/
cell_counts_bw_groups.R
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cell_counts_bw_groups.R
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##
## Compare log( cell counts ) b/w sBV, aBV and noBV groups stratified by CSTs/Lb-CSTs
##
library(car)
library(chron)
library(rstan)
rstan_options(auto_write = TRUE)
source("stan_utils.R")
source("Pmisc.R") # for bp.plot()
## dexp.3gr.ri.m <- stan_model(file="dexp_3groups_dexp_ri.stan")
load("dexp_3groups_dexp_ri.rds")
## dexp.gr.dexp.ri.m <- stan_model(file="dexp_gr_dexp_ri.stan")
load("dexp_gr_dexp_ri.rds")
## save(gt, cst, cat, cc, CSTs, file="hmp_shedding.rda")
load("hmp_shedding.rda")
## save(gt2, cst2, Lb.cst2, cat2, gt.noBV, gt.sBV, gt.aBV, file="hmp_shedding_v2.rda")
load("hmp_shedding_v2.rda")
cc <- gt$count
cc2 <- gt2$count
## checking out distribution structure of cc2
myHist(cc)
abline(v=50)
myHist(log10(cc))
abline(v=log10(50))
q <- qqPlot(log10(cc), distribution="norm")
qqPlot(log10(cc[cc<=50]), distribution="norm")
qqPlot(log10(cc[cc>50]), distribution="norm")
##
## plotting cell counts within BV groups stratified by Lb-CSTs
##
file <- "../pics/cell_count_by_gr_LbCST_hists.pdf"
pdf(file, width=9, height=6)
op <- par(mfrow=c(2,3), mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
for ( cst.type in c("IV","Lb") )
{
for ( BV.type in c("noBV", "aBV", "sBV") )
{
idx <- Lb.cst2==cst.type & cat2==BV.type
myHist(cc2[idx], main=paste(cst.type,BV.type), xlab="")
}
}
par(op)
dev.off()
file
cc2.list <- list()
for ( cst.type in c("IV","Lb") )
{
for ( BV.type in c("noBV", "aBV", "sBV") )
{
idx <- Lb.cst2==cst.type & cat2==BV.type
id <- paste0(cst.type,"::",BV.type)
cc2.list[[id]] <- cc2[idx]
}
}
file <- "../pics/cell_count_by_gr_LbCST_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(cc2.list, main="")
par(op)
dev.off()
file
##
## no stratification by CST
##
cc.list <- list()
cc.subj.list <- list()
log10.cc.list <- list()
log10.cc.subj.list <- list()
for ( BV.type in c("noBV", "aBV", "sBV") )
{
idx <- cat==BV.type
cc.list[[BV.type]] <- cc[idx]
log10.cc.list[[BV.type]] <- log10(cc[idx])
y <- aggregate(x = cc[idx], by = list(gt$subjID[idx]), FUN = "median")
cc.subj.list[[BV.type]] <- y$x
log10.cc.subj.list[[BV.type]] <- log10(y$x)
}
file <- "../pics/cell_count_by_gr_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(cc.list, main="")
par(op)
dev.off()
file
file <- "../pics/subj_cell_count_by_gr_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(cc.subj.list, main="")
par(op)
dev.off()
file
## after log transform
file <- "../pics/log10_cell_count_by_gr_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.list, main="", dx=0.05)
par(op)
dev.off()
file
file <- "../pics/log10_subj_cell_count_by_gr_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.subj.list, main="")
par(op)
dev.off()
file
##
## comparison of median cell counts between BV groups - 3 way comparison
##
m.dat <- list(y=log10(cc),
