From 9b92e5858519260e800a64c75bb8ee772480d220 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Friederike=20D=C3=BCndar?= Date: Wed, 9 Feb 2022 17:38:06 +0100 Subject: [PATCH] code for the figures --- FiguresForManuscript.Rmd | 429 +++++++++++++++++ FiguresForManuscript.html | 981 ++++++++++++++++++++++++++++++++++++++ FiguresForManuscript.pdf | Bin 0 -> 454398 bytes 3 files changed, 1410 insertions(+) create mode 100644 FiguresForManuscript.Rmd create mode 100644 FiguresForManuscript.html create mode 100644 FiguresForManuscript.pdf diff --git a/FiguresForManuscript.Rmd b/FiguresForManuscript.Rmd new file mode 100644 index 0000000..6162358 --- /dev/null +++ b/FiguresForManuscript.Rmd @@ -0,0 +1,429 @@ +--- +title: Code for generating the data underlying the scRNA-seq-based figures in the + SIFT-seq paper +author: "Friederike Dündar" +date: "2/7/2022; updated `r Sys.Date()`" +output: + pdf_document: + toc: yes + toc_depth: '5' + html_document: + code_folding: hide + toc: yes + toc_depth: 5 + theme: yeti + highlight: tango +editor_options: + chunk_output_type: console +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) +``` + +```{r cache=FALSE, message=FALSE, warning=FALSE} +library(SingleCellExperiment); library(data.table);library(magrittr) +library(ggplot2); theme_set(theme_bw(base_size = 16) ) +library(ggrepel); library(patchwork) +``` + + +For each patient's data set (0606T1, 21LT2, MR4050/MK_02), there's a separate R +package that contains the processed data as well as the code details of how that +data was generated (check out the vignettes for that). + +Here, we show how the figures shown in the SIFT-seq manuscript were generated although the +final-final versions that made it into the paper were also done with GraphPadPrism for +reasons of legibility. + +```{r load_data} +## load winners +data("cdrs0606T1", package = "Klebanoff0606T1") +data("cdrs21LT2", package = "Klebanoff21LT2") + +## load SingleCellExperiment objects +sce.06 <- Klebanoff0606T1::load_0606T1shared() +Klebanoff0606T1::load_DE_results() #delist.both +de.06 <- delist.both + +sce.21 <- Klebanoff21LT2::load_21LT2shared() +Klebanoff21LT2::load_DE_results() #delist.both +de.21 <- delist.both + +rm(delist.both); invisible(gc()) +``` + +## Fig 1 + +- (B) and (E): "tgrams" of IFNG logFC for 21LT2 and 0606T1, respectively + +- (C) and (D): volcano plots and violin plots were done in GraphPad Prism, see the +xlsx and txt files in the data directory + +```{r} +t21 <- Klebanoff0606T1::prep_data4tgram(sce.21, + which_clonotypes = unique(sce.21$id), + DEresult_list = de.21, goi = "IFNG", + additional_colData = c("antigen","freq_per_Sample", "freq_across_all", + "fit_for_test", "Patient")) + +t06 <- Klebanoff0606T1::prep_data4tgram(sce.06, + which_clonotypes = unique(sce.06$id), + DEresult_list = de.06, goi = "IFNG", + additional_colData = c("antigen","freq_per_Sample", "freq_across_all", + "fit_for_test", "Patient")) +``` + +```{r functions} +#' Extract T test results +#' @description returns the relevant values calculated +#' by R's \code{\link{t.test()}} in the form of a data.table. +#' @return data.table with logFC value (MUT/WT), standard error, +#' lower and upper bounds of the confidence interval. +extract.tres <- function(testres){ + logFC.t = testres$estimate[2] - testres$estimate[1] #levels: WT < MUT + sde = testres$stderr + ci.low = testres$conf.int[[2]]*-1 # needed b/c WT < MUT levels + ci.up = testres$conf.int[[1]]*-1 + return(data.table(logFC.t = logFC.t, sde = sde, ci.low = ci.low, ci.up = ci.up)) +} +``` + +```{r} +# 21LT2 =================================== +## calculate CI +test21res <- t21[ + fit_for_test == TRUE , + extract.tres(t.test(logcounts~antigen)), + by = c("id", "gene_name")] + +## summarize everything in one dt +t21.summ <- t21[, -c("cell", "cdr3s_aa","logcounts", "Sample","antigen", "freq_per_Sample"), + with=FALSE] %>% + unique %>% test21res[., on = "id"] %>% unique +t21.summ[ is.na(ci.low), ci.low:=0] +t21.summ[ is.na(ci.up), ci.up:=0] + + +# 0606T1 ==================================== +## calculate CI etc. +test06res <- t06[ + fit_for_test == TRUE , + extract.tres(t.test(logcounts~antigen)), + by = c("id", "gene_name")] + +## summarize +t06.summ <- t06[, -c("cell", "cdr3s_aa","logcounts", "Sample","antigen", "freq_per_Sample"), + with=FALSE] %>% + unique %>% test06res[., on = "id"] %>% unique +t06.summ[ is.na(ci.low), ci.low:=0] +t06.summ[ is.na(ci.up), ci.up:=0] + +t21.summ0 <- copy(t21.summ) +t21.summ0[, logFC := ifelse(logFC.t > 0, logFC.t, 0)] +t21.summ0[, logFC := ifelse(is.na(logFC.t), 0, logFC)] +t21.summ0[, ci.low0 := ifelse(logFC.t >0, ci.low, 0)] +t21.summ0[, ci.up0 := ifelse(logFC.t >0, ci.up, 0)] +setorder(t21.summ0, -freq_across_all) +t21.summ0$id.sort <- factor(t21.summ0$id, + levels = t21.summ0$id, ordered = TRUE) + +t06.summ0 <- copy(t06.summ) +t06.summ0[, logFC := ifelse(logFC.t > 0, logFC.t, 0)] +t06.summ0[, logFC := ifelse(is.na(logFC.t), 0, logFC)] +t06.summ0[, ci.low0 := ifelse(logFC.t >0, ci.low, 0)] +t06.summ0[, ci.up0 := ifelse(logFC.t >0, ci.up, 0)] +setorder(t06.summ0, -freq_across_all) +t06.summ0$id.sort <- factor(t06.summ0$id, levels = t06.summ0$id, ordered = TRUE) +``` + +```{r} +thresh <- 2 +``` + +```{r tgram_21LT2, fig.width = 13.75} +ystar <- max(t21.summ0$logFC) + 2 + +ggplot(t21.summ0, aes(x = id.sort, y = logFC)) + + geom_hline(yintercept = 0) + + geom_hline(yintercept = thresh, linetype = "dashed", color = "grey50") + + ## points ------------------------------- + geom_point(size = 4, aes(color = freq_across_all)) + + scale_color_gradientn( + name = "clonotype\nfrequency", + colours = c("blue","grey85","firebrick3")) + + ## error bars --------------------------- + geom_errorbar(data = t21.summ0[logFC >= thresh], + aes(ymin=ci.low0, ymax=ci.up0), + width=.1, color="grey45") + + facet_grid(. ~ Patient, scales="free_x", space = "free_x") + + ## add asterisks ----------------------- + geom_text(inherit.aes = FALSE, + aes(x = id.sort, y = ystar, label=star), + colour="black", size=8)+ + geom_text(data = t21.summ0[star != ""], + inherit.aes = FALSE, + aes(x = id.sort, label = id, y = ystar+.5)) + + ## grid appearance etc. --------------- + theme(panel.grid.minor.x=element_blank(), + panel.grid.major.x=element_blank(), + axis.ticks = element_blank(), + axis.text.x = element_blank()) + + xlab("clonotypes") + + ylab("logFC IFNG expression [MUT/WT]") +``` + +```{r tgram_0606T1, fig.width = 28, fig.height = 6.5} +ystar <- max(t06.summ0$logFC) + 2.5 ## DF + +ggplot(t06.summ0, aes(x = id.sort, y = logFC)) + ## DF + geom_hline(yintercept = 0) + + geom_hline(yintercept = thresh, linetype = "dashed", color = "grey50") + + ## points ------------------------------- + geom_point(size = 4, aes(color = freq_across_all)) + + scale_color_gradientn( + name = "clonotype\nfrequency", + colours = c("blue","grey85","firebrick3")) + + ## error bars --------------------------- + geom_errorbar(data = t06.summ0[logFC >= thresh], ## DF + aes(ymin=ci.low0, ymax=ci.up0), + width=.1, color="grey45") + + facet_grid(. ~ Patient, scales="free_x", space = "free_x") + + ## add asterisks ----------------------- + geom_text(inherit.aes = FALSE, + aes(x = id.sort, y = ystar, label=star), + colour="black", size=15)+ + geom_text(data = t06.summ0[star != ""], ## DF + inherit.aes = FALSE, size = 8, + aes(x = id.sort, label = id, y = ystar+1)) + + ## grid appearance etc. --------------- + theme(panel.grid.minor.x=element_blank(), + panel.grid.major.x=element_blank(), + axis.ticks = element_blank(), + axis.text.x = element_blank(), + legend.position = "bottom") + + xlab("clonotypes") + + ylab("logFC IFNG expression [MUT/WT]") +``` + + +## Ext Fig 1: volcano plots MUT vs. WT + +--> see the data files `DEgenes_21LT2_C18_mut_vs_wt.txt` and `DEgenes_0606T1.xlsx`. + +## Ext Fig 3 and 4: boxplots of IFNg + +>mutation-reactive TCRs can be identified by normalized IFNG signal comparisons between MUT and WT stimulated conditions + +- ExtFig 3: 21LT1 +- ExtFig 4: 0606T1: + +```{r boxplots_21LT2, fig.height = 18} +t21 <- Klebanoff0606T1::prep_data4tgram(sce.21, + which_clonotypes = unique(sce.21$id), + DEresult_list = de.21, goi = "IFNG", + additional_colData = c("antigen","freq_per_Sample", "freq_across_all", + "fit_for_test", "Patient")) +setorder(t21, freq_across_all) +t21$id <- factor(t21$id, levels = unique(t21$id), ordered = TRUE) + +ggplot(t21, aes(x = id, y = logcounts))+ + geom_point(alpha = 0, aes(color = antigen)) + + geom_tile(data=t21[star != ""], + aes(x = id, y = 1, height = Inf, width = 1), + alpha = 1, fill = "khaki1") + + ggbeeswarm::geom_quasirandom( + groupOnX = TRUE, size = 1.5, shape = 21, alpha =.7, + dodge.width = .8, aes(fill = antigen), color = "white") + + geom_boxplot(data=t21[freq_across_all > 3 ], + outlier.alpha = 0,lwd=.25, fill = NA, aes(color = antigen)) + + facet_grid(.~gene_name) + coord_flip() + + xlab("") + + theme(panel.grid = element_blank()) + + scale_fill_manual(values = c("grey65","tomato1")) + + scale_color_manual(values = c("black","black")) + + ggtitle("All clonotypes of 21LT2", + subtitle = "sorted by frequency; yellow highlights the logFC\nthat + were found to be statistically significant") + + guides(fill = guide_legend(override.aes = list(size = 3, alpha = 1) ), + color = FALSE ) +``` + + +```{r boxplots_0606, fig.height = 17} +t06 <- Klebanoff0606T1::prep_data4tgram(sce.06, + which_clonotypes = unique(sce.06$id), + DEresult_list = de.06, goi = "IFNG", + additional_colData = c("antigen","freq_per_Sample", "freq_across_all", + "fit_for_test", "Patient")) +setorder(t06, freq_across_all) +t06$id <- factor(t06$id, levels = unique(t06$id), ordered = TRUE) + +pl <- list() +ymax <- ceiling(max(t06$logcounts)) + +for(x in unique(t06$gene_name)){ + tmp <- t06[gene_name == x] + bp <- tmp[, median(logcounts), by = c("antigen","id")] %>% .[V1 > 0] %>% + .$id %>% as.character %>% unique + P <- ggplot(tmp[freq_across_all > 20], aes(x = id, y = logcounts)) + if(nrow(tmp[star != ""])>0){ + P <- P + geom_point(alpha = 0, aes(color = antigen)) + + geom_tile(data=tmp[star != ""], + aes(x = id, y = 1, height = Inf, width = 1), + alpha = 1, fill = "khaki1") + } + P <- P + ggbeeswarm::geom_quasirandom( + groupOnX = TRUE, size = 1.5, shape = 21, alpha =.7, + dodge.width = .8, aes(fill = antigen), color = "white") + + facet_grid(.~gene_name) + coord_flip(ylim = c(0,ymax)) + + geom_boxplot(data=tmp[freq_across_all > 3 & as.character(id) %in% bp], + outlier.alpha = 0,lwd=.25, fill = NA, aes(color = antigen)) + + theme( + legend.position = "bottom", + panel.grid.minor = element_blank(), + panel.grid.major.x = element_blank(), + panel.grid.major.y = element_line(size = .2)) + + xlab("") + + scale_fill_manual(values = c("grey65","tomato1")) + + scale_color_manual(values = c("black","black")) + + guides(fill = guide_legend(override.aes = list(size = 3, alpha = 1) ), + color = FALSE ) + pl[[x]] <- P +} + +pw06 <- pl[[1]] +pw06 + plot_annotation(title = "Clones with >20 cells across both antigens", + subtitle = "DT0606") + +# For DT0606 it probably makes more sense to show the ones with non-zero median +# expression values in at least one gene +medsT06 <- t06[, median(logcounts), by = c("antigen","id","gene_name")] %>% + .[V1 > 0] %>% .$id %>% as.character %>% unique + +tmp <- t06[as.character(id) %in% medsT06] +setorder(tmp, freq_across_all) +tmp$id <- factor(tmp$id, levels = unique(tmp$id), ordered = TRUE) +odd_numbers <- levels(tmp$id)[seq(1, nlevels(tmp$id),2)] + +ggplot(tmp, aes(x =id, y = logcounts))+ + geom_point(alpha = 0, aes(fill = antigen)) + # decoy points to set up the x axis + ## grey background for alternating IDs + geom_tile( + data = tmp[as.character(id) %in% odd_numbers], + aes(x = id, y = 1, height = Inf, width = 1), + alpha = 1, fill = "grey98") + + ## highlight sign. ones + geom_boxplot(data=tmp[star != ""], + outlier.alpha = 0.5,lwd=.25, fill = "yellow", + aes(color = antigen)) + + ## plot the actual values per sample + ggbeeswarm::geom_quasirandom( + groupOnX = TRUE, size = 1.5, shape = 21, alpha =.7, + dodge.width = .8, aes(fill = antigen), color = "white") + + facet_wrap(~gene_name) + coord_flip() + + geom_boxplot( + data=tmp[freq_across_all > 3], + outlier.alpha = 0,lwd=.25, fill = NA, aes(color = antigen)) + + ## remove grind lines etc. + theme( + legend.position = "bottom", + panel.grid.minor = element_blank(), + panel.grid.major.x = element_blank(), + panel.grid.major.y = element_blank()) + + xlab("") + + scale_fill_manual(values = c("grey65","tomato1")) + + scale_color_manual(values = c("black","black")) + + guides( + fill = guide_legend(override.aes = list(size = 3, alpha = 1) ), + color = FALSE ) + + ggtitle("Clones with non-zero median expression values", + subtitle = "DT0606; sorted by frequency") +``` + +## ExtDataFig 18: MR4050 + +- for volcano plots and boxplots (C-E), see `DEgenes_MR4050.xlsx` and `MR4050_C19_Winner_logcounts_CD3-4-8.txt` -- these plots were, in fact, done with GraphPad Prism + +```{r} +sce.mr <- KlebanoffMR4050::load_MR4050shared() +KlebanoffMR4050::load_DE_results() +de.mr <- delist.both + +rm(delist.both); invisible(gc()) + +tmr <- Klebanoff0606T1::prep_data4tgram(sce.mr, which_clonotypes = unique(sce.mr$id), + DEresult_list = de.mr, goi = "IFNG", + additional_colData = c("antigen","freq_per_Sample", + "freq_across_all","fit_for_test", "Patient")) + +# MR4050 ==================================== +## calculate CI etc. +testMRres <- tmr[ + fit_for_test == TRUE , + extract.tres(t.test(logcounts~antigen)), + by = c("id", "gene_name")] + +## summarize +tMR.summ <- tmr[, -c("cell", "cdr3s_aa","logcounts", "Sample","antigen", + "freq_per_Sample"), with=FALSE] %>% unique %>% .[testMRres, on = "id"] %>% + unique +tMR.summ[ is.na(ci.low), ci.low:=0] +tMR.summ[ is.na(ci.up), ci.up:=0] +``` + +* Error bar: SE + +```{r} +tMR.summ0 <- copy(tMR.summ) + +## if delta IFNG < 0, set to 0. +tMR.summ0[, logFC := ifelse(logFC.t > 0, logFC.t, 0)] +tMR.summ0[, ci.low0 := ifelse(logFC.t >0, ci.low, 0)] +tMR.summ0[, ci.up0 := ifelse(logFC.t >0, ci.up, 0)] + +## sort by frequency +setorder(tMR.summ0, -freq_across_all) +tMR.summ0[, comb.ID := paste(id, Patient, sep = ".")] +tMR.summ0$id.sort <- factor(tMR.summ0$comb.ID, + levels = tMR.summ0$comb.ID, ordered = TRUE) + +tMR.summ0[is.na(FDR), FDR := 1] +tMR.summ0[, neg.log10.FDR := -1*log10(FDR)] +``` + +```{r vers5_se, fig.width = 15} +ystar <- max(tMR.summ0$logFC.t) + 1.5 +ggplot(tMR.summ0, aes(x = id.sort, y = logFC.t)) + + geom_point(aes(size = freq_across_all, color = FDR)) + + geom_errorbar(aes(ymin=logFC.t-sde, ymax=logFC.t+sde), width=.1, color="grey45") + + facet_grid(gene_name ~ Patient, scales="free_x", space = "free_x") + + ## add asterisks ----------------------- + geom_text(inherit.aes = FALSE, + aes(x = id.sort, y = ystar, label=star), + colour="black", size=8)+ + geom_text(data = tMR.summ0[star != ""], + inherit.aes = FALSE, + aes(x = id.sort, label = id, y = 9.5)) + + ## grid appearance etc. ---------------- + theme(panel.grid.minor.x=element_blank(), + panel.grid.major.x=element_blank(), + axis.ticks = element_blank(), + axis.text.x = element_blank(), + legend.position = "bottom", + legend.text = element_text( size = 10), + legend.key.size = unit(2,"line")) + + scale_color_gradientn( colours = rev(c("gray70","yellow","limegreen"))) + + scale_size(name = "clonotype frequency") + + xlab("clonotype") + ylab("logFC expression [MUT/WT]") + + scale_x_discrete( expand = expansion(add = 5)) + + ggrepel::geom_label_repel( + data = tMR.summ0[logFC.t > 0.5 & id!="C19"], + box.padding = 0.1, label.padding = 0.1, label.size = 0.1, + aes(label = id)) + +``` + + + + diff --git a/FiguresForManuscript.html b/FiguresForManuscript.html new file mode 100644 index 0000000..0232cde --- /dev/null +++ b/FiguresForManuscript.html @@ -0,0 +1,981 @@ + + + + + + + + + + + + + + +Code for generating the data underlying the scRNA-seq-based figures in the SIFT-seq paper + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + +
library(SingleCellExperiment); library(data.table);library(magrittr)
+library(ggplot2); theme_set(theme_bw(base_size = 16) )
+library(ggrepel); library(patchwork)
+

