From 982cf08b5c13850b1792351ee43c0893b50452ab Mon Sep 17 00:00:00 2001 From: Huang Lin Date: Fri, 13 Mar 2020 16:45:32 -0400 Subject: [PATCH] Add Figure S10. --- .Rhistory | 430 +++---- .Rproj.user/E94C3793/pcs/files-pane.pper | 2 +- .Rproj.user/E94C3793/pcs/source-pane.pper | 2 +- .../E94C3793/pcs/windowlayoutstate.pper | 4 +- .Rproj.user/E94C3793/rmd-outputs | 5 +- .../large/fdr_power_ancom_bc_bh.csv | 3 + figures/Figure 3.jpeg | Bin 288597 -> 287411 bytes figures/Figure 3.pdf | Bin 12141 -> 12157 bytes figures/Figure 4.jpeg | Bin 371021 -> 370817 bytes figures/Figure 4.pdf | Bin 6437 -> 6396 bytes figures/Figure 4a.jpeg | Bin 198141 -> 197439 bytes figures/Figure 4a.pdf | Bin 5562 -> 5561 bytes figures/Figure 4b.jpeg | Bin 232349 -> 233298 bytes figures/Figure 4b.pdf | Bin 5565 -> 5554 bytes figures/Figure S1.jpeg | Bin 296113 -> 290783 bytes figures/Figure S1.pdf | Bin 12060 -> 12036 bytes figures/Figure S10.jpeg | Bin 0 -> 372990 bytes figures/Figure S10.pdf | Bin 0 -> 6390 bytes figures/Figure S10a.jpeg | Bin 0 -> 198600 bytes figures/Figure S10a.pdf | Bin 0 -> 5569 bytes figures/Figure S10b.jpeg | Bin 0 -> 234417 bytes figures/Figure S10b.pdf | Bin 0 -> 5554 bytes figures/Figure S2.jpeg | Bin 259736 -> 260465 bytes figures/Figure S2.pdf | Bin 12084 -> 12071 bytes figures/Figure S3.jpeg | Bin 370504 -> 370358 bytes figures/Figure S3.pdf | Bin 6442 -> 6399 bytes figures/Figure S3a.jpeg | Bin 199233 -> 197705 bytes figures/Figure S3a.pdf | Bin 5547 -> 5561 bytes figures/Figure S3b.jpeg | Bin 231464 -> 232418 bytes figures/Figure S3b.pdf | Bin 5558 -> 5572 bytes figures/Figure S4.jpeg | Bin 361776 -> 361333 bytes figures/Figure S4.pdf | Bin 6388 -> 6348 bytes figures/Figure S4a.jpeg | Bin 177851 -> 172489 bytes figures/Figure S4a.pdf | Bin 5512 -> 5516 bytes figures/Figure S4b.jpeg | Bin 245383 -> 246267 bytes figures/Figure S4b.pdf | Bin 5550 -> 5564 bytes figures/Figure S8.jpeg | Bin 343598 -> 340326 bytes figures/Figure S8.pdf | Bin 12281 -> 12361 bytes scripts/figure_3_s1_s2_s8.Rmd | 6 +- scripts/figure_3_s1_s2_s8.html | 335 +++--- scripts/figure_4_s3_s4.Rmd | 107 ++ scripts/figure_4_s3_s4.html | 1050 +++++++++++------ 42 files changed, 1177 insertions(+), 767 deletions(-) create mode 100644 data/sim_poi_gam/large/fdr_power_ancom_bc_bh.csv create mode 100644 figures/Figure S10.jpeg create mode 100644 figures/Figure S10.pdf create mode 100644 figures/Figure S10a.jpeg create mode 100644 figures/Figure S10a.pdf create mode 100644 figures/Figure S10b.jpeg create mode 100644 figures/Figure S10b.pdf diff --git a/.Rhistory b/.Rhistory index 416b461..c2155b2 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,218 +1,3 @@ -} -c(bias.true, bias.em, bias.wls) -# The number of taxa, library size, and sample size -n.taxa = 1000; balanced.micro.load = FALSE; balanced.lib.size = TRUE -samp.frac = "small"; n.samp = "50_50" -# The proportion of differentially abundant taxa -prop.diff=c(0.05, 0.15, 0.25) -# Set seeds -iterNum=100 -abn.seed=seq(iterNum) -# Define the simulation parameters -simparams=expand.grid(n.taxa, n.samp, prop.diff, abn.seed, -balanced.micro.load, balanced.lib.size, samp.frac) -colnames(simparams)=c("n.taxa", "n.samp", "prop.diff", "abn.seed", -"balanced.micro.load", "balanced.lib.size", "samp.frac") -simparams=simparams%>%mutate(obs.seed=abn.seed+1) -simparams=simparams%>%separate(col = n.samp, into = c("n.samp.grp1", "n.samp.grp2"), sep = "_") -simparams=simparams%>%arrange(n.taxa, n.samp.grp1, prop.diff, abn.seed, obs.seed) -simparams.list=apply(simparams, 1, paste0, collapse="_") -simparamslabels=c("n.taxa", "n.samp.grp1", "n.samp.grp2","prop.diff", "abn.seed", -"balanced.micro.load", "balanced.lib.size", "samp.frac", "obs.seed") -i = simparams.list[[1]] -print(i) -params = strsplit(i, "_")[[1]] -names(params) <- simparamslabels -# Paras for data generation -n.taxa=as.numeric(params["n.taxa"]) -n.samp.grp1=as.numeric(params["n.samp.grp1"]) -n.samp.grp2=as.numeric(params["n.samp.grp2"]) -prop.diff=as.numeric(params["prop.diff"]) -abn.seed=as.numeric(params["abn.seed"]) -obs.seed=as.numeric(params["obs.seed"]) -balanced.micro.load=as.logical(params["balanced.micro.load"]) -balanced.lib.size=as.logical(params["balanced.lib.size"]) -samp.frac=params["samp.frac"] -# Data generation -low.abn=50; med.abn=200; high.abn=10000; struc.zero.prop=0.20; out.zero.prop=0.05 -test.dat=abn.tab.gen1(n.taxa, n.samp.grp1, n.samp.grp2, low.abn, med.abn, high.abn, -prop.diff, abn.seed, obs.seed, struc.zero.prop, out.zero.prop, -balanced.micro.load, balanced.lib.size, samp.frac) -obs.abn=test.dat$obs.abn -meta.data=cbind(Sample.ID=paste0("sub", seq(n.samp.grp1+n.samp.grp2)), -group=rep(c(1, 2), c(n.samp.grp1, n.samp.grp2))) -d=log(test.dat$samp.frac) -# Pre-processing -feature.table=obs.abn; sample.var="Sample.ID"; group.var="group" -zero.cut=0.90; lib.cut=1000; neg.lb=FALSE -pre.process=feature_table_pre_process(feature.table, meta.data, sample.var, -group.var, zero.cut, lib.cut, neg.lb) -feature.table=pre.process$feature.table -group.name=pre.process$group.name -group.ind=pre.process$group.ind -struc.zero=pre.process$structure.zeros -# Paras for ANCOM-BC -grp.name=group.name; grp.ind=group.ind; adj.method="bonferroni" -tol.EM=1e-5; max.iterNum=100; perNum=1000; alpha=0.05 -# Run ANCOM-BC -suppressWarnings(out <- try(ANCOM_BC(feature.table, grp.name, grp.ind, struc.zero, -adj.method, tol.EM, max.iterNum, perNum, alpha), -silent = TRUE)) -if (inherits(out, "try-error")) { -bias.true=NA; bias.em=NA; bias.wls=NA; bias.diff=NA -}else{ -bias.true=mean(d[1:n.samp.grp1]) - mean(d[(n.samp.grp1+1):(n.samp.grp1+n.samp.grp2)]) -bias.em=out$bias.em[2]; bias.wls=out$bias.wls[2] -} -c(bias.true, bias.em, bias.wls) -i = simparams.list[[2]] -print(i) -params = strsplit(i, "_")[[1]] -names(params) <- simparamslabels -# Paras for data generation -n.taxa=as.numeric(params["n.taxa"]) -n.samp.grp1=as.numeric(params["n.samp.grp1"]) -n.samp.grp2=as.numeric(params["n.samp.grp2"]) -prop.diff=as.numeric(params["prop.diff"]) -abn.seed=as.numeric(params["abn.seed"]) -obs.seed=as.numeric(params["obs.seed"]) -balanced.micro.load=as.logical(params["balanced.micro.load"]) -balanced.lib.size=as.logical(params["balanced.lib.size"]) -samp.frac=params["samp.frac"] -# Data generation -low.abn=50; med.abn=200; high.abn=10000; struc.zero.prop=0.20; out.zero.prop=0.05 -test.dat=abn.tab.gen1(n.taxa, n.samp.grp1, n.samp.grp2, low.abn, med.abn, high.abn, -prop.diff, abn.seed, obs.seed, struc.zero.prop, out.zero.prop, -balanced.micro.load, balanced.lib.size, samp.frac) -obs.abn=test.dat$obs.abn -meta.data=cbind(Sample.ID=paste0("sub", seq(n.samp.grp1+n.samp.grp2)), -group=rep(c(1, 2), c(n.samp.grp1, n.samp.grp2))) -d=log(test.dat$samp.frac) -# Pre-processing -feature.table=obs.abn; sample.var="Sample.ID"; group.var="group" -zero.cut=0.90; lib.cut=1000; neg.lb=FALSE -pre.process=feature_table_pre_process(feature.table, meta.data, sample.var, -group.var, zero.cut, lib.cut, neg.lb) -feature.table=pre.process$feature.table -group.name=pre.process$group.name -group.ind=pre.process$group.ind -struc.zero=pre.process$structure.zeros -# Paras for ANCOM-BC -grp.name=group.name; grp.ind=group.ind; adj.method="bonferroni" -tol.EM=1e-5; max.iterNum=100; perNum=1000; alpha=0.05 -# Run ANCOM-BC -suppressWarnings(out <- try(ANCOM_BC(feature.table, grp.name, grp.ind, struc.zero, -adj.method, tol.EM, max.iterNum, perNum, alpha), -silent = TRUE)) -if (inherits(out, "try-error")) { -bias.true=NA; bias.em=NA; bias.wls=NA; bias.diff=NA -}else{ -bias.true=mean(d[1:n.samp.grp1]) - mean(d[(n.samp.grp1+1):(n.samp.grp1+n.samp.grp2)]) -bias.em=out$bias.em[2]; bias.wls=out$bias.wls[2] -} -c(bias.true, bias.em, bias.wls) -rm(list = ls()) -library(tidyverse) -library(phyloseq) -library(ggpubr) -library(RColorBrewer) -library(DT) -library(pander) -panderOptions('table.caption.prefix', NULL) -panderOptions('table.continues', NULL) -panderOptions('table.emphasize.rownames', FALSE) -source("sim_data_poi_gam_two_grp.R") -source("ancom_bc_v1.0.R") -# The number of taxa, library size, and sample size -n.taxa = 1000; balanced.micro.load = FALSE; balanced.lib.size = TRUE -samp.frac = "small"; n.samp = "50_50" -# The proportion of differentially abundant taxa -prop.diff=c(0.05, 0.15, 0.25) -# Set seeds -iterNum=100 -abn.seed=seq(iterNum) -# Define the simulation parameters -simparams=expand.grid(n.taxa, n.samp, prop.diff, abn.seed, -balanced.micro.load, balanced.lib.size, samp.frac) -colnames(simparams)=c("n.taxa", "n.samp", "prop.diff", "abn.seed", -"balanced.micro.load", "balanced.lib.size", "samp.frac") -simparams=simparams%>%mutate(obs.seed=abn.seed+1) -simparams=simparams%>%separate(col = n.samp, into = c("n.samp.grp1", "n.samp.grp2"), sep = "_") -simparams=simparams%>%arrange(n.taxa, n.samp.grp1, prop.diff, abn.seed, obs.seed) -simparams.list=apply(simparams, 1, paste0, collapse="_") -simparamslabels=c("n.taxa", "n.samp.grp1", "n.samp.grp2","prop.diff", "abn.seed", -"balanced.micro.load", "balanced.lib.size", "samp.frac", "obs.seed") -# Read in original data -p.df = read_csv("../data/sim_additional/em_vs_wls.csv") -# Reshape data -p.df = data.frame(t(p.df)) -View(p.df) -colnames(p.df) = c("true", "em", "wls") -p.df = p.df%>%mutate(prop = rep(prop.diff, each = iterNum)) -View(p.df) -p.df = read_csv("../data/sim_additional/em_vs_wls.csv") -# Reshape data -p.df = data.frame(t(p.df)) -colnames(p.df) = c("true", "em", "wls") -p.df = p.df%>%mutate(prop = rep(paste0(prop.diff*100, "%"), each = iterNum)) -View(p.df) -# Annotation data -txt.df = data.frame(em = -0.5, wls = 0.4, paste0(prop.diff*100, "%"), corr = NA, p = NA) -txt.df$corr = p.df%>%group_by(prop, samp.size)%>% -summarise(corr = cor.test(em, wls)$estimate)%>%.