0269.2017.01.11
## 29095 43853
## WT12565.0189.0377.2017.01.11 WT14129.0153.0151.2017.01.11
## 26219 58444
## WT14135.0123.0132.2017.01.11 WT14818.0111.0120.2017.01.11
## 42345 33448
## WT15084.0046.0282.2017.01.11 WT15117.0157.0155.2017.01.11
## 122971 41809
## WT15123.0164.0346.2017.01.11 WT23223.0144.0320.2017.01.11
## 37517 39399
## WT23273.0101.0114.2017.01.11 WT24900.0152.0150.2017.01.11
## 32170 25802
## WT24916.0044.0281.2017.01.11 WT24944.0124.0159.2017.01.11
## 74046 36504
## WT24950.0033.0267.2017.01.11 WT24966.0142.0318.2017.01.11
## 54801 37339
## WT27396.0121.0130.2017.01.11 WT27407.0156.0154.2017.01.11
## 30349 27423
## WT27441.0129.0137.2017.01.11 WT30335.0134.0140.2017.01.11
## 25366 76057
## WT30357.0049.0274.2017.01.11 WT30818.0107.0118.2017.01.11
## 35457 26163
## WT30824.0147.0145.2017.01.11 WT30830.0052.0275.2017.01.11
## 45066 41492
## WT34353.0120.0129.2017.01.11 WT40033.0133.0139.2017.01.11
## 40414 22514
## WT40049.0148.0146.2017.01.11 WT40083.0038.0271.2017.01.11
## 60368 23791
## WT40516.0122.0131.2017.01.11 WT41865.0089.0097.2017.01.11
## 69537 24316
## WT41910.0095.0108.2017.01.11 WT42069.0154.0152.2017.01.11
## 43202 25172
## WT43352.0087.0447.2017.01.11 WT43368.0118.0127.2017.01.11
## 15820 50208
## WT43374.0048.0273.2017.01.11 WT44245.0081.0337.2017.01.11
## 57836 54390
## WT44601.0082.0445.2017.01.11 WT44762.0130.0138.2017.01.11
## 41145 118478
## WT47297.0173.0105.2017.01.11 WT48435.0099.0112.2017.01.11
## 30740 39063
## WT48441.0112.0121.2017.01.11 WT48457.0060.0441.2017.01.11
## 31526 41539
## WT48491.0146.0144.2017.01.11 WT48502.0106.0117.2017.01.11
## 28055 63937
## WT50842.0187.0376.2017.01.11 WT50858.0128.0136.2017.01.11
## 20957 36154
## WY74094.0079.0324.2017.01.11 WY74105.0151.0149.2017.01.11
## 55072 67120
## WY74777.0073.0444.2017.01.11 WY74799.0176.0369.2017.01.11
## 45932 27435
## WY75915.0114.0123.2017.01.11 WY75959.0191.0379.2017.01.11
## 27184 28579
## WY75971.0062.0443.2017.01.11 WY76486.0162.0344.2017.01.11
## 49071 52240
## WY78078.0161.0343.2017.01.11 WY78084.0061.0442.2017.01.11
## 29088 15433
## WY78567.0119.0128.2017.01.11 WY79216.0040.0272.2017.01.11
## 21515 42024
## WY79222.0056.0439.2017.01.11 WY79266.0113.0157.2017.01.11
## 27261 55014
## WY79272.0177.0370.2017.01.11 WY80257.0027.0277.2017.01.11
## 31091 65700
## WY80324.0137.0287.2017.01.11 WY81156.0104.0116.2017.01.11
## 37886 41525
## WY81162.0169.0362.2017.01.11 WY81184.0045.0436.2017.01.11
## 65619 23675
## XE13926.0025.0214.2017.01.11 XE13948.0192.0380.2017.01.11
## 25666 43145
## XE13960.0150.0148.2017.01.11 XE15596.0149.0147.2017.01.11
## 34682 51049
## XE17300.0083.0339.2017.01.11 XE21060.0015.0266.2017.01.11
## 60567 39820
## XE21076.0182.0372.2017.01.11 XE21082.0186.0375.2017.01.11
## 37296 20236
## XE21098.0115.0158.2017.01.11 XE22892.0171.0364.2017.01.11
## 42113 50713
