diff --git a/.Rhistory b/.Rhistory index 8d2ab32..416b461 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,156 +1,10 @@ -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 -n.taxa.raw = nrow(feature.table) -taxa.id.raw = rownames(feature.table) -n.samp = ncol(feature.table) -sample.id = colnames(feature.table) -n.grp = length(grp.ind) -n.samp.grp = sapply(grp.ind, length) -### 0. Discard taxa with structural zeros for the moment -comp.taxa.pos = which(apply(struc.zero, 1, function(x) all(x == 0))) # position of complete taxa (no structural zeros) -O = feature.table[comp.taxa.pos, ] -n.taxa = nrow(O) -taxa.id = rownames(O) -n.samp = ncol(O) -y = as.matrix(log(O + 1)) -### 1. Initial estimates of sampling fractions and mean absulute abundances -mu = t(apply(y, 1, function(i) tapply(i, rep(1:n.grp, n.samp.grp), function(j) -mean(j, na.rm = T)))) -d = colMeans(y - mu[, rep(1:n.grp, times = n.samp.grp)], na.rm = T) -## Iteration in case of missing values of y -iterNum = 0 -epsilon = 100 -while (epsilon > tol.EM & iterNum < max.iterNum) { -# Updating mu -mu.new = t(apply(t(t(y) - d), 1, function(i) tapply(i, rep(1:n.grp, n.samp.grp), function(j) -mean(j, na.rm = T)))) -# Updating d -d.new = colMeans(y - mu.new[, rep(1:ncol(mu.new), times = n.samp.grp)], na.rm = T) -# Iteration -epsilon = sqrt(sum((mu.new - mu)^2) + sum((d.new - d)^2)) -iterNum = iterNum + 1 -mu = mu.new -d = d.new -} -mu.var.each = (y-t(t(mu[, rep(1:ncol(mu), times = n.samp.grp)])+d))^2 -mu.var = t(apply(mu.var.each, 1, function(x) tapply(x, rep(1:n.grp, n.samp.grp), function(y) -mean(y, na.rm = T)))) -sample.size = t(apply(y, 1, function(x) -unlist(tapply(x, rep(1:n.grp, n.samp.grp), function(y) length(y[!is.na(y)]))))) -mu.var = mu.var/sample.size -### 2. Estimate the bias (between-group difference of sampling fractions) by E-M algorithm -bias.em.vec = rep(NA, n.grp - 1) -bias.wls.vec = rep(NA, n.grp - 1) -bias.var.vec = rep(NA, n.grp - 1) -for (i in 1:(n.grp-1)) { -Delta = mu[, 1] - mu[, 1+i] -nu = rowSums(mu.var[, c(1, 1+i)]) -## 2.1 Initials -pi0_0 = 0.75 -pi1_0 = 0.125 -pi2_0 = 0.125 -delta_0 = mean(Delta[Delta >= quantile(Delta, 0.25, na.rm = T)& -Delta <= quantile(Delta, 0.75, na.rm = T)], na.rm = T) -l1_0 = mean(Delta[Delta < quantile(Delta, 0.125, na.rm = T)], na.rm = T) -l2_0 = mean(Delta[Delta > quantile(Delta, 0.875, na.rm = T)], na.rm = T) -kappa1_0 = var(Delta[Delta < quantile(Delta, 0.125, na.rm = T)], na.rm = T) -if(is.na(kappa1_0)|kappa1_0 == 0) kappa1_0 = 1 -kappa2_0 = var(Delta[Delta > quantile(Delta, 0.875, na.rm = T)], na.rm = T) -if(is.na(kappa2_0)|kappa2_0 == 0) kappa2_0 = 1 -## 2.2 Apply E-M algorithm -# 2.21 Store all paras in vectors/matrices -pi0.vec = c(pi0_0); pi1.vec = c(pi1_0); pi2.vec = c(pi2_0) -delta.vec = c(delta_0); l1.vec = c(l1_0); l2.vec = c(l2_0) -kappa1.vec = c(kappa1_0); kappa2.vec = c(kappa2_0) -# 2.22 E-M iteration -iterNum = 0 -epsilon = 100 -while (epsilon > tol.EM & iterNum < max.iterNum) { -# print(iterNum) -## Current value of paras -pi0 = pi0.vec[length(pi0.vec)]; pi1 = pi1.vec[length(pi1.vec)]; pi2 = pi2.vec[length(pi2.vec)] -delta = delta.vec[length(delta.vec)]; -l1 = l1.vec[length(l1.vec)]; l2 = l2.vec[length(l2.vec)] -kappa1 = kappa1.vec[length(kappa1.vec)]; kappa2 = kappa2.vec[length(kappa2.vec)] -## E-step -pdf0 = sapply(seq(n.taxa), function(i) dnorm(Delta[i], delta, sqrt(nu[i]))) -pdf1 = sapply(seq(n.taxa), function(i) dnorm(Delta[i], delta + l1, sqrt(nu[i] + kappa1))) -pdf2 = sapply(seq(n.taxa), function(i) dnorm(Delta[i], delta + l2, sqrt(nu[i] + kappa2))) -r0i = pi0*pdf0/(pi0*pdf0 + pi1*pdf1 + pi2*pdf2); r0i[is.na(r0i)] = 0 -r1i = pi1*pdf1/(pi0*pdf0 + pi1*pdf1 + pi2*pdf2); r1i[is.na(r1i)] = 0 -r2i = pi2*pdf2/(pi0*pdf0 + pi1*pdf1 + pi2*pdf2); r2i[is.na(r2i)] = 0 -## M-step -pi0_new = mean(r0i, na.rm = T); pi1_new = mean(r1i, na.rm = T); pi2_new = mean(r2i, na.rm = T) -delta_new = sum(r0i*Delta/nu + r1i*(Delta-l1)/(nu+kappa1) + r2i*(Delta-l2)/(nu+kappa2), na.rm = T)/ -sum(r0i/nu + r1i/(nu+kappa1) + r2i/(nu+kappa2), na.rm = T) -l1_new = min(sum(r1i*(Delta-delta)/(nu+kappa1), na.rm = T)/sum(r1i/(nu+kappa1), na.rm = T), 0) -l2_new = max(sum(r2i*(Delta-delta)/(nu+kappa2), na.rm = T)/sum(r2i/(nu+kappa2), na.rm = T), 0) -# Nelder-Mead simplex algorithm for kappa1 and kappa2 -obj.kappa1 = function(x){ -log.pdf = log(sapply(seq(n.taxa), function(i) dnorm(Delta[i], delta+l1, sqrt(nu[i]+x)))) -log.pdf[is.infinite(log.pdf)] = 0 --sum(r1i*log.pdf, na.rm = T) } -kappa1_new = neldermead(x0 = kappa1, fn = obj.kappa1, lower = 0)$par -obj.kappa2 = function(x){ -log.pdf = log(sapply(seq(n.taxa), function(i) dnorm(Delta[i], delta+l2, sqrt(nu[i]+x)))) -log.pdf[is.infinite(log.pdf)] = 0 --sum(r2i*log.pdf, na.rm = T) -} -kappa2_new = neldermead(x0 = kappa2, fn = obj.kappa2, lower = 0)$par -## Merge to the paras vectors/matrices -pi0.vec = c(pi0.vec, pi0_new); pi1.vec = c(pi1.vec, pi1_new); pi2.vec = c(pi2.vec, pi2_new) -delta.vec = c(delta.vec, delta_new) -l1.vec = c(l1.vec, l1_new); l2.vec = c(l2.vec, l2_new) -kappa1.vec = c(kappa1.vec, kappa1_new); kappa2.vec = c(kappa2.vec, kappa2_new) -## Calculate the new epsilon -epsilon = sqrt((pi0_new-pi0)^2 + (pi1_new-pi1)^2 + (pi2_new-pi2)^2 + (delta_new-delta)^2+ -(l1_new-l1)^2 + (l2_new-l2)^2 + (kappa1_new-kappa1)^2 + (kappa2_new-kappa2)^2) -iterNum = iterNum+1 -} -# 2.23 Estimate the bias -bias.em.vec[i] = delta.vec[length(delta.vec)] -# 2.24 The WLS estimator of bias -# Cluster 0 -C0 = which(Delta >= quantile(Delta, pi1_new, na.rm = T) & Delta < quantile(Delta, 1 - pi2_new, na.rm = T)) -# Cluster 1 -C1 = which(Delta < quantile(Delta, pi1_new, na.rm = T)) -# Cluster 2 -C2 = which(Delta >= quantile(Delta, 1 - pi2_new, na.rm = T)) -nu_temp = nu -nu_temp[C1] = nu_temp[C1] + kappa1_new -nu_temp[C2] = nu_temp[C2] + kappa2_new -wls.deno = sum(1 / nu_temp) -wls.nume = 1 / nu_temp -wls.nume[C0] = (wls.nume * Delta)[C0] -wls.nume[C1] = (wls.nume * (Delta - l1_new))[C1] -wls.nume[C2] = (wls.nume * (Delta - l2_new))[C2]; -wls.nume = sum(wls.nume) -bias.wls.vec[i] = wls.nume / wls.deno -# 2.25 Estimate the variance of bias -bias.var.vec[i] = 1 / wls.deno -if (is.na(bias.var.vec[i])) bias.var.vec[i] = 0 -} -bias.em.vec = c(0, bias.em.vec) -bias.wls.vec = c(0, bias.wls.vec) -bias.var.vec -library(tidyverse) -source("scripts/sim_data_poi_gam_two_grp.R") -source("scripts/ancom_bc_v1.0.R") +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 = "20_30" +samp.frac = "small"; n.samp = "50_50" # The proportion of differentially abundant taxa -prop.diff=c(0.05, 0.15, 0.25, 0.50, 0.75) +prop.diff=c(0.05, 0.15, 0.25) # Set seeds iterNum=100 abn.seed=seq(iterNum) @@ -165,14 +19,7 @@ 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") -library(doParallel) -library(foreach) -simparams.list=simparams.list[[c(1, 101, 201, 301, 401)]] -simparams.list=simparams.list[c(1, 101, 201, 301, 401)] -simparams.list -start_time <- Sys.time() -simlist=foreach(i = simparams.list, .combine = 'cbind') %dopar% { -# i = simparams.list[[1]] +i = simparams.list[[1]] print(i) params = strsplit(i, "_")[[1]] names(params) <- simparamslabels @@ -194,6 +41,7 @@ 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 @@ -211,15 +59,58 @@ suppressWarnings(out <- try(ANCOM_BC(feature.table, grp.name, grp.ind, struc.zer adj.method, tol.EM, max.iterNum, perNum, alpha), silent = TRUE)) if (inherits(out, "try-error")) { -bias.em=NA; bias.wls=NA; bias.diff=NA +bias.true=NA; bias.em=NA; bias.wls=NA; bias.diff=NA }else{ -bias.em=out$bias.em[2]; bias.wls=out$bias.wls[2]; bias.diff=bias.em-bias.wls +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.em, bias.wls, bias.diff) +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] } -end_time <- Sys.time() -end_time - start_time -simlist +c(bias.true, bias.em, bias.wls) rm(list = ls()) library(tidyverse) library(phyloseq) @@ -234,9 +125,9 @@ 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 = "20_30" +samp.frac = "small"; n.samp = "50_50" # The proportion of differentially abundant taxa -prop.diff=c(0.05, 0.15, 0.25, 0.50, 0.75) +prop.diff=c(0.05, 0.15, 0.25) # Set seeds iterNum=100 abn.seed=seq(iterNum) @@ -251,137 +142,52 @@ 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 +# Read in original data p.df = read_csv("../data/sim_additional/em_vs_wls.csv") -## Reshaping data -simpattern=distinct(simparams, n.taxa, n.samp.grp1, n.samp.grp2, prop.diff) -View(simpattern) -View(p.df) -p.df = t(p.df) -View(p.df) -colnames(p.df) = c("em", "wls", "diff") +# 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)) -## Read in original data +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("em", "wls", "diff") -p.df = p.df%>%mutate(prop = rep(prop.diff, each = iterNum)) +colnames(p.df) = c("true", "em", "wls") +p.df = p.df%>%mutate(prop = rep(paste0(prop.diff*100, "%"), each = iterNum)) View(p.df) -txt.df = data.frame(prop = prop.diff, -corr = NA) -View(txt.df) -cor.test(p.df$em, p.df$wls) -temp = cor.test(p.df$em, p.df$wls) -temp$statistic -temp$estimate -txt.df$corr = p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)%>%.$estimate) -# Annotation data -txt.df = data.frame(prop = prop.diff, corr = NA, p = NA) -txt.df$corr = p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)%>%.$estimate) -View(txt.df) -temp$p.value -txt.df$p = p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)%>%.$p.value) -View(txt.df) -txt.df = txt.df%>%mutate(label = paste0("Corr = ", corr, " (p < .001)")) -txt.df = txt.df%>%mutate_each(label = paste0("Corr = ", corr, " (p < .001)")) -txt.df$label = paste0("Corr = ", round(corr, 2), " (p < .001)") -txt.df$label = paste0("Corr = ", round(txt.df$corr, 2), " (p < .001)") -paste0("Corr = ", round(txt.df$corr, 2), " (p < .001)") -# Annotation data -txt.df = data.frame(prop = prop.diff, corr = NA, p = NA) -txt.df$corr = p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)%>%.$estimate) -# Annotation data -txt.df = data.frame(prop = prop.diff, corr = NA, p = NA) -prop.diff -data.frame(prop = prop.diff, corr = NA, p = NA) # Annotation data -txt.df = data.frame(prop = prop.diff, corr = NA, p = NA) -View(txt.df) -txt.df$corr = p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)%>%.$estimate) -View(txt.df) -# Annotation data -txt.df = data.frame(prop = prop.diff, corr = NA, p = NA) -p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)%>%.$estimate) -p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)%>%.