$corr -txt.df$corr = p.df%>%group_by(prop)%>% -summarise(corr = cor.test(em, wls)$estimate)%>%.$corr -txt.df$p = p.df%>%group_by(prop)%>% -summarise(p = cor.test(em, wls)$p.value)%>%.$p -txt.df = txt.df%>%mutate(level = ifelse(p < .001, "p < .001", round(p, 3)), -label = paste0("r = ", round(corr, 2), " (", level, ")")) -View(txt.df) -txt.df$prop = factor(txt.df$prop) -p.df$prop = factor(p.df$prop) -txt.df$prop -txt.df = data.frame(em = -0.5, wls = 0.4, prop = paste0(prop.diff*100, "%"), corr = NA, p = NA) -txt.df$corr = p.df%>%group_by(prop)%>% -summarise(corr = cor.test(em, wls)$estimate)%>%.$corr -txt.df$p = p.df%>%group_by(prop)%>% -summarise(p = cor.test(em, wls)$p.value)%>%.$p -txt.df = txt.df%>%mutate(level = ifelse(p < .001, "p < .001", round(p, 3)), -label = paste0("r = ", round(corr, 2), " (", level, ")")) -txt.df$prop = factor(txt.df$prop) -p.df$prop = factor(p.df$prop) -txt.df$prop -txt.df$prop = factor(txt.df$prop, levels = c("5%", "15%", "25%")) -txt.df$prop -p.df$prop = factor(p.df$prop, levels = c("5%", "15%", "25%")) -p=ggplot(p.df, aes(em, wls)) + geom_point(size = 0.3) + -geom_abline(slope = 1, intercept = 0, color = "steelblue", linetype="dashed") + -scale_x_continuous(breaks = seq(-0.8, 0.4, 0.4), limits = c(-0.85, 0.45)) + -scale_y_continuous(breaks = seq(-0.8, 0.4, 0.4), limits = c(-0.85, 0.45)) + -facet_wrap(.~prop)+ -labs(x="EM estimator", y="WLS estimator")+ -theme_bw()+ -theme(plot.title = element_text(hjust = 0.5), -panel.grid.major = element_blank(), -panel.grid.minor = element_blank(), -legend.position = "bottom", -strip.background = element_rect(fill="white"))+ -guides(color = guide_legend(override.aes = list(size=3)))+ -geom_text(data = txt.df, mapping = aes(x = em, y = wls, label = label)) -p -p=ggplot(p.df, aes(em, wls)) + geom_point(size = 0.3) + -geom_abline(slope = 1, intercept = 0, color = "steelblue", linetype="dashed") + -facet_wrap(.~prop)+ -labs(x="EM estimator", y="WLS estimator")+ -theme_bw()+ -theme(plot.title = element_text(hjust = 0.5), -panel.grid.major = element_blank(), -panel.grid.minor = element_blank(), -legend.position = "bottom", -strip.background = element_rect(fill="white"))+ -guides(color = guide_legend(override.aes = list(size=3)))+ -geom_text(data = txt.df, mapping = aes(x = em, y = wls, label = label)) -p -seq(-0.6, 0.6, 0.2) -seq(-0.6, 0.6, 0.3) p=ggplot(p.df, aes(em, wls)) + geom_point(size = 0.3) + geom_abline(slope = 1, intercept = 0, color = "steelblue", linetype="dashed") + scale_x_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + @@ -510,3 +295,218 @@ strip.background = element_rect(fill="white"))+ guides(color = guide_legend(override.aes = list(size=3)))+ geom_text(data = txt.df, mapping = aes(x = em, y = wls, label = label)) p +rm(list = ls()) +library(readr) +library(microbiome) +source("ancom_bc_v1.0.R") +data(dietswap) +pseq = dietswap +n_taxa = ntaxa(pseq) +n_samp = nsamples(pseq) +# Metadata +meta_data = meta(pseq) +# Taxonomy table +taxonomy = tax_table(pseq) +# Absolute abundances +otu_absolute = abundances(pseq) +# Pre-processing +feature.table = otu_absolute; sample.var = "sample"; group.var = "nationality"; +zero.cut = 0.90; lib.cut = 1000; neg.lb = TRUE +pre.process = feature_table_pre_process(feature.table, meta_data, sample.var, +group.var, zero.cut, lib.cut, neg.lb) +feature.table = pre.process$feature.table +group.name = pre.process$group.name +group.ind = pre.process$group.ind +struc.zero = pre.process$structure.zeros +# Paras for ANCOM-BC +grp.name = group.name; grp.ind = group.ind; adj.method = "bonferroni" +tol.EM = 1e-5; max.iterNum = 100; perNum = 1000; alpha = 0.05 +start_time<- Sys.time() +out = ANCOM_BC(feature.table, grp.name, grp.ind, struc.zero, +adj.method, tol.EM, max.iterNum, perNum, alpha) +end_time <- Sys.time() +end_time - start_time +# Pre-processing +feature.table = otu_absolute; sample.var = "sample"; group.var = "bmi_group"; +zero.cut = 0.90; lib.cut = 1000; neg.lb = TRUE +pre.process = feature_table_pre_process(feature.table, meta_data, sample.var, +group.var, zero.cut, lib.cut, neg.lb) +feature.table = pre.process$feature.table +group.name = pre.process$group.name +group.ind = pre.process$group.ind +struc.zero = pre.process$structure.zeros +# Paras for ANCOM-BC +grp.name = group.name; grp.ind = group.ind; adj.method = "bonferroni" +tol.EM = 1e-5; max.iterNum = 100; perNum = 1000; alpha = 0.05 +start_time <- Sys.time() +out = ANCOM_BC(feature.table, grp.name, grp.ind, struc.zero, +adj.method, tol.EM, max.iterNum, perNum, alpha) +end_time <- Sys.time() +end_time - start_time +rm(list = ls()) +library(readxl) +library(tidyverse) +library(nloptr) +library(phyloseq) +library(stringr) +library(ggpubr) +library(magrittr) +library(qwraps2) +library(pander) +panderOptions('table.caption.prefix', NULL) +panderOptions('table.continues', NULL) +panderOptions('table.emphasize.rownames', FALSE) +source("ancom_bc_v1.0.R") +# Read in meta data +meta_data=read_tsv("../data/global_gut/global_gut_metadata.txt") +meta_data=meta_data%>%transmute(Sample.ID=`#SampleID`, age=AGE, sex=SEX, country=COUNTRY)%>% +arrange(Sample.ID) +meta_data=meta_data[complete.cases(meta_data), ] +meta_data$age=as.numeric(meta_data$age) +meta_data$country=recode(meta_data$country, `GAZ:Malawi` = "MA", +`GAZ:United States of America` = "US", `GAZ:Venezuela` = "VEN") +# From Yatsunenko's supplementary files +meta_data2=read_excel("../data/global_gut/NIHMS365354-supplement-3.xls", sheet = 1, skip = 2) +meta_data2=meta_data2[!is.na(meta_data2$`Sample Identifier`), ] +meta_data2=meta_data2%>% +transmute(Sample.ID=`Sample Identifier`, age=`Age (Years)`, gender=Gender, +bmi=`BMI (kg/m2)`, breast.fed=`Breast-fed`, country=Country)%>% +arrange(Sample.ID) +meta_data2$age=signif(as.numeric(meta_data2$age), digits = 2) +meta_data2$bmi=signif(as.numeric(meta_data2$bmi), digits = 2) +meta_data2$breast.fed[which(meta_data2$breast.fed=="NA1")]="NA" +# read in taxonomy +taxonomy=read_tsv("../data/global_gut/global_gut_taxonomy.txt") +taxonomy=taxonomy%>%rowwise()%>% +mutate(genus_name=paste(Phylum, Genus, sep = ";")) +# read in the OTU table and aggregate into phylum level +otu_table=read_tsv("../data/global_gut/global_gut_otu.txt") +otu_table=otu_table[, -532] +otu_table$OTU_ID=taxonomy$Phylum[match(otu_table$OTU_ID, taxonomy$OTU_ID)] +phylum_table=otu_table%>%group_by(OTU_ID)%>% +summarise_all(sum) +non_info_pos=grep("\\p__\\b", phylum_table$OTU_ID) # Exact match +phylum_table=phylum_table[-non_info_pos, ] +phylum_table=as.data.frame(phylum_table) +options(qwraps2_markup = "markdown") +summary_template = +list("Age" = +list("min" = ~ min(.data$age, na.rm = T), +"max" = ~ max(.data$age, na.rm = T), +"mean (sd)" = ~ qwraps2::mean_sd(.data$age, na_rm = T, show_n = "never")), +"BMI" = +list("min" = ~ min(.data$bmi, na.rm = T), +"max" = ~ max(.data$bmi, na.rm = T), +"mean (sd)" = ~ qwraps2::mean_sd(.data$bmi, na_rm = T, show_n = "never")), +"Gender" = +list("F" = ~ n_perc0(.data$gender=="Female"), +"M" = ~ n_perc0(.data$gender=="Male"), +"NA" = ~ n_perc0(.data$gender=="NA")), +"Breast Fed" = +list("Y" = ~ qwraps2::n_perc0(.data$breast.fed == "Yes"), +"N" = ~ qwraps2::n_perc0(.data$breast.fed == "No (Formula-fed)"), +"NA" = ~ qwraps2::n_perc0(.data$breast.fed == "NA")) +) +temp = summary_table(dplyr::group_by(meta_data2, country), summary_template) +temp +View(temp) +rm(list = ls()) +library(tidyverse) +library(phyloseq) +library(ggpubr) +library(RColorBrewer) +library(DT) +library(pander) +panderOptions('table.caption.prefix', NULL) +panderOptions('table.continues', NULL) +panderOptions('table.emphasize.rownames', FALSE) +source("sim_data_poi_gam_two_grp.R") +source("ancom_bc_v1.0.R") +source("ancom_simple.R") +# The number of taxa, library size, and sample size +n.taxa = 1000; balanced.micro.load = FALSE; balanced.lib.size = TRUE +samp.frac = "small"; n.samp = c("20_30", "50_50") +# The proportion of differentially abundant taxa +prop.diff=c(0.05, 0.15, 0.25) +# Set seeds +iterNum=100 +abn.seed=seq(iterNum) +# Define the simulation parameters +simparams=expand.grid(n.taxa, n.samp, prop.diff, abn.seed, +balanced.micro.load, balanced.lib.size, samp.frac) +colnames(simparams)=c("n.taxa", "n.samp", "prop.diff", "abn.seed", +"balanced.micro.load", "balanced.lib.size", "samp.frac") +simparams=simparams%>%mutate(obs.seed=abn.seed+1) +simparams=simparams%>%separate(col = n.samp, into = c("n.samp.grp1", "n.samp.grp2"), sep = "_") +simparams=simparams%>%arrange(n.taxa, n.samp.grp1, prop.diff, abn.seed, obs.seed) +simparams.list=apply(simparams, 1, paste0, collapse="_") +simparamslabels=c("n.taxa", "n.samp.grp1", "n.samp.grp2","prop.diff", "abn.seed", +"balanced.micro.load", "balanced.lib.size", "samp.frac", "obs.seed") +## Read in original data +dat.ancom_bc=read_csv("../data/sim_poi_gam/large/fdr_power_ancom_bc_bh.csv") +dat.ancom=read_csv("../data/sim_poi_gam/large/fdr_power_ancom_large.csv") +dat.deseq2=read_csv("../data/sim_poi_gam/large/fdr_power_deseq2_large.csv") +dat.edger=read_csv("../data/sim_poi_gam/large/fdr_power_edger_large.csv") +dat.zilg=read_csv("../data/sim_poi_gam/large/fdr_power_zilg_large.csv") +dat.zig=read_csv("../data/sim_poi_gam/large/fdr_power_zig_large.csv") +dat.wilcox_un=read_csv("../data/sim_poi_gam/large/fdr_power_wilcox_un_large.csv") +dat.wilcox_tss=read_csv("../data/sim_poi_gam/large/fdr_power_wilcox_tss_large.csv") +## Reshaping data +simpattern=distinct(simparams, n.taxa, n.samp.grp1, n.samp.grp2, prop.diff) +data_summary = function(eval_data, method){ +FDR=tapply(as.numeric(eval_data[1, ]), +rep(seq(nrow(simpattern)), each=iterNum), function(x) mean(x, na.rm = T)) +FDRSD=tapply(as.numeric(eval_data[1, ]), +rep(seq(nrow(simpattern)), each=iterNum), function(x) sd(x, na.rm = T)) +power=tapply(as.numeric(eval_data[2, ]), +rep(seq(nrow(simpattern)), each=iterNum), function(x) mean(x, na.rm = T)) +powerSD=tapply(as.numeric(eval_data[2, ]), +rep(seq(nrow(simpattern)), each=iterNum), function(x) sd(x, na.rm = T)) +data_sum = data.frame(FDR, FDRSD, power, powerSD, simpattern, method) +data_sum = data_sum%>%unite(n.samp.grp, n.samp.grp1, n.samp.grp2, sep = ", ") +return(data_sum) +} +eval.dat.list = list(dat.ancom_bc, dat.ancom, dat.deseq2, dat.edger, +dat.zilg, dat.zig, dat.wilcox_un, dat.wilcox_tss) +method.list = list("ANCOM-BC", "ANCOM", "DESeq2", "edgeR", "ZILG", "ZIG", "Wilcoxon", "Wilcoxon + TSS") +dat.fig.list = vector(mode = "list", length = length(eval.dat.list)) +for (i in 1:length(eval.dat.list)) { +dat.fig.list[[i]] = data_summary(eval.dat.list[[i]], method.list[[i]]) +} +## Merge data +dat.fig=Reduce('rbind', dat.fig.list) +dat.fig$n.samp.grp=factor(dat.fig$n.samp.grp) +levels(dat.fig$n.samp.grp)=c("n = 20/30", "n = 50/50") +dat.fig$method=factor(dat.fig$method) +dat.fig$prop.diff=factor(dat.fig$prop.diff) +dat.fig%>%datatable()%>%formatRound(columns=c("FDR", "FDRSD", "power", "powerSD"), digits=3) +p1=ggplot(dat.fig, aes(x=prop.diff, y=FDR, fill=method)) + +geom_hline(yintercept=0.05, linetype="solid", color="black", size = 0.2)+ +geom_hline(yintercept=0.05+sqrt(0.05*0.95/100), linetype="dashed", color="black", size = 0.2)+ +scale_y_continuous(breaks = c(0.05, seq(0.2, 1, 0.2)), limits = c(0, 0.8))+ +coord_flip()+facet_grid(.~n.samp.grp)+ +geom_bar(stat="identity", position=position_dodge())+ +labs(x="Proportion of Differentially Abundant Taxa", y="", fill=NULL, title="FDR")+ +scale_fill_brewer(palette="Dark2")+ +theme_bw()+ +theme(panel.grid.major = element_blank(), +panel.grid.minor = element_blank(), +plot.title=element_text(hjust = 0.5), +strip.background = element_rect(fill="white"), +legend.position = "bottom")+ +guides(fill=guide_legend(nrow=2, byrow=TRUE)) +ggarrange(p1, labels = "a") +p2=ggplot(dat.fig, aes(x=prop.diff, y=power, fill=method)) + +scale_y_continuous(breaks = seq(0.2, 1, 0.2), limits = c(0, 1))+ +coord_flip()+facet_grid(.~n.samp.grp)+ +geom_bar(stat="identity", position=position_dodge())+ +labs(x="Proportion of Differentially Abundant Taxa", y="", fill=NULL, title="Power")+ +scale_fill_brewer(palette="Dark2")+ +theme_bw()+ +theme(panel.grid.major = element_blank(), +panel.grid.minor = element_blank(), +strip.background = element_rect(fill="white"), +plot.title=element_text(hjust = 0.5), +legend.position = "bottom")+ +guides(fill=guide_legend(nrow=2, byrow=TRUE)) +ggarrange(p2, labels = "b") diff --git a/.Rproj.user/E94C3793/pcs/files-pane.pper b/.Rproj.user/E94C3793/pcs/files-pane.pper index 2fcf01e..fe9c011 100644 --- a/.Rproj.user/E94C3793/pcs/files-pane.pper +++ b/.Rproj.user/E94C3793/pcs/files-pane.pper @@ -1,5 +1,5 @@ { - "path" : "~/Box Sync/0. 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