## XE22953.0096.0109.2017.01.11 XE27236.0054.0284.2017.01.11
## 52070 135285
## XE27414.0059.0286.2017.01.11 XE28202.0028.0278.2017.01.11
## 51840 44456
## XE29167.0135.0141.2017.01.11 XE30225.0020.0212.2017.01.11
## 64259 24576
## XE31552.0011.0211.2017.01.11 XE33411.0143.0319.2017.01.11
## 20444 38078
## XE36257.0175.0156.2017.01.11 XE36952.0091.0099.2017.01.11
## 48972 11715
## XE36996.0009.0210.2017.01.11 XE38944.0167.0360.2017.01.11
## 38916 26375
## XE38950.0140.0316.2017.01.11 XE38966.0034.0268.2017.01.11
## 38755 55702
## XE38972.0178.0371.2017.01.11 XE38988.0110.0119.2017.01.11
## 35384 32939
## XE38994.0185.0374.2017.01.11 XE40684.0024.0213.2017.01.11
## 21707 25565
## XE40690.0172.0365.2017.01.11 XE40701.0163.0103.2017.01.11
## 46970 46782
## XE40717.0010.0265.2017.01.11 XE40745.0006.0264.2017.01.11
## 44631 59807
## XE40751.0088.0096.2017.01.11 XE41305.0193.0381.2017.01.11
## 17438 27692
## XE41311.0138.0314.2017.01.11 XE41327.0085.0446.2017.01.11
## 63493 13408
## XE41333.0136.0142.2017.01.11 XE41349.0168.0361.2017.01.11
## 44701 26835
## XE41561.0037.0280.2017.01.11 XE41577.0170.0363.2017.01.11
## 44727 31898
## XE41583.0183.0373.2017.01.11 WS19294.0064.0471.2016.11.13
## 26328 58457
## WS20829.0126.0349.2016.11.13 WS20835.0007.0390.2016.11.13
## 136145 35250
## WS21390.0076.0299.2016.11.13 WS21540.0031.0414.2016.11.13
## 144074 52735
## WT02689.0077.0300.2016.11.13 WT02728.0109.0332.2016.11.13
## 70581 83182
## WT04693.0016.0246.2016.11.13 WT04704.0071.0294.2016.11.13
## 60837 133384
## WT07417.0184.0222.2016.11.13 WT08055.0075.0298.2016.11.13
## 122883 116845
## WT09332.0070.0293.2016.11.13 WT09776.0074.0297.2016.11.13
## 159041 156778
## WT10406.0003.0386.2016.11.13 WT11111.0023.0406.2016.11.13
## 24724 196412
## WT12559.0072.0295.2016.11.13 WT14141.0002.0385.2016.11.13
## 192888 57645
## WT15090.0180.0218.2016.11.13 WT23295.0022.0251.2016.11.13
## 106854 68044
## WT27518.0050.0457.2016.11.13 WT27607.0039.0422.2016.11.13
## 32576 90830
## WT30341.0012.0243.2016.11.13 WT30868.0042.0449.2016.11.13
## 55241 74533
## WT30880.0069.0292.2016.11.13 WT34347.0181.0219.2016.11.13
## 81481 103299
## WT37711.0068.0291.2016.11.13 WT42192.0131.0354.2016.11.13
## 146323 52000
## WT47308.0004.0387.2016.11.13 WY76492.0067.0290.2016.11.13
## 54624 132789
## WY78062.0019.0248.2016.11.13 WY79238.0108.0331.2016.11.13
## 67007 37816
## WY79244.0105.0328.2016.11.13 WY80318.0103.0326.2016.11.13
## 16417 53131
## WY82116.0188.0226.2016.11.13 XE15574.0065.0288.2016.11.13
## 79589 203487
## XE17922.0179.0217.2016.11.13 XE17938.0014.0245.2016.11.13
## 93388 67436
## XE18326.0084.0307.2016.11.13 XE21109.0018.0247.2016.11.13
## 81864 105606
## XE28157.0013.0244.2016.11.13 XE28274.0051.0458.2016.11.13
## 94532 24318
## XE29173.0008.0240.2016.11.13 XE29812.0132.0355.2016.11.13
## 82688 143895