For each patient’s data set (0606T1, 21LT2, MR4050/MK_02), there’s a separate R package that contains the processed data as well as the code details of how that data was generated (check out the vignettes for that).

+

Here, we show how the figures shown in the SIFT-seq manuscript were generated although the final-final versions that made it into the paper were also done with GraphPadPrism for reasons of legibility.

+
## load winners
+data("cdrs0606T1", package = "Klebanoff0606T1")
+data("cdrs21LT2", package = "Klebanoff21LT2")
+
+## load SingleCellExperiment objects
+sce.06 <- Klebanoff0606T1::load_0606T1shared()
+Klebanoff0606T1::load_DE_results() #delist.both
+de.06 <- delist.both
+
+sce.21 <- Klebanoff21LT2::load_21LT2shared()
+Klebanoff21LT2::load_DE_results() #delist.both
+de.21 <- delist.both
+
+rm(delist.both); invisible(gc())
+
+

Fig 1

+
    +
    1. +
    2. and (E): “tgrams” of IFNG logFC for 21LT2 and 0606T1, respectively
    3. +
  • +
    1. +
    2. and (D): volcano plots and violin plots were done in GraphPad Prism, see the xlsx and txt files in the data directory
    3. +
  • +
+
t21  <- Klebanoff0606T1::prep_data4tgram(sce.21, which_clonotypes = unique(sce.21$id),
+  DEresult_list = de.21, goi = "IFNG", 
+  additional_colData = c("antigen","freq_per_Sample", "freq_across_all","fit_for_test", "Patient"))
+
+t06 <- Klebanoff0606T1::prep_data4tgram(sce.06, which_clonotypes = unique(sce.06$id),
+  DEresult_list = de.06, goi = "IFNG", 
+  additional_colData = c("antigen","freq_per_Sample", "freq_across_all","fit_for_test", "Patient"))
+
#' Extract T test results
+#' @description returns the relevant values calculated
+#' by R's \code{\link{t.test()}} in the form of a data.table.
+#' @return data.table with logFC value (MUT/WT), standard error,
+#' lower and upper bounds of the confidence interval.
+extract.tres <- function(testres){
+  logFC.t = testres$estimate[2] - testres$estimate[1] #levels: WT < MUT
+  sde = testres$stderr
+  ci.low = testres$conf.int[[2]]*-1 # needed b/c WT < MUT levels
+  ci.up = testres$conf.int[[1]]*-1
+  return(data.table(logFC.t = logFC.t, sde = sde, ci.low = ci.low, ci.up = ci.up))
+}
+
# 21LT2 ===================================
+## calculate CI 
+test21res <- t21[ 
+  fit_for_test == TRUE , 
+  extract.tres(t.test(logcounts~antigen)), 
+  by = c("id", "gene_name")]
+
+## summarize everything in one dt
+t21.summ <- t21[, -c("cell", "cdr3s_aa","logcounts", "Sample","antigen", "freq_per_Sample"), with=FALSE] %>%
+  unique %>% test21res[., on = "id"] %>% unique
+t21.summ[ is.na(ci.low), ci.low:=0]
+t21.summ[ is.na(ci.up), ci.up:=0]
+
+
+# 0606T1 ====================================
+## calculate CI etc.
+test06res <- t06[ 
+  fit_for_test == TRUE , 
+  extract.tres(t.test(logcounts~antigen)), 
+  by = c("id", "gene_name")]
+
+## summarize
+t06.summ <- t06[, -c("cell", "cdr3s_aa","logcounts", "Sample","antigen", "freq_per_Sample"), with=FALSE] %>%
+  unique %>% test06res[., on = "id"] %>% unique
+t06.summ[ is.na(ci.low), ci.low:=0]
+t06.summ[ is.na(ci.up), ci.up:=0]
+
+t21.summ0 <- copy(t21.summ)
+t21.summ0[, logFC := ifelse(logFC.t > 0, logFC.t, 0)]
+t21.summ0[, logFC := ifelse(is.na(logFC.t), 0, logFC)]
+t21.summ0[, ci.low0 := ifelse(logFC.t >0, ci.low, 0)]
+t21.summ0[, ci.up0 := ifelse(logFC.t >0, ci.up, 0)]
+setorder(t21.summ0, -freq_across_all)
+t21.summ0$id.sort <- factor(t21.summ0$id, levels = t21.summ0$id, ordered = TRUE)
+
+t06.summ0 <- copy(t06.summ)
+t06.summ0[, logFC := ifelse(logFC.t > 0, logFC.t, 0)]
+t06.summ0[, logFC := ifelse(is.na(logFC.t), 0, logFC)]
+t06.summ0[, ci.low0 := ifelse(logFC.t >0, ci.low, 0)]
+t06.summ0[, ci.up0 := ifelse(logFC.t >0, ci.up, 0)]
+setorder(t06.summ0, -freq_across_all)
+t06.summ0$id.sort <- factor(t06.summ0$id, levels = t06.summ0$id, ordered = TRUE)
+
thresh <- 2
+
ystar <- max(t21.summ0$logFC) + 2
+
+ggplot(t21.summ0, aes(x = id.sort, y = logFC)) +
+  geom_hline(yintercept = 0) +
+  geom_hline(yintercept = thresh, linetype = "dashed", color = "grey50") +
+  ## points -------------------------------
+  geom_point(size = 4, aes(color = freq_across_all)) +
+  scale_color_gradientn(
+    name = "clonotype\nfrequency",  
+    colours = c("blue","grey85","firebrick3")) +
+  ## error bars ---------------------------
+  geom_errorbar(data = t21.summ0[logFC >= thresh], 
+    aes(ymin=ci.low0, ymax=ci.up0), 
+    width=.1, color="grey45") +
+  facet_grid(. ~ Patient, scales="free_x", space = "free_x") +
+  ## add asterisks -----------------------
+  geom_text(inherit.aes = FALSE, 
+    aes(x = id.sort, y = ystar, label=star),
+    colour="black", size=8)+
+  geom_text(data = t21.summ0[star != ""],
+    inherit.aes = FALSE,
+    aes(x = id.sort, label = id, y = ystar+.5)) +
+  ## grid appearance etc. ---------------
+  theme(panel.grid.minor.x=element_blank(),
+    panel.grid.major.x=element_blank(),
+    axis.ticks = element_blank(),
+    axis.text.x = element_blank()) +
+  xlab("clonotypes") + 
+  ylab("logFC IFNG expression [MUT/WT]")
+

