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lm_of_simulation_params.R
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lm_of_simulation_params.R
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#J. Harrison
library(xtable)
rm(list=ls())
set.seed(666)
options(scipen = 99)
dat <- read.table("./data/allv4.csv",
stringsAsFactors = F)
#remove the unwanted header names, after improving the names of the data.frame
names(dat) <- dat[3,]
dat <- dat[-grep("notus", dat$notus),]
#SANITY CHECK
dim(dat) #Check that dimensions are correct.
dat$combo <- paste(dat$modelType,
dat$notus,
dat$nreads,
dat$nreps,
dat$precision_theta,
dat$num_taxa_different,
dat$effectsize,
#dat$simRep,
sep = "")
table(dat$notus)
table(dat$nreads)
table(dat$modelType)
#
# #SANITY CHECK
# #check that everything ran.
# params <- read.csv("serialRunParamsv2.csv")
# sanityCombo <- paste(params[,1],
# params[,2],
# params[,3],
# params[,4],
# params[,5],
# params[,6],
# params[,7]
# #params[,8]
# , sep = "")
#
# #Note the !
# notdone <- paste(params[,1],
# params[,2],
# params[,3],
# params[,4],
# params[,5],
# params[,6],
# params[,7]
# #params[,8]
# ,sep = ":")[which(!(sanityCombo %in% dat$combo))]
#
# redo <- data.frame(matrix(unlist(strsplit(notdone, ":")),
# byrow = T,
# ncol = 8))
# dim(redo)
# write.csv(redo, file = "redo.csv", row.names = F)
#REMAKE the combo variable without simrep, so that
#averaging across replicates works properly
dat <- dat[,-which(names(dat) == "combo")]
dim(dat)
combo <- paste(dat$modelType,
dat$notus,
dat$nreads,
dat$nreps,
dat$precision_theta,
dat$num_taxa_different,
dat$effectsize,
sep = "")
dat <- data.frame(combo,dat)
names(dat)[1] <- "combo"
#Convert everything to numbers, to clean up code below.
for(i in 3:length(dat)){
dat[,i] <- as.numeric(dat[,i])
}
#Convert true positives to proportions.
truepPropsAll <- dat[, grep("truep_\\w+_all", names(dat))] / dat$numDiffering
truepPropsabund <- dat[, grep("truep_\\w+_abund", names(dat))] / dat$numDiffering_abund
truepPropsmed <- dat[, grep("truep_\\w+_med$", names(dat))] / dat$numDiffering_med
truepPropsrare <- dat[, grep("truep_\\w+_rare", names(dat))] / dat$numDiffering_rare
truepPropsmedhi <- dat[, grep("truep_\\w+_medhi", names(dat))] / dat$numDiffering_rare
#Calculate false positive rates
dat$fpr_diff_all <- dat$falsep_diff_all/(dat$falsep_diff_all + dat$truen_diff_all)
dat$fpr_wilcox_all <- dat$falsep_wilcoxBH_FDR_all/(dat$falsep_wilcoxBH_FDR_all + dat$truen_wilcoxBH_FDR_all)
dat$fpr_deseq_all <- dat$falsep_deseq_all/(dat$falsep_deseq_all + dat$truen_deseq_all)
dat$fpr_edger_all <- dat$falsep_edgeR_all/(dat$falsep_edgeR_all + dat$truen_edgeR_all)
dat$fpr_ancom_all <- dat$falsep_ancom_all/(dat$falsep_ancom_all + dat$truen_ancom_all)
dat$fpr_stan_all <- dat$falsep_stanHMC_all/(dat$falsep_stanHMC_all + dat$truen_stanHMC_all)
dat$fpr_stanvb_all <- dat$falsep_stanVB_all/(dat$falsep_stanVB_all + dat$truen_stanVB_all)
dat$fpr_aldexT_all <- dat$falsep_aldexT_all/(dat$falsep_aldexT_all + dat$truen_aldexT_all)
dat$fpr_jags_clr_all <- dat$falsep_jags_clr_all/(dat$falsep_jags_clr_all + dat$truen_jags_clr_all)
dat <- data.frame(dat, truepPropsAll, truepPropsabund, truepPropsmed, truepPropsrare, truepPropsmedhi)
out <- dat
####################
# Perform analysis #
####################
#Make Uniform the reference level
out$modelType <- as.factor(out$modelType)
out$modelType <- relevel(out$modelType, ref = "uniform")
regplotr <- function(reg){
par(mfrow = c(2,2))
plot(reg, which = c(1,2,3,4))
}
results <- data.frame(
matrix(ncol = 9,
nrow = 5))
k <- 1
for(i in grep("all.1$", names(out))){
reg <- lm(out[,i] ~ out$modelType
+ out$notus
+ out$nreads
+ out$nreps
+ out$precision_theta
)
#Calculate CIs in beta coefficients.