## XE33372.0021.0427.2016.11.13 XE36174.0063.0470.2016.11.13
## 74927 92285
## XE37001.0030.0413.2016.11.13 XE39009.0029.0412.2016.11.13
## 80217 48882
## XE39532.0017.0400.2016.11.13 XE39554.0041.0448.2016.11.13
## 205766 65169
## XE40723.0001.0384.2016.11.13 XE40739.0086.0309.2016.11.13
## 61118 34672
## XE41599.0005.0388.2016.11.13 MBA4060.0077.0266.2016.03.20
## 56531 11517
## MBA1007.0092.0281.2016.03.11 MBA1037.0129.0437.2016.03.11
## 16176 14385
## MBA1083.0088.0367.2016.03.11 MBA1141.0108.0297.2016.03.11
## 11423 11454
## MBA1166.0047.0236.2016.03.11 MBA1172.0017.0444.2016.03.11
## 11848 11809
## MBA1261.0125.0433.2016.03.11 MBA1327.0122.0368.2016.03.11
## 13925 20343
## MBA1447.0019.0469.2016.03.11 MBA4044.0024.0470.2016.03.11
## 45329 42889
## MBA4049.0043.0416.2016.03.11 MBA4051.0001.0446.2016.03.11
## 13037 12351
## MBA4056.0119.0308.2016.03.11 MBA4065.0152.0341.2016.03.11
## 12105 12002
## MBA4077.0040.0378.2016.03.11 MBA4085.0075.0264.2016.03.11
## 27897 14923
## MBA4088.0082.0271.2016.03.11 MBA4091.0085.0429.2016.03.11
## 9947 14136
## MBA4120.0076.0265.2016.03.11 MBA4121.0046.0235.2016.03.11
## 13922 12858
## MBA4129.0087.0468.2016.03.11 MBA4130.0148.0337.2016.03.11
## 40274 16263
## MBA4134.0120.0363.2016.03.11 MBA4139.0164.0353.2016.03.11
## 25631 11278
## 233202.Boston.0164.2016.02.14 930024.Boston.0114.2016.02.14
## 24841 27023
## MBA1003.0131.0320.2016.02.14 MBA1021.0035.0460.2016.02.14
## 49506 21557
## MBA1030.0014.0205.2016.02.14 MBA1033.0055.0244.2016.02.14
## 60325 23191
## MBA1035.0102.0426.2016.02.14 MBA1041.0053.0242.2016.02.14
## 40213 14230
## MBA1052.0009.0451.2016.02.14 MBA1071.0011.0202.2016.02.14
## 14859 82865
## MBA1074.0058.0247.2016.02.14 MBA1082.0023.0463.2016.02.14
## 13002 28556
## MBA1084.0114.0303.2016.02.14 MBA1090.0003.0194.2016.02.14
## 11543 133259
## MBA1095.0090.0279.2016.02.14 MBA1096.0060.0249.2016.02.14
## 23430 19672
## MBA1099.0066.0255.2016.02.14 MBA1100.0027.0386.2016.02.14
## 19574 26058
## MBA1101.0149.0338.2016.02.14 MBA1103.0026.0385.2016.02.14
## 19728 24858
## MBA1111.0139.0328.2016.02.14 MBA1133.0041.0453.2016.02.14
## 17707 23140
## MBA1135.0140.0329.2016.02.14 MBA1139.0018.0445.2016.02.14
## 9599 13882
## MBA1143.0105.0294.2016.02.14 MBA1151.0107.0296.2016.02.14
## 19881 13159
## MBA1159.0165.0354.2016.02.14 MBA1163.0167.0356.2016.02.14
## 30857 33492
## MBA1181.0126.0315.2016.02.14 MBA1187.0062.0251.2016.02.14
## 43998 15695
## MBA1190.0048.0237.2016.02.14 MBA1193.0132.0321.2016.02.14
## 53414 15292
## MBA1199.0069.0258.2016.02.14 MBA1202.0094.0456.2016.02.14
## 21096 14588
## MBA1209.0101.0290.2016.02.14 MBA1211.0136.0325.2016.02.14
## 10734 20052
## MBA1218.0030.0425.2016.02.14 MBA1230.0093.0282.2016.02.14
## 19204 19109
## MBA1237.0170.0359.2016.02.14 MBA1241.0022.0419.2016.02.14
## 25519 30836
## MBA1248.0070.0259.2016.02.14 