$estimate) -p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)$estimate%>%.$corr) -p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)$estimate) -p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)) -p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)$estimate)%>%.$corr -p.df%>%group_by(prop)%>%summarise(corr = cor.test(em, wls)$estimate)%>%.$corr -# Annotation data -txt.df = data.frame(prop = prop.diff, 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 -View(txt.df) -txt.df$label = paste0("Corr = ", round(txt.df$corr, 2), " (p < .001)") -View(txt.df) -txt.df = txt.df%>%mutate(p.level = ifelse(p < .001, "p < .001", round(p, 3)), -label = paste0("Corr = ", round(corr, 2), " (", p.level, ")")) -View(txt.df) -txt.df = data.frame(prop = prop.diff, 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(p.level = ifelse(p < .001, "p < .001", round(p, 3)), -label = paste0("Corr = ", round(corr, 2), " (", p.level, ")")) +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$prop, levels = prop.diff) +txt.df$prop = factor(txt.df$prop) +p.df$prop = factor(p.df$prop) txt.df$prop -p.df$prop = factor(p.df$prop, levels = prop.diff) -p.df -View(p.df) -View(simpattern) -p=ggplot(p.df, aes(em, wls)) + geom_point(size = 0.3) + -facet_wrap(.~prop, nrow = 2)+ -labs(x="EM estimator", y="WLS estimator"))+ -p=ggplot(p.df, aes(em, wls)) + geom_point(size = 0.3) + -facet_wrap(.~prop, nrow = 2)+ -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, color = NULL,group= NULL)) -p -txt.df = data.frame(em = 0.2, wls = 0.25, prop = prop.diff, 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 = 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("Corr = ", round(corr, 2), " (", level, ")")) -txt.df$prop = factor(txt.df$prop, levels = prop.diff) -p.df$prop = factor(p.df$prop, levels = prop.diff) -p=ggplot(p.df, aes(em, wls)) + geom_point(size = 0.3) + -facet_wrap(.~prop, nrow = 2)+ -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, color = NULL,group= NULL)) -p -?facet_wrap -p=ggplot(p.df, aes(em, wls)) + geom_point(size = 0.3) + -geom_abline(slope = 1, intercept = 0, color = "steelblue") + -facet_wrap(.~prop, nrow = 2, scales = "free")+ -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, color = NULL,group= NULL)) -p +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") + -facet_wrap(.~prop, nrow = 2)+ +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), @@ -390,18 +196,11 @@ 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, color = NULL,group= NULL)) +geom_text(data = txt.df, mapping = aes(x = em, y = wls, label = label)) p -txt.df = data.frame(em = -0.5, wls = 0.4, prop = prop.diff, 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("Corr = ", round(corr, 2), " (", level, ")")) -txt.df$prop = factor(txt.df$prop, levels = prop.diff) -p.df$prop = factor(p.df$prop, levels = prop.diff) p=ggplot(p.df, aes(em, wls)) + geom_point(size = 0.3) + -geom_abline(slope = 1, intercept = 0, color = "steelblue") + -facet_wrap(.~prop, nrow = 2)+ +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), @@ -412,9 +211,13 @@ 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", alpha = 0.9) + -facet_wrap(.~prop, nrow = 2)+ +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)) + +scale_y_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + +facet_wrap(.