N=length(cc),
gr=cat.n,
nGr=3,
subjID=as.integer(factor(gt$subjID)),
nSubj=length(unique(gt$subjID)))
str(m.dat)
nItr <- 5000
thin <- 5
nChains <- 4
## fit.raw <- fit
fit <- sampling(dexp.3gr.ri.m, data=m.dat, iter=nItr, thin=thin, chains=nChains, cores=nChains)#, control = list(adapt_delta = 0.99, max_treedepth = 15))# init=ini)
d1.ci <- summary(fit, pars=c("delta1"), probs=c(0.025,0.975))$summary
d2.ci <- summary(fit, pars=c("delta2"), probs=c(0.025,0.975))$summary
d3.ci <- summary(fit, pars=c("delta3"), probs=c(0.025,0.975))$summary
r1.1 <- get.stan.pval(fit,"delta1[1]")
r1.2 <- get.stan.pval(fit,"delta1[2]")
pvals1 <- c(r1.1$pval, r1.2$pval)
r2.1 <- get.stan.pval(fit,"delta2[1]")
r2.2 <- get.stan.pval(fit,"delta2[2]")
pvals2 <- c(r2.1$pval, r2.2$pval)
r3.1 <- get.stan.pval(fit,"delta3[1]")
r3.2 <- get.stan.pval(fit,"delta3[2]")
pvals3 <- c(r3.1$pval, r3.2$pval)
(t1 <- cbind(d1.ci[,c(1,4,5)], pvals1))
## mean 2.5% 97.5% pvals1
## delta1[1] -0.4046618 -0.5005609 -0.3100666 2.220446e-16
## delta1[2] 0.2647050 0.1756154 0.3579456 7.205353e-09
tblTexFile <- "../docs/Tables/log10_cell_count_noBVref.tex"
tblLabel <- "cc.noBV.ref:tbl"
lt <- latex(formatC(t1, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
(t2 <- cbind(d2.ci[,c(1,4,5)], pvals2))
## mean 2.5% 97.5% pvals2
## delta2[1] 0.4046618 0.3100666 0.5005609 2.385188e-16
## delta2[2] 0.6693668 0.5515170 0.7828199 4.505485e-33
tblTexFile <- "../docs/Tables/log10_cell_count_aBVref.tex"
tblLabel <- "cc.aBV.ref:tbl"
lt <- latex(formatC(t2, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
(t3 <- cbind(d3.ci[,c(1,4,5)], pvals3))
## mean 2.5% 97.5% pvals3
## delta3[1] -0.2647050 -0.3579456 -0.1756154 7.205353e-09
## delta3[2] -0.6693668 -0.7828199 -0.5515170 0.000000e+00
tblTexFile <- "../docs/Tables/log10_cell_count_sBVref.tex"
tblLabel <- "cc.sBV.ref:tbl"
lt <- latex(formatC(t3, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
summary(fit, pars=c("mu"), probs=c(0.025,0.975))$summary
## mean se_mean sd 2.5% 97.5% n_eff Rhat
## mu[1] 29.73164 0.1190146 4.614073 20.637573 38.41938 1503.0331 1.0012572
## mu[2] 15.55229 0.1516282 4.055113 7.279989 23.04669 715.2301 1.0017392
## mu[3] 3.37011 0.1119280 5.022299 -6.900129 13.43530 2013.3885 0.9996933
## mean se_mean sd 2.5% 97.5% n_eff Rhat
## mu[1] 1.936378 0.0006457579 0.02709602 1.882536 1.987478 1760.644 1.0010769
## mu[2] 1.531716 0.0009624521 0.04217905 1.450585 1.614276 1920.592 0.9989891
## mu[3] 2.201083 0.0009714801 0.04161036 2.116588 2.280788 1834.573 1.0011382
describe(log10(cc.list[["noBV"]]))
describe(log10(cc.list[["aBV"]]))
describe(log10(cc.list[["sBV"]]))
## n missing distinct Info Mean Gmd .05 .10 .25 .50
## 104 0 78 1 90.2 52.34 19.30 27.30 57.75 90.50
## .75 .90 .95
## 126.00 146.00 152.70
## lowest : 11 12 13 19 21, highest: 153 158 186 205 232
describe(cc.subj.list[["noBV"]])