MBA1251.0033.0467.2016.02.14
## 16468 26599
## MBA1260.0158.0347.2016.02.14 MBA1267.0038.0227.2016.02.14
## 39840 17977
## MBA1270.0042.0231.2016.02.14 MBA1271.0116.0462.2016.02.14
## 16729 22420
## MBA1279.0162.0461.2016.02.14 MBA1307.0029.0372.2016.02.14
## 48196 13686
## MBA1312.0074.0263.2016.02.14 MBA1317.0067.0256.2016.02.14
## 16407 16233
## MBA1330.0050.0239.2016.02.14 MBA1341.0160.0349.2016.02.14
## 12379 21517
## MBA1344.0169.0358.2016.02.14 MBA1355.0037.0458.2016.02.14
## 38110 38263
## MBA1363.0123.0312.2016.02.14 MBA1365.0171.0360.2016.02.14
## 11750 19399
## MBA1370.0034.0465.2016.02.14 MBA1375.0061.0250.2016.02.14
## 32525 24576
## MBA1382.0159.0348.2016.02.14 MBA1385.0124.0313.2016.02.14
## 23025 37460
## MBA1392.0134.0323.2016.02.14 MBA1399.0147.0336.2016.02.14
## 41620 26657
## MBA1410.0130.0379.2016.02.14 MBA1437.0063.0252.2016.02.14
## 19712 13941
## MBA1460.0106.0295.2016.02.14 MBA1480.0166.0355.2016.02.14
## 9819 34300
## MBA1486.0115.0304.2016.02.14 MBA1488.0072.0452.2016.02.14
## 22751 13626
## MBA1511.0163.0352.2016.02.14 MBA1515.0153.0342.2016.02.14
## 21490 24838
## MBA4041.0044.0449.2016.02.14 MBA4043.0059.0248.2016.02.14
## 10254 13060
## MBA4045.0157.0346.2016.02.14 MBA4047.0012.0203.2016.02.14
## 33741 48792
## MBA4050.0028.0434.2016.02.14 MBA4052.0118.0457.2016.02.14
## 14519 35830
## MBA4053.0057.0246.2016.02.14 MBA4057.0133.0322.2016.02.14
## 25496 9954
## MBA4061.0010.0201.2016.02.14 MBA4062.0079.0393.2016.02.14
## 104643 11876
## MBA4063.0100.0289.2016.02.14 MBA4066.0142.0430.2016.02.14
## 16644 12023
## MBA4067.0104.0293.2016.02.14 MBA4068.0007.0198.2016.02.14
## 22257 49188
## MBA4069.0004.0195.2016.02.14 MBA4070.0086.0478.2016.02.14
## 90587 41192
## MBA4072.0161.0350.2016.02.14 MBA4074.0078.0267.2016.02.14
## 27345 13679
## MBA4076.0016.0207.2016.02.14 MBA4078.0151.0340.2016.02.14
## 47724 35591
## MBA4080.0065.0254.2016.02.14 MBA4081.0052.0241.2016.02.14
## 23350 20586
## MBA4082.0032.0221.2016.02.14 MBA4086.0110.0299.2016.02.14
## 15687 18709
## MBA4087.0155.0344.2016.02.14 MBA4089.0141.0330.2016.02.14
## 35300 21650
## MBA4092.0008.0199.2016.02.14 MBA4095.0002.0193.2016.02.14
## 69046 138863
## MBA4096.0145.0334.2016.02.14 MBA4102.0154.0343.2016.02.14
## 18861 23941
## MBA4103.0128.0317.2016.02.14 MBA4106.0168.0357.2016.02.14
## 13480 33202
## MBA4107.0138.0327.2016.02.14 MBA4108.0099.0288.2016.02.14
## 12705 14653
## MBA4109.0073.0262.2016.02.14 MBA4111.0036.0459.2016.02.14
## 10720 38086
## MBA4112.0005.0196.2016.02.14 MBA4113.0137.0396.2016.02.14
## 59252 13144
## MBA4114.0064.0253.2016.02.14 MBA4115.0143.0332.2016.02.14
## 13795 9242
## MBA4118.0031.0466.2016.02.14 MBA4119.0127.0316.2016.02.14
## 42048 13338
## MBA4122.0112.0301.2016.02.14 MBA4123.0056.0245.2016.02.14
## 28583 21135
## MBA4126.0150.0339.2016.02.14 MBA4127.0021.0417.2016.02.14
## 8596 18287