his <- round(summary(reg)$coefficients[,1] + 1.96*summary(reg)$coefficients[,2],
2)
lows <- round(summary(reg)$coefficients[,1] - 1.96*summary(reg)$coefficients[,2],
2)
stars <- vector()
stars[which(round(summary(reg)$coefficients[,4], 2) <= 0.1)] <- "*"
stars[which(round(summary(reg)$coefficients[,4], 2) <= 0.05)] <- "**"
stars[which(round(summary(reg)$coefficients[,4], 2) <= 0.01)] <- "***"
stars[which(round(summary(reg)$coefficients[,4], 2) > 0.1)] <- ""
#output results
results[k,1] <- names(out)[i]
results[k,2:8] <- paste(round(summary(reg)$coefficients[,1], 2),
paste("(",
paste(his, lows, sep = ", "),
")",
stars,
sep = "")
)
regplotr(reg)
k <- k + 1
}
names(results) <- c("Method",
"Intercept",
"Pareto (shape = 0.7)",
"Pareto (shape = 4)",
"Num. of features",
"Num. of observations",
"Num. of replicates",
"Noise")
#Rewrite so it looks nice in latex
results[,1] <- c("DMM Gibbs",
"Wilcoxon",
"DESeq2",
"edgeR",
"ANCOM",
"DMM HMC",
"DMM VB",
"DMM Gibbs CLR",
"Aldex T-test")
print(xtable(results, type = "latex"),
file = "lm_results.tex",
floating.environment='sidewaystable',
include.rownames=FALSE)
#sanity check
lm(out$truep_ancom_all.1 ~ out$modelType
+ out$notus
+ out$nreads
+ out$nreps
+ out$precision_theta
)
lm(out$truep_diff_all.1 ~ out$modelType
+ out$notus
+ out$nreads
+ out$nreps
+ out$precision_theta
)
#fpr
results <- data.frame(
matrix(ncol = 9,
nrow = 5))
k <- 1
for(i in grep("fpr[A-Za-z_]*all", names(out))){
reg <- lm(out[,i] ~ out$modelType
+ out$notus
+ out$nreads
+ out$nreps
+ out$precision_theta
)
#Calculate CIs in beta coefficients.
his <- round(summary(reg)$coefficients[,1] + 1.96*summary(reg)$coefficients[,2],
2)
lows <- round(summary(reg)$coefficients[,1] - 1.96*summary(reg)$coefficients[,2],
2)
stars <- vector()
stars[which(round(summary(reg)$coefficients[,4], 2) <= 0.1)] <- "*"
stars[which(round(summary(reg)$coefficients[,4], 2) <= 0.05)] <- "**"
stars[which(round(summary(reg)$coefficients[,4], 2) <= 0.01)] <- "***"
stars[which(round(summary(reg)$coefficients[,4], 2) > 0.1)] <- ""
#output results
results[k,1] <- names(out)[i]
results[k,2:8] <- paste(round(summary(reg)$coefficients[,1], 2),
paste("(",
paste(his, lows, sep = ", "),
")",
stars,
sep = "")
)
#regplotr(reg)
k <- k + 1
}
names(results) <- c("Method",
"Intercept",
"Pareto (shape = 0.7)",
"Pareto (shape = 4)",
"Num. of features",
"Num. of observations",
"Num. of replicates",
"Noise")
#Rewrite so it looks nice in latex
results[,1] <- c("DMM Gibbs",
"Wilcoxon",
"DESeq2",
"edgeR",
"ANCOM",
"DMM HMC",
"DMM VB",
"DMM Gibbs CLR",
"Aldex T-test")
print(xtable(results, type = "latex"),
file = "lm_results_fdr.tex",
floating.environment='table',
include.rownames=FALSE)
#sanity check
re<-lm(out$fpr_ancom_all ~ out$modelType
+ out$notus
+ out$nreads
+ out$nreps
+ out$precision_theta
);summary(re)
re<- lm(out$fpr_diff_all ~ out$modelType
+ out$notus
+ out$nreads
+ out$nreps
+ out$precision_theta
);summary(re)