~prop)+ labs(x="EM estimator", y="WLS estimator")+ theme_bw()+ theme(plot.title = element_text(hjust = 0.5), @@ -425,9 +228,20 @@ 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 +txt.df = data.frame(em = -0.35, 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, levels = c("5%", "15%", "25%")) +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") + -facet_wrap(.~prop, nrow = 2)+ +scale_x_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + +scale_y_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + +facet_wrap(.~prop)+ labs(x="EM estimator", y="WLS estimator")+ theme_bw()+ theme(plot.title = element_text(hjust = 0.5), @@ -438,11 +252,210 @@ 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 +ggsave("../figures/Figure S9.pdf", width=6.25, height=5, units='in') +ggsave("../figures/Figure S9.jpeg", width=6.25, height=5, units='in', dpi = 300) +ggsave("../figures/Figure S9.pdf", width=10, height=5, units='in') +ggsave("../figures/Figure S9.jpeg", width=10, height=5, units='in', dpi = 300) +# 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=1 +obs.seed=1:100 +# Define the simulation parameters +simparams=expand.grid(n.taxa, n.samp, prop.diff, abn.seed, obs.seed, +balanced.micro.load, balanced.lib.size, samp.frac) +colnames(simparams)=c("n.taxa", "n.samp", "prop.diff", "abn.seed", "obs.seed", +"balanced.micro.load", "balanced.lib.size", "samp.frac") +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", "obs.seed", +"balanced.micro.load", "balanced.lib.size", "samp.frac") +# Read in original data +tab.df = read_csv("../data/sim_additional/bias_var_em_wls.csv") +# Reshape data +tab.df = data.frame(t(tab.df)) +colnames(tab.df) = c("true", "em", "wls") +tab.df = tab.df%>%mutate(prop = rep(paste0(prop.diff*100, "%"), each = iterNum)) +View(tab.df) +# Summary +tab = tab.df%>%group_by(prop)%>% +summarise(bias.em = (mean(em) - true[1])^2, +bias.wls = (mean(wls) - true[1])^2, +var.em = var(em), +var.wls = var(wls), +`1/(nm0)` = 1/(50*1000*(1-prop.diff)))%>% +arrange(prop, samp.size) +# Summary +tab = tab.df%>%group_by(prop)%>% +summarise(bias.em = (mean(em) - true[1])^2, +bias.wls = (mean(wls) - true[1])^2, +var.em = var(em), +var.wls = var(wls))%>% +arrange(prop, samp.size) +# Summary +tab = tab.df%>%group_by(prop)%>% +summarise(bias.em = (mean(em) - true[1])^2, +bias.wls = (mean(wls) - true[1])^2, +var.em = var(em), +var.wls = var(wls))%>% +arrange(prop) +View(tab) +tab.df = read_csv("../data/sim_additional/bias_var_em_wls.csv") +# Reshape data +tab.df = data.frame(t(tab.df)) +colnames(tab.df) = c("true", "em", "wls") +tab.df = tab.df%>%mutate(prop = rep(prop.diff, each = iterNum)) +# Summary +tab = tab.df%>%group_by(prop)%>% +summarise(bias.em = (mean(em) - true[1])^2, +bias.wls = (mean(wls) - true[1])^2, +var.em = var(em), +var.wls = var(wls), +`1/(nm0)` = 1/(50*1000*(1-prop)))%>% +arrange(prop) +# Summary +tab = tab.df%>%group_by(prop)%>% +summarise(bias.em = (mean(em) - true[1])^2, +bias.wls = (mean(wls) - true[1])^2, +var.em = var(em), +var.wls = var(wls), +`1/(nm0)` = 1/(50*1000*(1-prop[1])))%>% +arrange(prop) +View(tab) +1/(50*1000*0.95) +1/(50*1000*0.85) +?recode +tab$prop = recode(tab$prop, `0.05` = "5%", `0.15` = "15%", `0.25` = "25%") +pander(tab) +# Summary +tab = tab.df%>%group_by(prop)%>% +summarise(bias.em = signif((mean(em) - true[1])^2, 3), +bias.wls = signif((mean(wls) - true[1])^2, 3), +var.em = signif(var(em), 3), +var.wls = signif(var(wls), 3), +`1/(nm0)` = signif(1/(50*1000*(1-prop[1])), 3))%>% +arrange(prop) +tab$prop = recode(tab$prop, `0.05` = "5%", `0.15` = "15%", `0.25` = "25%") +pander(tab) +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") +# Read in original data +tab.df = read_csv("../data/sim_additional/bias_var_em_wls.csv") +# Reshape data +tab.df = data.frame(t(tab.df)) +colnames(tab.df) = c("true", "em", "wls") +tab.df = tab.df%>%mutate(diff = rep(prop.diff, each = iterNum)) +# 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=1 +obs.seed=1:100 +# Define the simulation parameters +simparams=expand.grid(n.taxa, n.samp, prop.diff, abn.seed, obs.seed, +balanced.micro.load, balanced.lib.size, samp.frac) +colnames(simparams)=c("n.taxa", "n.samp", "prop.diff", "abn.seed", "obs.seed", +"balanced.micro.load", "balanced.lib.size", "samp.frac") +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", "obs.seed", +"balanced.micro.load", "balanced.lib.size", "samp.frac") +# Read in original data +tab.df = read_csv("../data/sim_additional/bias_var_em_wls.csv") +# Reshape data +tab.df = data.frame(t(tab.df)) +colnames(tab.df) = c("true", "em", "wls") +tab.df = tab.df%>%mutate(diff = rep(prop.diff, each = iterNum)) +# Summary +tab = tab.df%>%group_by(diff)%>% +summarise(bias2.em = signif((mean(em) - true[1])^2, 3), +bias2.wls = signif((mean(wls) - true[1])^2, 3), +var.em = signif(var(em), 3), +var.wls = signif(var(wls), 3), +`1/(nm0)` = signif(1/(50*1000*(1-diff[1])), 3))%>% +arrange(prop) +# Summary +tab = tab.df%>%group_by(diff)%>% +summarise(bias2.em = signif((mean(em) - true[1])^2, 3), +bias2.wls = signif((mean(wls) - true[1])^2, 3), +var.em = signif(var(em), 3), +var.wls = signif(var(wls), 3), +`1/(nm0)` = signif(1/(50*1000*(1-diff[1])), 3))%>% +arrange(diff) +tab$diff = recode(tab$diff, `0.05` = "5%", `0.15` = "15%", `0.25` = "25%") +pander(tab) +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)) +colnames(p.df) = c("true", "em", "wls") +p.df = p.df%>%mutate(prop = rep(paste0(prop.diff*100, "%"), each = iterNum)) +# Annotation data +txt.df = data.frame(em = -0.35, 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 +View(txt.df) +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, levels = c("5%", "15%", "25%")) +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, nrow = 2)+ +scale_x_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + +scale_y_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + +facet_wrap(.~prop)+ labs(x="EM estimator", y="WLS estimator")+ theme_bw()+ theme(plot.title = element_text(hjust = 0.5), @@ -453,51 +466,40 @@ 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 -# Read in original data -p.df = read_csv("../data/sim_additional/em_vs_wls.csv") -View(p.df) -p.df = p.df[, -1:100] -p.df = p.df[, -(1:100)] -View(p.df) View(p.df) -colnames(p.df) = paste0("result", 1:400) -View(p.df) -colnames(p.df) = paste0("result.", 1:400) -View(p.df) -write.csv(p.df, "../data/sim_additional/em_vs_wls.csv") -# Read in original data -p.df = read_csv("../data/sim_additional/em_vs_wls.csv") -View(p.df) -# Read in original data -p.df = read_csv("../data/sim_additional/em_vs_wls.csv") -View(p.df) -# Read in original data -p.df = read_csv("../data/sim_additional/em_vs_wls.csv") -# Reshape data -p.df = data.frame(t(p.df)) -colnames(p.df) = c("em", "wls", "diff") -p.df = p.df%>%mutate(prop = rep(paste0(prop.diff*100, "%"), each = iterNum)) -paste0(prop.diff*100, "%") -# The proportion of differentially abundant taxa -prop.diff=c(0.15, 0.25, 0.50, 0.75) -p.df = p.df%>%mutate(prop = rep(paste0(prop.diff*100, "%"), each = iterNum)) -View(p.df) -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("Corr = ", round(corr, 2), " (", level, ")")) +temp = p.df%>%filter(prop = "25%") +temp = p.df%>%filter(prop == "25%") +View(temp) +cor.test(temp$em, temp$wls) +# Annotation data +txt.df = data.frame(em = -0.35, wls = 0.4, corr = NA, p = NA) View(txt.df) -txt.df$prop -txt.df$prop = factor(txt.df$prop) -txt.df$prop -p.df$prop = factor(p.df$prop) -p.df$prop +txt.df$corr = p.df%>%group_by(prop)%>% +summarise(corr = cor.test(em, wls)$estimate)%>%.