## MBA4131.0006.0197.2016.02.14 MBA4132.0051.0240.2016.02.14
## 121717 12687
## MBA4133.0135.0324.2016.02.14 MBA4136.0020.0464.2016.02.14
## 18548 26125
## MBA4138.0068.0257.2016.02.14 MBA4140.0015.0206.2016.02.14
## 21282 32717
## 226855.boston.0016.2015.11.25 229075.boston.0178.2015.11.25
## 12316 21754
## 447537.boston.0170.2015.11.25 503564.boston.0169.2015.11.25
## 11055 16113
## 561130.boston.0065.2015.11.25 588800.boston.0068.2015.11.25
## 64533 54160
## 629356.boston.0076.2015.11.25 765828.boston.0089.2015.11.25
## 17564 18760
## 772512.boston.0090.2015.11.25 826391.boston.0177.2015.11.25
## 28599 29717
## 849016.boston.0176.2015.11.25 872569.boston.0179.2015.11.25
## 27270 22343
## 136109.048.0334.2018.12.15 137787.006.0293.2018.12.15
## 142042 110949
## 186400.027.0314.2018.12.15 207722.029.0316.2018.12.15
## 130465 140162
## 221355.060.0345.2018.12.15 225794.001.0288.2018.12.15
## 131800 169725
## 235185.033.0320.2018.12.15 236512.015.0302.2018.12.15
## 146986 136583
## 240884.013.0300.2018.12.15 241367.042.0329.2018.12.15
## 134087 130533
## 243736.036.0323.2018.12.15 276693.051.0337.2018.12.15
## 125215 143542
## 282036.026.0313.2018.12.15 298053.032.0319.2018.12.15
## 140377 139377
## 319820.035.0322.2018.12.15 354109.012.0299.2018.12.15
## 156031 130651
## 356403.053.0339.2018.12.15 364485.002.0289.2018.12.15
## 163928 138715
## 373167.021.0308.2018.12.15 395379.010.0297.2018.12.15
## 91803 125471
## 402235.052.0338.2018.12.15 411794.028.0315.2018.12.15
## 103407 125418
## 416151.055.0340.2018.12.15 420641.004.0291.2018.12.15
## 147234 137640
## 423443.005.0292.2018.12.15 445474.025.0312.2018.12.15
## 153787 162503
## 453551.040.0327.2018.12.15 459452.044.0331.2018.12.15
## 149435 105418
## 467375.007.0294.2018.12.15 471179.003.0290.2018.12.15
## 148513 139922
## 492275.058.0343.2018.12.15 570298.041.0328.2018.12.15
## 165014 135343
## 574541.045.0332.2018.12.15 576662.030.0317.2018.12.15
## 124317 151416
## 578598.017.0304.2018.12.15 580176.038.0325.2018.12.15
## 121232 141927
## 588443.031.0318.2018.12.15 588873.064.0349.2018.12.15
## 94483 109394
## 612872.019.0306.2018.12.15 667045.043.0330.2018.12.15
## 139365 116480
## 675294.009.0296.2018.12.15 684908.056.0341.2018.12.15
## 138860 122542
## 714983.057.0342.2018.12.15 720750.034.0321.2018.12.15
## 145724 121774
## 774051.062.0347.2018.12.15 821034.039.0326.2018.12.15
## 147680 119747
## 822655.037.0324.2018.12.15 823946.047.0333.2018.12.15
## 93713 121833
## 850240.022.0309.2018.12.15 851204.014.0301.2018.12.15
## 107960 145594
## 853781.063.0348.2018.12.15 857829.018.0305.2018.12.15
## 130937 136894
## 883092.049.0335.2018.12.15 909074.024.0311.2018.12.15
## 126602 83194
## 918320.061.0346.2018.12.15 938150.059.0344.2018.12.15
## 150659 159642
## 945496.008.0295.2018.12.15 948983.050.0336.2018.12.15
## 130406 160584
## 