## n missing distinct Info Mean Gmd .05 .10 .25 .50
## 97 0 76 1 90.84 53.66 19.0 26.6 57.0 91.0
## .75 .90 .95
## 127.0 147.2 153.0
## lowest : 11 12 13 19 21, highest: 153 158 186 205 232
##
## The same but in CST IV samples
##
idx <- Lb.cst2=="IV"
log10.cc.IV.list <- list()
log10.cc.IV.subj.list <- list()
for ( BV.type in c("noBV", "aBV", "sBV") )
{
idx <- Lb.cst2=="IV" & cat2==BV.type
log10.cc.IV.list[[BV.type]] <- log10(cc2[idx])
y <- aggregate(x = cc2[idx], by = list(gt2$subjID[idx]), FUN = "median")
log10.cc.IV.subj.list[[BV.type]] <- log10(y$x)
}
file <- "../pics/log10_cell_count_in_CST_IV_by_gr_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.IV.list, main="")
par(op)
dev.off()
file
file <- "../pics/log10_subj_cell_count_in_CST_IV_by_gr_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.IV.subj.list, main="")
par(op)
dev.off()
file
##
## CST IV
##
gt2.IV <- gt2[Lb.cst2=="IV",]
m.dat <- list(y=log10(gt2.IV$count),
N=length(gt2.IV$count),
gr=cat2.n[Lb.cst2=="IV"],
nGr=3,
subjID=as.integer(factor(gt2.IV$subjID)),
nSubj=length(unique(gt2.IV$subjID)))
str(m.dat)
nItr <- 5000
thin <- 5
nChains <- 4
## fit.raw <- fit
fit.IV <- sampling(dexp.3gr.ri.m, data=m.dat, iter=nItr, thin=thin, chains=nChains, cores=nChains)#, control = list(adapt_delta = 0.99, max_treedepth = 15))# init=ini)
d1.IV.ci <- summary(fit.IV, pars=c("delta1"), probs=c(0.025,0.975))$summary
d2.IV.ci <- summary(fit.IV, pars=c("delta2"), probs=c(0.025,0.975))$summary
d3.IV.ci <- summary(fit.IV, pars=c("delta3"), probs=c(0.025,0.975))$summary
r1.1 <- get.stan.pval(fit.IV,"delta1[1]")
r1.2 <- get.stan.pval(fit.IV,"delta1[2]")
pvals1 <- c(r1.1$pval, r1.2$pval)
r2.1 <- get.stan.pval(fit.IV,"delta2[1]")
r2.2 <- get.stan.pval(fit.IV,"delta2[2]")
pvals2 <- c(r2.1$pval, r2.2$pval)
r3.1 <- get.stan.pval(fit.IV,"delta3[1]")
r3.2 <- get.stan.pval(fit.IV,"delta3[2]")
pvals3 <- c(r3.1$pval, r3.2$pval)
(t1 <- cbind(d1.IV.ci[,c(1,4,5)], pvals1))
tblTexFile <- "../docs/Tables/log10_cell_count_CST_IV_noBVref.tex"
tblLabel <- "cc.noBV.ref:tbl"
lt <- latex(formatC(t1, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
(t2 <- cbind(d2.IV.ci[,c(1,4,5)], pvals2))
tblTexFile <- "../docs/Tables/log10_cell_count_CST_IV_aBVref.tex"
tblLabel <- "cc.aBV.ref:tbl"
lt <- latex(formatC(t2, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
(t3 <- cbind(d3.IV.ci[,c(1,4,5)], pvals3))
tblTexFile <- "../docs/Tables/log10_cell_count_CST_IV_sBVref.tex"
tblLabel <- "cc.sBV.ref:tbl"
lt <- latex(formatC(t3, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
##
## Lb CSTs
##
log10.cc.Lb.list <- list()
log10.cc.Lb.subj.list <- list()
for ( BV.type in c("noBV", "aBV", "sBV") )
{
idx <- Lb.cst2=="Lb" & cat2==BV.type
log10.cc.Lb.list[[BV.type]] <- log10(cc2[idx])
y <- aggregate(x = cc2[idx], by = list(gt2$subjID[idx]), FUN = "median")
log10.cc.Lb.subj.list[[BV.type]] <- log10(y$x)