$corr +# Annotation data +txt.df = p.df%>%group_by(prop)%>% +summarise(corr = cor.test(em, wls)$estimate, +p = cor.test(em, wls)$p.value)) +# Annotation data +txt.df = p.df%>%group_by(prop)%>% +summarise(corr = cor.test(em, wls)$estimate, +p = cor.test(em, wls)$p.value) +View(txt.df) +# Annotation data +txt.df = p.df%>%group_by(prop)%>% +summarise(corr = cor.test(em, wls)$estimate, +p = cor.test(em, wls)$p.value)%>% +mutate(em = -0.35, wls = 0.4) +View(txt.df) +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, levels = c("5%", "15%", "25%")) +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, nrow = 2)+ +scale_x_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + +scale_y_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + +facet_wrap(.~prop)+ labs(x="EM estimator", y="WLS estimator")+ theme_bw()+ theme(plot.title = element_text(hjust = 0.5), @@ -508,5 +510,3 @@ 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 -ggsave("../figures/Figure S9.pdf", width=6.25, height=5, units='in') -ggsave("../figures/Figure S9.jpeg", width=6.25, height=5, units='in', dpi = 300) diff --git a/.Rproj.user/E94C3793/rmd-outputs b/.Rproj.user/E94C3793/rmd-outputs index f4d6c1c..d44202d 100644 --- a/.Rproj.user/E94C3793/rmd-outputs +++ b/.Rproj.user/E94C3793/rmd-outputs @@ -1,10 +1,8 @@ ~/Box Sync/0. Dissertation Research/1st Paper/Manuscript/Tables and Figures/scripts/figure_s6_s7_table_s4_s5.html -~/Box Sync/0. Dissertation Research/1st Paper/Manuscript/Tables and Figures/scripts/figure_s9.html -~/Box Sync/0. Dissertation Research/1st Paper/Manuscript/Tables and Figures/scripts/figure_5.html -~/Box Sync/0. Dissertation Research/1st Paper/Manuscript/Tables and Figures/scripts/figure_6_table_s1_s2_s3.html +~/Box Sync/0. Dissertation Research/1st Paper/Manuscript/Tables and Figures/scripts/figure_s9_table_s6.html ~/Box Sync/0. Dissertation Research/1st Paper/Manuscript/Tables and Figures/scripts/figure_s5.html - +~/Box Sync/0. Dissertation Research/1st Paper/Manuscript/Tables and Figures/scripts/figure_s9_table_s6.html diff --git a/data/sim_additional/bias_var_em_wls.csv b/data/sim_additional/bias_var_em_wls.csv new file mode 100644 index 0000000..02478ab --- /dev/null +++ b/data/sim_additional/bias_var_em_wls.csv @@ -0,0 +1,4 @@ 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diff --git a/data/sim_additional/em_vs_wls.csv b/data/sim_additional/em_vs_wls.csv index 0e32150..5e9a1e9 100644 --- a/data/sim_additional/em_vs_wls.csv +++ b/data/sim_additional/em_vs_wls.csv @@ -1,4 +1,4 @@ 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+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") +``` + +# 1. Correlation Between EM and WLS Estimators + +## 1.1 Simulation Settings + +```{r, message=FALSE, warning=FALSE, comment=NA} +# 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") +``` + +## 1.2 ANCOM-BC + +```{r, message=FALSE, warning=FALSE, comment=NA, eval=FALSE} +library(doParallel) +library(foreach) + +detectCores() +myCluster=makeCluster(4, type = "FORK") +registerDoParallel(myCluster) + +start_time <- Sys.time() +simlist=foreach(i = simparams.list, .combine = 'cbind') %dopar% { + # 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) +} +end_time <- Sys.time() +end_time - start_time + +stopCluster(myCluster) +write_csv(data.frame(simlist), "em_vs_wls.csv") +``` + +## 1.3 Fig. S9 + +```{r, message=FALSE, warning=FALSE, comment=NA, fig.width=10} +# Read in original data +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)) + +# Annotation data +txt.df = p.df%>%group_by(prop)%>% + summarise(corr = cor.test(em, wls)$estimate, + p = cor.test(em, wls)$p.value)%>% + mutate(em = -0.35, wls = 0.4) +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, levels = c("5%", "15%", "25%")) +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(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + + scale_y_continuous(breaks = seq(-0.6, 0.6, 0.3), limits = c(-0.65, 0.65)) + + 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 +ggsave("../figures/Figure S9.pdf", width=10, height=5, units='in') +ggsave("../figures/Figure S9.jpeg", width=10, height=5, units='in', dpi = 300) +``` + +# 2. Bias and Variance of EM and WLS Estimators + +## 2.1 Simulation Settings + +```{r, message=FALSE, warning=FALSE, comment=NA} +# 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=1 +obs.seed=1:100 + +# Define the simulation parameters +simparams=expand.grid(n.taxa, n.samp, prop.diff, abn.seed, obs.seed, + balanced.micro.load, balanced.lib.size, samp.frac) +colnames(simparams)=c("n.taxa", "n.samp", "prop.diff", "abn.seed", "obs.seed", + "balanced.micro.load", "balanced.lib.size", "samp.frac") +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", "obs.seed", + "balanced.micro.load", "balanced.lib.size", "samp.frac") +``` + +## 2.2 ANCOM-BC + +```{r, message=FALSE, warning=FALSE, comment=NA, eval=FALSE} +library(doParallel) +library(foreach) + +detectCores() +myCluster=makeCluster(4, type = "FORK") +registerDoParallel(myCluster) + +start_time <- Sys.time() +simlist=foreach(i = simparams.list, .combine = 'cbind') %dopar% { + # 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) +} +end_time <- Sys.time() +end_time - start_time + +stopCluster(myCluster) +write_csv(data.frame(simlist), "bias_var_em_wls.csv") +``` + +## 2.3 Table S6 + +```{r, message=FALSE, warning=FALSE, comment=NA} +# Read in original data +tab.df = read_csv("../data/sim_additional/bias_var_em_wls.csv") + +# Reshape data +tab.df = data.frame(t(tab.df)) +colnames(tab.df) = c("true", "em", "wls") +tab.df = tab.df%>%mutate(diff = rep(prop.diff, each = iterNum)) + +# Summary +tab = tab.df%>%group_by(diff)%>% + summarise(bias2.em = signif((mean(em) - true[1])^2, 3), + bias2.wls = signif((mean(wls) - true[1])^2, 3), + var.em = signif(var(em), 3), + var.wls = signif(var(wls), 3), + `1/(nm0)` = signif(1/(50*1000*(1-diff[1])), 3))%>% + arrange(diff) + +tab$diff = recode(tab$diff, `0.05` = "5%", `0.15` = "15%", `0.25` = "25%") +pander(tab) +``` + +# Session information + +```{r, message=FALSE, warning=FALSE, comment=NA} +sessionInfo() +devtools::session_info() +``` diff --git a/scripts/figure_s9.html b/scripts/figure_s9_table_s6.html similarity index 79% rename from scripts/figure_s9.html rename to scripts/figure_s9_table_s6.html index 6d8b6cd..c3c8cbf 100644 --- a/scripts/figure_s9.html +++ b/scripts/figure_s9_table_s6.html @@ -10,9 +10,9 @@ - + -