+
ystar <- max(t06.summ0$logFC) + 2.5 ## DF
+
+ggplot(t06.summ0, aes(x = id.sort, y = logFC)) + ## DF
+  geom_hline(yintercept = 0) +
+  geom_hline(yintercept = thresh, linetype = "dashed", color = "grey50") +
+  ## points -------------------------------
+  geom_point(size = 4, aes(color = freq_across_all)) +
+  scale_color_gradientn(
+    name = "clonotype\nfrequency",  
+    colours = c("blue","grey85","firebrick3")) +
+  ## error bars ---------------------------
+  geom_errorbar(data = t06.summ0[logFC >= thresh], ## DF
+    aes(ymin=ci.low0, ymax=ci.up0), 
+    width=.1, color="grey45") +
+  facet_grid(. ~ Patient, scales="free_x", space = "free_x") +
+  ## add asterisks -----------------------
+  geom_text(inherit.aes = FALSE, 
+    aes(x = id.sort, y = ystar, label=star),
+    colour="black", size=15)+
+  geom_text(data = t06.summ0[star != ""], ## DF
+    inherit.aes = FALSE, size = 8,
+    aes(x = id.sort, label = id, y = ystar+1)) +
+  ## grid appearance etc. ---------------
+  theme(panel.grid.minor.x=element_blank(),
+    panel.grid.major.x=element_blank(),
+    axis.ticks = element_blank(),
+    axis.text.x = element_blank(),
+    legend.position = "bottom") +
+  xlab("clonotypes") + 
+  ylab("logFC IFNG expression [MUT/WT]")
+

+
+
+

Ext Fig 1: Volcano plots MUT vs. WT

+

–> see the data files DEgenes_21LT2_C18_mut_vs_wt.txt and ~/Documents/Projects/2018-10_Smita/2020-04_0606T1/DEgenes_0606T1.xlsx.

+
+
+

Ext Fig 3 and 4: boxplots of IFNg

+
+

mutation-reactive TCRs can be identified by normalized IFNG signal comparisons between MUT and WT stimulated conditions