}
file <- "../pics/log10_cell_count_in_CST_Lb_by_gr_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.Lb.list, main="")
par(op)
dev.off()
file
file <- "../pics/log10_subj_cell_count_in_CST_Lb_by_gr_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.Lb.subj.list, main="")
par(op)
dev.off()
file
gt2.Lb <- gt2[Lb.cst2=="Lb",]
m.dat <- list(y=log10(gt2.Lb$count),
N=length(gt2.Lb$count),
gr=cat2.n[Lb.cst2=="Lb"],
nGr=3,
subjID=as.integer(factor(gt2.Lb$subjID)),
nSubj=length(unique(gt2.Lb$subjID)))
str(m.dat)
nItr <- 5000
thin <- 5
nChains <- 4
fit.Lb <- sampling(dexp.3gr.ri.m, data=m.dat, iter=nItr, thin=thin, chains=nChains, cores=nChains)#, control = list(adapt_delta = 0.99, max_treedepth = 15))# init=ini)
d1.Lb.ci <- summary(fit.Lb, pars=c("delta1"), probs=c(0.025,0.975))$summary
d2.Lb.ci <- summary(fit.Lb, pars=c("delta2"), probs=c(0.025,0.975))$summary
d3.Lb.ci <- summary(fit.Lb, pars=c("delta3"), probs=c(0.025,0.975))$summary
r1.1 <- get.stan.pval(fit.Lb,"delta1[1]")
r1.2 <- get.stan.pval(fit.Lb,"delta1[2]")
pvals1 <- c(r1.1$pval, r1.2$pval)
r2.1 <- get.stan.pval(fit.Lb,"delta2[1]")
r2.2 <- get.stan.pval(fit.Lb,"delta2[2]")
pvals2 <- c(r2.1$pval, r2.2$pval)
r3.1 <- get.stan.pval(fit.Lb,"delta3[1]")
r3.2 <- get.stan.pval(fit.Lb,"delta3[2]")
pvals3 <- c(r3.1$pval, r3.2$pval)
(t1 <- cbind(d1.Lb.ci[,c(1,4,5)], pvals1))
tblTexFile <- "../docs/Tables/log10_cell_count_CST_Lb_noBVref.tex"
tblLabel <- "cc.noBV.ref:tbl"
lt <- latex(formatC(t1, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
(t2 <- cbind(d2.Lb.ci[,c(1,4,5)], pvals2))
tblTexFile <- "../docs/Tables/log10_cell_count_CST_Lb_aBVref.tex"
tblLabel <- "cc.aBV.ref:tbl"
lt <- latex(formatC(t2, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
(t3 <- cbind(d3.Lb.ci[,c(1,4,5)], pvals3))
tblTexFile <- "../docs/Tables/log10_cell_count_CST_Lb_sBVref.tex"
tblLabel <- "cc.sBV.ref:tbl"
lt <- latex(formatC(t3, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowlabel="", caption="",
where='!htp',vbar=FALSE)
##
## within group CST IV vs Lb comparisons of log cell counts
##
##
## noBV
##
table(gt.noBV$Lb.cst)
## IV Lb
## 31 70
log10.cc.noBV.list <- list()
for ( cst.type in c("IV", "Lb") )
{
idx <- gt.noBV$Lb.cst==cst.type
log10.cc.noBV.list[[cst.type]] <- log10(gt.noBV$count[idx])
}
file <- "../pics/log10_cell_count_in_noBV_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.noBV.list, main="")
par(op)
dev.off()
file
m.dat <- list(y=log10(gt.noBV$count),
N=length(gt.noBV$count),
gr=ifelse(gt.noBV$Lb.cst=="Lb",2,1),# Lb is the reference class
nGr=2,
subjID=as.integer(factor(gt.noBV$subjID)),
nSubj=length(unique(gt.noBV$subjID)))
str(m.dat)
nItr <- 5000
thin <- 5
nChains <- 4
fit <- sampling(dexp.gr.dexp.ri.m, data=m.dat, iter=nItr, thin=thin, chains=nChains, cores=nChains)
(d.ci <- summary(fit, pars=c("delta"), probs=c(0.025,0.975))$summary)
r <- get.stan.pval(fit,"delta[1]")
