Created r format(Sys.Date(),"%m/%d/%y")
require(rmarkdown) require(immunoSeqR) require(ggplot2) require(gridExtra) require(knitr) require(reshape2) first_run <- FALSE verbose=FALSE r <- function(){render('~/Documents/emj/ImmunoseqResults/immunoSeqR/scripts/rmd/sbrt.Rmd', output_file='~/Documents/emj/ImmunoseqResults/new/sbrt.pdf')}
ds <- readRDS('~/Documents/emj/ImmunoseqResults/data/sbrt/ds_agg.Rds') load('~/Documents/emj/ImmunoseqResults/data/sbrt/sum.ds.Rda') dict <- readRDS('~/Documents/emj/ImmunoseqResults/data/sbrt/dict.Rds') plot_ds <- readRDS('~/Documents/emj/ImmunoseqResults/data/sbrt/plot_ds.Rds') stats <- readRDS('~/Documents/emj/ImmunoseqResults/data/sbrt/stats.Rds') load('~/Documents/emj/ImmunoseqResults/data/sbrt/olm.Rda')
The data set contains r sum.ds$nsamp
samples, of r sum.ds$ntypes
types.
There are r sum.ds$unique.tcr
unique TCRs, collapsed from r sum.ds$total.tcr
distinct
productive CDR3 sequences. This represents an average synonymity of r sum.ds$avg.syn
.
The maximum synonymity (in the parent data set) was r sum.ds$max.syn
(clone sequence r
sum.ds$max.syn.aa
).\par
The available metadata fields in the dictionary are: r paste(names(dict)[-1],collapse=', ')
. The statistics computed are r paste(names(stats)[names(stats)!='fn'],collapse=', ')
.
\clearpage
rownames(mm) <- paste0(dict$patient,dict$type) colnames(mm) <- rownames(mm) #w <- c(which(dict$response=='NR'),which(dict$response=='R')) tdict <- dict[,] tmm <- mm tmm.m <- melt(tmm) g <- ggplot(tmm.m,aes(x=Var1,y=Var2)) + geom_tile(aes(fill=value)) + coord_fixed() + scale_fill_gradient(low='white',high='black')+ theme(axis.text=element_text(size=5,color='black'), axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+ xlab('') + ylab('') + ggtitle('Morisita Overlap') g
w <- grep('Number of Expanded',names(plot_ds)) g <- iseqr_plot_factor(plot_ds,names(plot_ds)[w],'arm',type='POSTSBRT') + xlab('Arm') g w <- grep('Number of Expanded',names(plot_ds)) g <- iseqr_plot_factor(plot_ds,names(plot_ds)[w],'arm',type='POSTFOLF') + xlab('Arm') f
resp <- sapply(names(out2$num_exp),FUN=iseqr_lookup,dict=tdict,o_col='os.tert') pt <- sapply(names(out2$num_exp),FUN=iseqr_lookup,dict=tdict,o_col='ptv80.tert') arm <- sapply(names(out2$num_exp),FUN=iseqr_lookup,dict=tdict,o_col='arm') df <- data.frame(patient=names(out2$num_exp),response=resp,arm=arm,pt=pt,num_exp=out2$num_exp) g <- iseqr_plot_factor(df,'num_exp','response',NA) g <- g + ylab('Number of Expanded Clones') + xlab('Survival Tertile') f <- iseqr_plot_factor(df,'num_exp','arm',NA) + xlab('Arm') f <- f + ylab('Number of Expanded Clones') i <- iseqr_plot_factor(df,'num_exp','pt',NA) + xlab('PTV80 Tertile') i <- i + ylab('Number of Expanded Clones') grid.arrange(f,g,i,ncol=3)
#put sum of seqs at the end stats <- stats[,c(1,3,2,4)] plot_ds <- merge(dict,stats)
metrics <- names(stats[names(stats)!='fn']) types <- levels(plot_ds$type) l <- vector('list') for(b in metrics){ l[[b]] <- iseqr_plot_factor(plot_ds,b,'type',NA) + theme(axis.text=element_text(size=5)) } do.call(grid.arrange,c(l,ncol=length(types)))
for(a in metrics){ print(kable(iseqr_summarize(plot_ds,'type',metric=a), format='markdown', digits=3, align='c')) }
\clearpage
x_val <- 'arm' metrics <- names(stats[names(stats)!='fn']) types <- levels(plot_ds$type) for(b in metrics){ l <- vector('list') for(a in types){ l[[a]] <- iseqr_plot_factor(plot_ds,b,x_val,a) } do.call(grid.arrange,c(l,ncol=length(types))) }
\clearpage
x_val <- 'os' metrics <- names(stats[names(stats)!='fn']) types <- levels(plot_ds$type) for(b in metrics){ l <- vector('list') for(a in types[types!='PDAC']){ l[[a]] <- iseqr_plot_metrics(plot_ds[!is.na(plot_ds$os),],b,x_val,a) + theme(axis.text=element_text(size=5)) } do.call(grid.arrange,c(l,ncol=length(types))) cat(' \n') }
x_val <- 'os.tert' metrics <- names(stats[names(stats)!='fn']) types <- levels(plot_ds$type) for(b in metrics){ l <- vector('list') for(a in types){ l[[a]] <- iseqr_plot_factor(plot_ds,b,x_val,a) } do.call(grid.arrange,c(l,ncol=length(types))) }
\clearpage
x_val <- 'ptv80' metrics <- names(stats[names(stats)!='fn']) types <- levels(plot_ds$type) for(b in metrics){ l <- vector('list') for(a in types[types!='PDAC']){ l[[a]] <- iseqr_plot_metrics(plot_ds[!is.na(plot_ds$ptv80),],b,x_val,a) + theme(axis.text=element_text(size=5)) } do.call(grid.arrange,c(l,ncol=length(types))) cat(' \n') }
x_val <- 'ptv80.tert' metrics <- names(stats[names(stats)!='fn']) types <- levels(plot_ds$type) for(b in metrics){ l <- vector('list') for(a in types){ l[[a]] <- iseqr_plot_factor(plot_ds,b,x_val,a) } do.call(grid.arrange,c(l,ncol=length(types))) }
\clearpage
x_val <- 'cells' metrics <- names(stats[names(stats)!='fn']) types <- levels(plot_ds$type) for(b in metrics){ l <- vector('list') for(a in types[types!='PDAC']){ l[[a]] <- iseqr_plot_metrics(plot_ds[!is.na(plot_ds$cells),],b,x_val,a) + theme(axis.text=element_text(size=5)) } do.call(grid.arrange,c(l,ncol=length(types))) cat(' \n') }
x_val <- 'age' metrics <- names(stats[names(stats)!='fn']) types <- levels(plot_ds$type) for(b in metrics){ l <- vector('list') for(a in types[types!='PDAC']){ l[[a]] <- iseqr_plot_metrics(plot_ds[!is.na(plot_ds$cells),],b,x_val,a) + theme(axis.text=element_text(size=5)) } do.call(grid.arrange,c(l,ncol=length(types))) cat(' \n') }
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