+
+
    +
  • ExtFig 3: 21LT1
  • +
  • ExtFig 4: 0606T1:
  • +
+
t21  <- Klebanoff0606T1::prep_data4tgram(sce.21, which_clonotypes = unique(sce.21$id),
+  DEresult_list = de.21, goi = "IFNG",
+  additional_colData = c("antigen","freq_per_Sample", "freq_across_all","fit_for_test", "Patient"))
+setorder(t21, freq_across_all)
+t21$id <- factor(t21$id, levels = unique(t21$id), ordered = TRUE) 
+
+ggplot(t21, aes(x = id, y = logcounts))+
+  geom_point(alpha = 0, aes(color = antigen)) + 
+  geom_tile(data=t21[star != ""],
+      aes(x = id, y = 1, height = Inf, width = 1), 
+      alpha = 1, fill = "khaki1") +
+  ggbeeswarm::geom_quasirandom(
+   groupOnX = TRUE, size = 1.5, shape = 21, alpha =.7,
+    dodge.width = .8, aes(fill = antigen), color = "white") +
+  geom_boxplot(data=t21[freq_across_all > 3 ], 
+    outlier.alpha = 0,lwd=.25, fill = NA, aes(color = antigen)) +
+  facet_grid(.~gene_name) + coord_flip() +
+  xlab("") +
+  theme(panel.grid = element_blank()) +
+  scale_fill_manual(values = c("grey65","tomato1")) +
+  scale_color_manual(values = c("black","black")) +
+  ggtitle("All clonotypes of 21LT2", subtitle = "sorted by frequency; yellow highlights the logFC\nthat were found to be statistically significant") +
+   guides(fill = guide_legend(override.aes = list(size = 3, alpha = 1) ),
+      color = FALSE )
+

+
t06  <- Klebanoff0606T1::prep_data4tgram(sce.06, which_clonotypes = unique(sce.06$id),
+  DEresult_list = de.06, goi = "IFNG",
+  additional_colData = c("antigen","freq_per_Sample", "freq_across_all","fit_for_test", "Patient"))
+setorder(t06, freq_across_all)
+t06$id <- factor(t06$id, levels = unique(t06$id), ordered = TRUE) 
+
+pl <- list()
+ymax <- ceiling(max(t06$logcounts))
+
+for(x in unique(t06$gene_name)){
+  tmp <- t06[gene_name == x]
+  bp <- tmp[, median(logcounts), by = c("antigen","id")] %>% .[V1 > 0] %>% .$id %>% as.character %>% unique
+  P <- ggplot(tmp[freq_across_all > 20], aes(x = id, y = logcounts))
+  if(nrow(tmp[star != ""])>0){
+    P <- P + geom_point(alpha = 0, aes(color = antigen)) +
+      geom_tile(data=tmp[star != ""],
+        aes(x = id, y = 1, height = Inf, width = 1), 
+        alpha = 1, fill = "khaki1")
+  }
+  P <- P +  ggbeeswarm::geom_quasirandom(
+    groupOnX = TRUE, size = 1.5, shape = 21, alpha =.7,
+      dodge.width = .8, aes(fill = antigen), color = "white") +
+    facet_grid(.~gene_name) + coord_flip(ylim = c(0,ymax)) +
+    geom_boxplot(data=tmp[freq_across_all > 3 & as.character(id) %in% bp],
+      outlier.alpha = 0,lwd=.25, fill = NA, aes(color = antigen)) +
+    theme(
+      legend.position = "bottom",
+      panel.grid.minor = element_blank(), 
+      panel.grid.major.x = element_blank(),
+      panel.grid.major.y = element_line(size = .2)) +
+    xlab("") +
+  scale_fill_manual(values =  c("grey65","tomato1")) +
+  scale_color_manual(values = c("black","black")) +
+    guides(fill = guide_legend(override.aes = list(size = 3, alpha = 1) ),
+      color = FALSE )
+  pl[[x]] <- P
+}
+
+pw06 <- pl[[1]] 
+pw06 + plot_annotation(title =  "Clones with >20 cells across both antigens", subtitle = "DT0606")
+

+
# For DT0606 it probably makes more sense to show the ones with non-zero median expression values in at least one gene
+medsT06 <- t06[, median(logcounts), by = c("antigen","id","gene_name")] %>% 
+  .[V1 > 0] %>% .$id  %>% as.character %>% unique
+
+tmp <- t06[as.character(id) %in% medsT06]
+setorder(tmp, freq_across_all)
+tmp$id <- factor(tmp$id, levels = unique(tmp$id), ordered = TRUE) 
+odd_numbers <- levels(tmp$id)[seq(1, nlevels(tmp$id),2)]
+
+ggplot(tmp, aes(x =id, y = logcounts))+
+  geom_point(alpha = 0, aes(fill = antigen)) + # decoy points to set up the x axis
+  ## grey background for alternating IDs
+    geom_tile( 
+      data = tmp[as.character(id) %in% odd_numbers],
+      aes(x = id, y = 1, height = Inf, width = 1), 
+      alpha = 1, fill = "grey98") +
+    ## highlight sign. ones
+    geom_boxplot(data=tmp[star != ""],
+      outlier.alpha = 0.5,lwd=.25, fill = "yellow",
+      aes(color = antigen)) +
+  ## plot the actual values per sample
+    ggbeeswarm::geom_quasirandom(
+      groupOnX = TRUE, size = 1.5, shape = 21, alpha =.7,
+      dodge.width = .8, aes(fill = antigen), color = "white") +
+  facet_wrap(~gene_name) + coord_flip() +
+  geom_boxplot(
+    data=tmp[freq_across_all > 3],
+    outlier.alpha = 0,lwd=.25, fill = NA, aes(color = antigen)) +
+  ## remove grind lines etc.
+  theme(
+      legend.position = "bottom",
+      panel.grid.minor = element_blank(), 
+      panel.grid.major.x = element_blank(),
+      panel.grid.major.y = element_blank()) +
+  xlab("") +
+  scale_fill_manual(values =  c("grey65","tomato1")) +
+  scale_color_manual(values = c("black","black")) +
+  guides(
+    fill = guide_legend(override.aes = list(size = 3, alpha = 1) ),
+    color = FALSE ) +
+  ggtitle("Clones with non-zero median expression values", subtitle = "DT0606; sorted by frequency")
+

