In ahopki14/tcrSeqR: Tools for analyzing data from Adaptive's ImmunoSeq platform

Comparison of Studies

Created r format(Sys.Date(),"%m/%d/%y")

mycols <- colors()[c(28,128,81,256)]
opts_knit$set(base.dir = '~/Documents/emj/ImmunoseqResults/new/')
opts_chunk$set(echo=FALSE)

require(immunoSeqR)
require(ggplot2)
require(gridExtra)
require(reshape2)
require(knitr)
require(plyr)
require(rmarkdown)
r <- function(){render('~/Documents/emj/ImmunoseqResults/immunoSeqR/scripts/rmd/comparison.Rmd',
output_file='~/Documents/emj/ImmunoseqResults/new/comparison.pdf',
intermediates_dir='~/Documents/emj/ImmunoseqResults/new/')}
neoadjuvant <- readRDS('~/Documents/emj/ImmunoseqResults/data/neoadjuvant/plot_ds.Rds')

# Load all the data
adjuvant <- readRDS('~/Documents/emj/ImmunoseqResults/data/adjuvant/plot_ds.Rds')
ipi <- readRDS('~/Documents/emj/ImmunoseqResults/data/ipi/plot_ds.Rds')
sbrt <- readRDS('~/Documents/emj/ImmunoseqResults/data/sbrt/plot_ds.Rds')
baseline <- readRDS('~/Documents/emj/ImmunoseqResults/data/baseline/plot_ds.Rds')
pd1 <- readRDS('~/Documents/emj/ImmunoseqResults/data/pd1/plot_ds.Rds')

#merge into one
ipi$experiment <- rep('anti-CTLA4',nrow(ipi))
pd1$experiment <- rep('anti-PD1',nrow(pd1))
plot_ds <- rbind.fill(neoadjuvant,adjuvant,ipi,sbrt,pd1,baseline)
plot_ds$experiment <- factor(plot_ds$experiment,levels=unique(plot_ds$experiment))

tds <- plot_ds
tds$fac <- paste0(plot_ds$experiment,' (',plot_ds$type,')')
tds$fac <- factor(tds$fac,levels=unique(tds$fac)[c(1,2,3,4,5,6,8,9,7,12,11,10,17,16,14,15,13)])
w <- which(tds$experiment=='Healthy Donor')
tds <- tds[-w,]
g <- iseqr_plot_factor(tds,'Clonality','fac') + theme(axis.text=element_text(angle=90,hjust=1))
h <- iseqr_plot_factor(tds,'Richness','fac') + theme(axis.text=element_text(angle=90,hjust=1))  
i <- iseqr_plot_factor(tds,'Total Sequences','fac') + theme(axis.text=element_text(angle=90,hjust=1))  
g  
cat('\n\n')
h  
cat('\n\n')
i

Change in Metrics

Using POST1 and POSTSBRT

w <- c(which(plot_ds$type=='POST'),which(plot_ds$type=='POST1'),which(plot_ds$type=='POSTSBRT'))
tds <- plot_ds[w,]

g <- list()
m <- names(tds)[15:17]
for(a in 1:3){
  g[[a]] <- iseqr_plot_factor(tds,m[a],'experiment') + 
    geom_hline(yintercept=0,alpha=0.1) + 
    theme(axis.text=element_text(angle=90,hjust=1))
}
do.call(grid.arrange,c(g,ncol=3))

Using POST3 and POSTFOLF

w <- c(which(plot_ds$type=='POST'),which(plot_ds$type=='POST3'),which(plot_ds$type=='POSTFOLF'))
tds <- plot_ds[w,]

g <- list()
m <- names(tds)[15:17]
for(a in 1:3){
  g[[a]] <- iseqr_plot_factor(tds,m[a],'experiment') + 
    geom_hline(yintercept=0,alpha=0.1) +
        theme(axis.text=element_text(angle=90,hjust=1))

}
do.call(grid.arrange,c(g,ncol=3))

Expanded Clones

w <- c(which(plot_ds$type=='POST'),which(plot_ds$type=='POST1'),which(plot_ds$type=='POSTSBRT'))
tds <- plot_ds[w,]

m <- names(tds)[18]
g <- iseqr_plot_factor(tds,m,'experiment') +
    theme(axis.text=element_text(angle=90,hjust=1))
g

\clearpage

At POST3:
J0834 had 3 rounds of Ipi +/- GVAX

\begin{center} \begin{tabular}{l l} Arm 1 & Ipilimumab \ Arm 2 & Ipilimumab + Cy + GVAX \ \end{tabular} \end{center}

J14113 had 2 rounds of GVAX +/- PD1 and one round of LM

\begin{center} \begin{tabular}{l l} Arm A & GVAX + LM + Nivolumab \ Arm B & GVAX + LM \ \end{tabular} \end{center}

tds <- plot_ds[plot_ds$experiment=='anti-CTLA4' | plot_ds$experiment=='anti-PD1',]
g <- iseqr_plot_factor(tds,'Clonality','experiment','PRE')
h <- iseqr_plot_factor(tds,'Clonality','experiment','POST3')
g <- g + geom_point(aes(color=arm)) + scale_color_manual(values=mycols)
h <- h + geom_point(aes(color=arm)) + scale_color_manual(values=mycols)
grid.arrange(g,h,ncol=2)

g <- iseqr_plot_factor(tds,'Number of Expanded Clones vs PRE','experiment','POST3')
h <- iseqr_plot_factor(tds,"Log2 Fold Change in Clonality",'experiment','POST3')
g <- g + geom_point(aes(color=arm)) + scale_color_manual(values=mycols)
h <- h + geom_point(aes(color=arm)) + scale_color_manual(values=mycols)
grid.arrange(g,h,ncol=2)