982757.020.0307.2018.12.15 985170.011.0298.2018.12.15
## 135822 132462
## MBA1008.062.0371.2018.12.15 MBA1073.103.0380.2018.12.15
## 97776 121454
## MBA1144.144.0244.2018.12.15 MBA1215.004.0359.2018.12.15
## 58436 141749
## MBA1226.010.0360.2018.12.15 MBA1240.156.0246.2018.12.15
## 118025 123306
## MBA1284.025.0362.2018.12.15 MBA1302.089.0375.2018.12.15
## 106064 122557
## MBA1315.035.0365.2018.12.15 MBA1398.080.0374.2018.12.15
## 122382 124340
## MBA1491.070.0372.2018.12.15 MBA1509.039.0366.2018.12.15
## 141649 123099
## MBA1519.096.0377.2018.12.15 MBA4042.011.0361.2018.12.15
## 105745 135959
## MBA4046.032.0363.2018.12.15 MBA4048.146.0245.2018.12.15
## 121271 104141
## MBA4058.049.0368.2018.12.15 MBA4059.113.0242.2018.12.15
## 163357 154949
## MBA4064.117.0243.2018.12.15 MBA4071.098.0379.2018.12.15
## 99265 173599
## MBA4083.097.0378.2018.12.15 MBA4093.033.0364.2018.12.15
## 131209 131778
## MBA4099.045.0367.2018.12.15 MBA4104.091.0376.2018.12.15
## 140969 128881
## MBA4128.059.0369.2018.12.15
## 142304min(rowSums(OTU_table))## [1] 8596Total_counts<-as.data.frame(rowSums(OTU_table))
colnames(Total_counts)<-c("Counts")
ggplot(Total_counts, aes(x=Counts))+geom_histogram(binwidth=100)+geom_vline(aes(xintercept=mean(Counts, na.rm=T)), color="red", linetype="dashed", size=1)+theme_bw()+
ggtitle("Total count per sample distribution")+theme(plot.title=element_text(lineheight=10, size=15))+
xlab("Counts")+ylab("Number of samples")+theme(axis.text=element_text(size=15), axis.title=element_text(size=15))
#Remove all those samples that do not reach a minimum threshold of number of reads:
counts<-as.data.frame(rowSums(OTU_table))
colnames(counts)<-"counts"
counts$Sample<-row.names(counts)
subset_8000<-counts[counts$counts>=8000,]
subset_8000$Sample<-NULL
OTU_table<-OTU_table[row.names(OTU_table)%in%as.vector(row.names(subset_8000)),]
#Rarefy to same sequencing depth:
set.seed(1)
OTU_table_8000<-rrarefy(OTU_table, 8000) ## Warning in rrarefy(OTU_table, 8000): function should be used for observed
## counts, but smallest count is 2#Estimate richness:
richness_8000<-estimateR(OTU_table_8000)
richness_8000<-t(richness_8000)
richness_8000<-as.data.frame(richness_8000)
#Estimate evenness:
shannon<-diversityresult(x=OTU_table_8000, method="each site", index="Shannon")
diversity_8000<-cbind(shannon)
#Join data from richness and evenness calculations:
ecology_8000<-cbind(richness_8000[1], diversity_8000)
colnames(ecology_8000)<-c("Observed", "Shannon")
#Subset metadata
metadata<-metadata[row.names(metadata)%in%row.names(ecology_8000),,drop=FALSE]
all.equal(row.names(metadata),row.names(ecology_8000))## [1] TRUEecology_8000$SampleID<-metadata$SampleID
ecology_8000$Cohort<-metadata$sample_cohort
ecology_8000$hiv_phenotype<-metadata$hiv_phenotype
ecology_8000$sexual_orientation<-metadata$sexual_orientation