(pval <- r$pval)
(et <- as.table(c(d.ci[1,c(1,4,5)], pval)))
names(et)[4] <- "p-val"
names(et)[1] <- "$\\Delta$"
et <- rbind(et)
tblTexFile <- "../docs/Tables/cmp_median_log10_cell_counts_in_noBV_bw_Lb_dom_CST_type.tex"
tblLabel <- "cc.noBV:tbl"
lt <- latex(formatC(et, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowname=NULL, caption="",
where='!htp',vbar=FALSE, latexVerbatim=T)
##
## aBV
##
table(gt.aBV$Lb.cst)
## IV Lb
## 62 4
log10.cc.aBV.list <- list()
for ( cst.type in c("IV", "Lb") )
{
idx <- gt.aBV$Lb.cst==cst.type
log10.cc.aBV.list[[cst.type]] <- log10(gt.aBV$count[idx])
}
file <- "../pics/log10_cell_count_in_aBV_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.aBV.list, main="")
par(op)
dev.off()
file
m.dat <- list(y=log10(gt.aBV$count),
N=length(gt.aBV$count),
gr=ifelse(gt.aBV$Lb.cst=="Lb",2,1),# Lb is the reference class
nGr=2,
subjID=as.integer(factor(gt.aBV$subjID)),
nSubj=length(unique(gt.aBV$subjID)))
str(m.dat)
nItr <- 5000
thin <- 5
nChains <- 4
fit <- sampling(dexp.gr.dexp.ri.m, data=m.dat, iter=nItr, thin=thin, chains=nChains, cores=nChains)
(d.ci <- summary(fit, pars=c("delta"), probs=c(0.025,0.975))$summary)
r <- get.stan.pval(fit,"delta[1]")
(pval <- r$pval)
(et <- as.table(c(d.ci[1,c(1,4,5)], pval)))
names(et)[4] <- "p-val"
names(et)[1] <- "$\\Delta$"
et <- rbind(et)
tblTexFile <- "../docs/Tables/cmp_median_log10_cell_counts_in_aBV_bw_Lb_dom_CST_type.tex"
tblLabel <- "cc.aBV:tbl"
lt <- latex(formatC(et, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowname=NULL, caption="",
where='!htp',vbar=FALSE, latexVerbatim=T)
##
## sBV
##
table(gt.sBV$Lb.cst)
## IV Lb
## 13 4
log10.cc.sBV.list <- list()
for ( cst.type in c("IV", "Lb") )
{
idx <- gt.sBV$Lb.cst==cst.type
log10.cc.sBV.list[[cst.type]] <- log10(gt.sBV$count[idx])
}
file <- "../pics/log10_cell_count_in_sBV_bpplots.pdf"
pdf(file, width=9, height=6)
op <- par(mar=c(3.5, 3.4, 1.5, 1.5), mgp=c(2.25,0.6,0),tcl = -0.3)
bp.plot(log10.cc.sBV.list, main="")
par(op)
dev.off()
file
m.dat <- list(y=log10(gt.sBV$count),
N=length(gt.sBV$count),
gr=ifelse(gt.sBV$Lb.cst=="Lb",2,1),# Lb is the reference class
nGr=2,
subjID=as.integer(factor(gt.sBV$subjID)),
nSubj=length(unique(gt.sBV$subjID)))
str(m.dat)
nItr <- 5000
thin <- 5
nChains <- 4
fit <- sampling(dexp.gr.dexp.ri.m, data=m.dat, iter=nItr, thin=thin, chains=nChains, cores=nChains)
(d.ci <- summary(fit, pars=c("delta"), probs=c(0.025,0.975))$summary)
r <- get.stan.pval(fit,"delta[1]")
(pval <- r$pval)
(et <- as.table(c(d.ci[1,c(1,4,5)], pval)))
names(et)[4] <- "p-val"
names(et)[1] <- "$\\Delta$"
et <- rbind(et)
tblTexFile <- "../docs/Tables/cmp_median_log10_cell_counts_in_sBV_bw_Lb_dom_CST_type.tex"
tblLabel <- "cc.sBV:tbl"
lt <- latex(formatC(et, digits=2), file=tblTexFile, label=tblLabel,
caption.loc='bottom',longtable=FALSE, rowname=NULL, caption="",
where='!htp',vbar=FALSE, latexVerbatim=T)