+
+
+

ExtDataFig 18: MR4050

+
    +
  • for volcano plots and boxplots (C-E), see DEgenes_MR4050.xlsx and MR4050_C19_Winner_logcounts_CD3-4-8.txt – these plots were, in fact, done with GraphPad Prism
  • +
+
sce.mr <- KlebanoffMR4050::load_MR4050shared()
+KlebanoffMR4050::load_DE_results()
+de.mr <- delist.both
+
+rm(delist.both); invisible(gc())
+
+tmr  <- Klebanoff0606T1::prep_data4tgram(sce.mr, which_clonotypes = unique(sce.mr$id),
+  DEresult_list = de.mr, goi = "IFNG", 
+  additional_colData = c("antigen","freq_per_Sample", "freq_across_all","fit_for_test", "Patient"))
+
+# MR4050 ====================================
+## calculate CI etc.
+testMRres <- tmr[ 
+  fit_for_test == TRUE , 
+  extract.tres(t.test(logcounts~antigen)), 
+  by = c("id", "gene_name")]
+
+## summarize
+tMR.summ <- tmr[, -c("cell", "cdr3s_aa","logcounts", "Sample","antigen", "freq_per_Sample"), with=FALSE] %>%  unique %>% .[testMRres, on = "id"] %>% unique
+tMR.summ[ is.na(ci.low), ci.low:=0]
+tMR.summ[ is.na(ci.up), ci.up:=0]
+
    +
  • Error bar: SE
  • +
+
tMR.summ0 <- copy(tMR.summ)
+
+## if delta IFNG < 0, set to 0.
+tMR.summ0[, logFC := ifelse(logFC.t > 0, logFC.t, 0)]
+tMR.summ0[, ci.low0 := ifelse(logFC.t >0, ci.low, 0)]
+tMR.summ0[, ci.up0 := ifelse(logFC.t >0, ci.up, 0)]
+
+## sort by frequency
+setorder(tMR.summ0, -freq_across_all)
+tMR.summ0[, comb.ID := paste(id, Patient, sep = ".")]
+tMR.summ0$id.sort <- factor(tMR.summ0$comb.ID, levels = tMR.summ0$comb.ID, ordered = TRUE)
+
+tMR.summ0[is.na(FDR), FDR := 1]
+tMR.summ0[, neg.log10.FDR := -1*log10(FDR)]
+
ystar <- max(tMR.summ0$logFC.t) + 1.5
+ggplot(tMR.summ0, aes(x = id.sort, y = logFC.t)) +
+  geom_point(aes(size = freq_across_all, color = FDR)) +
+  geom_errorbar(aes(ymin=logFC.t-sde, ymax=logFC.t+sde), width=.1, color="grey45") +
+  facet_grid(gene_name ~ Patient, scales="free_x", space = "free_x") +
+  ## add asterisks -----------------------
+  geom_text(inherit.aes = FALSE,
+    aes(x = id.sort, y = ystar, label=star),
+    colour="black", size=8)+
+  geom_text(data = tMR.summ0[star != ""],
+    inherit.aes = FALSE,
+    aes(x = id.sort, label = id, y = 9.5)) +
+  ## grid appearance etc. ----------------
+  theme(panel.grid.minor.x=element_blank(),
+    panel.grid.major.x=element_blank(),
+    axis.ticks = element_blank(),
+    axis.text.x = element_blank(),
+    legend.position = "bottom",
+    legend.text = element_text( size = 10),
+    legend.key.size = unit(2,"line")) +
+  scale_color_gradientn( colours = rev(c("gray70","yellow","limegreen"))) +
+  scale_size(name = "clonotype frequency") +
+  xlab("clonotype") + ylab("logFC expression [MUT/WT]") +
+  scale_x_discrete( expand = expansion(add = 5)) +
+  ggrepel::geom_label_repel(
+    data = tMR.summ0[logFC.t > 0.5 & id!="C19"], 
+    box.padding = 0.1, label.padding = 0.1, label.size = 0.1,
+    aes(label = id))
+

+
+ + + + +
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