#Subset indivudals for this comparison
ecology_8000_nomsm<-ecology_8000[ecology_8000$sexual_orientation!="MSM" & ecology_8000$Cohort=="boston",,drop=FALSE]
ecology_8000_nomsm_melt<-melt(ecology_8000_nomsm)## Using SampleID, Cohort, hiv_phenotype, sexual_orientation as id variablesecology_8000_msm<-ecology_8000[ecology_8000$sexual_orientation=="MSM" & ecology_8000$Cohort=="boston",,drop=FALSE]
ecology_8000_msm_melt<-melt(ecology_8000_msm)## Using SampleID, Cohort, hiv_phenotype, sexual_orientation as id variablesplot_noMSM<-ggplot(data=ecology_8000_nomsm_melt, aes(x=hiv_phenotype,y=value))+geom_boxplot(aes(alpha=hiv_phenotype), outlier.color="white", fill="royalblue4")+theme_bw()+
geom_point(aes(alpha=hiv_phenotype), color="royalblue4", position=position_jitterdodge(jitter.width=0.25), size=1)+
facet_wrap(~variable, scales="free_y", nrow=1)+scale_alpha_manual(values=c(0.9, 0.6, 0.3))+
theme(axis.text.x = element_text(angle=90))+ggtitle("noMSM")
plot_MSM<-ggplot(data=ecology_8000_msm_melt, aes(x=hiv_phenotype,y=value))+geom_boxplot(aes(alpha=hiv_phenotype), outlier.color="white", fill="royalblue4")+theme_bw()+
geom_point(aes(alpha=hiv_phenotype), color="royalblue4", position=position_jitterdodge(jitter.width=0.25), size=1)+
facet_wrap(~variable, scales="free_y", nrow=1)+scale_alpha_manual(values=c(0.9, 0.6, 0.3))+
theme(axis.text.x = element_text(angle=90))+ggtitle("MSM")
ggsave("Figure3B.pdf", grid.arrange(plot_noMSM, plot_MSM, ncol=2, nrow=2), width=15, height=10)
#Statistical test
for (i in colnames(ecology_8000_nomsm)[1:2]){
print(i)
print(kruskal(ecology_8000_nomsm[i], ecology_8000_nomsm["hiv_phenotype"],group=F,p.adj = "bonferroni"))
}## [1] "Observed"
## $statistics
## Chisq Df p.chisq
## 0.51695815 2 0.77222519
##
## $parameters
## test p.ajusted name.t ntr alpha
## Kruskal-Wallis bonferroni ecology_8000_nomsm["hiv_phenotype"] 3 0.05
##
## $means
## ecology_8000_nomsm.i. rank std r Min Max Q25 Q50
## 1_hiv_negative 138.62353 56.711765 48.321093 85 61 249 96.0 137
## 2_suppressed 146.84211 60.842105 52.775482 19 79 300 112.0 130
## 4_unsuppressed 154.72727 63.045455 76.950752 11 46 333 116.5 146
## Q75
## 1_hiv_negative 175.0
## 2_suppressed 177.0
## 4_unsuppressed 186.5
##
## $comparison
## Difference pvalue Signif. LCL UCL
## 1_hiv_negative - 2_suppressed -4.1303406 1 -24.827767 16.567086
## 1_hiv_negative - 4_unsuppressed -6.3336898 1 -32.468284 19.800904
## 2_suppressed - 4_unsuppressed -2.2033493 1 -33.104413 28.697715
##
## $groups
## NULL
##
## attr(,"class")
## [1] "group"
## [1] "Shannon"
## $statistics
## Chisq Df p.chisq
## 0.28118102 2 0.86884502
##
## $parameters
## test p.ajusted name.t ntr alpha
## Kruskal-Wallis bonferroni ecology_8000_nomsm["hiv_phenotype"] 3 0.05
##
## $means
## ecology_8000_nomsm.i. rank std r Min
## 1_hiv_negative 3.4220815 57.035294 0.68648328 85 1.4674836
## 2_suppressed 3.5331089 61.157895 0.62121210 19 1.7319634
## 4_unsuppressed 3.4551810 60.000000 0.78369881 11 1.5978440
## Max Q25 Q50 Q75
## 1_hiv_negative 4.4513155 3.0708710 3.5533268 3.9279747
## 2_suppressed 4.5236775 3.1442770 3.7108917 3.9567380
## 4_unsuppressed 4.3767472 3.2269193 3.5800493 3.9414740
##
## $comparison
## Difference pvalue Signif. LCL UCL
## 1_hiv_negative - 2_suppressed -4.1226006 1 -24.843806 16.598605
## 1_hiv_negative - 4_unsuppressed -2.9647059 1 -29.129326 23.199914
## 2_suppressed - 4_unsuppressed 1.1578947 1 -29.778671 32.094461
##
## $groups
## NULL
##
## attr(,"class")
## [1] "group"for (i in colnames(ecology_8000_msm)[1:2]){
print(i)
print(kruskal(ecology_8000_msm[i], ecology_8000_msm["hiv_phenotype"],group=F,p.adj = "bonferroni"))
}## [1] "Observed"
## $statistics
## Chisq Df p.chisq
## 24.227218 2 5.4843659e-06
##
## $parameters
## test p.ajusted name.t ntr alpha
## Kruskal-Wallis bonferroni ecology_8000_msm["hiv_phenotype"] 3 0.05
##
## $means
## ecology_8000_msm.i. rank std r Min Max Q25 Q50
## 1_hiv_negative 209.87500 84.500000 73.519254 32 73 332 143.25 223.5
## 2_suppressed 127.97619 46.880952 41.464936 42 56 241 100.25 123.5
## 4_unsuppressed 140.13636 53.363636 52.645630 44 38 268 101.25 131.0
## Q75
## 1_hiv_negative 260.75
## 2_suppressed 149.50
## 4_unsuppressed 175.75
##
## $comparison
## Difference pvalue Signif. LCL UCL
## 1_hiv_negative - 2_suppressed 37.619048 0.0000 *** 20.105221 55.1328740
## 1_hiv_negative - 4_unsuppressed 31.136364 0.0001 *** 13.795518 48.4772097
## 2_suppressed - 4_unsuppressed -6.482684 0.9901 -22.584062 9.6186944
##
## $groups
## NULL
##
## attr(,"class")
## [1] "group"
## [1] "Shannon"
## $statistics
## Chisq Df p.chisq
## 17.723352 2 0.00014171734
##
## $parameters
## test p.ajusted name.t ntr alpha
## Kruskal-Wallis bonferroni ecology_8000_msm["hiv_phenotype"] 3 0.05
##
## $means
## ecology_8000_msm.i. rank std r Min Max
## 1_hiv_negative 3.8995527 79.156250 0.48983812 32 2.7533384 4.7212715
## 2_suppressed 3.2014241 45.404762 0.73980469 42 1.7331647 4.4821202
## 4_unsuppressed 3.4962105 58.659091 0.64920663 44 1.8739013 4.7411970
## Q25 Q50 Q75
## 1_hiv_negative 3.4553813 4.0302095 4.3146287
## 2_suppressed 2.7331945 3.2746910 3.6359910
## 4_unsuppressed 3.1059814 3.6027204 3.8471969
##
## $comparison
## Difference pvalue Signif. LCL
## 1_hiv_negative - 2_suppressed 33.751488 0.0000 *** 15.6325655
## 1_hiv_negative - 4_unsuppressed 20.497159 0.0193 * 2.5571932
## 2_suppressed - 4_unsuppressed -13.254329 0.1670 -29.9120041
## UCL
## 1_hiv_negative - 2_suppressed 51.8704107
## 1_hiv_negative - 4_unsuppressed 38.4371250
## 2_suppressed - 4_unsuppressed 3.4033461
##
## $groups
## NULL
##
## attr(,"class")
## [1] "group"#--------------------------------------------------------------------------------------------------------------