https://github.com/squatrim/marques2020
Tip revision: 45e31e7d17f006d2d3a17e66a63449f758bf5998 authored by squatrim on 02 August 2021, 14:13:14 UTC
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README.md
Marques\_et\_al\_2021
================
### NF1 regulates mesenchymal glioblastoma plasticity and aggressiveness through the AP-1 transcription factor FOSL1
Carolina Marques, Thomas Unterkircher, Paula Kroon, Barbara Oldrini,
Annalisa Izzo, Yuliia Dramaretska, Roberto Ferrarese, Eva Kling, Oliver
Schnell, Sven Nelander, Erwin F. Wagner, Latifa Bakiri, Gaetano
Gargiulo, Maria Stella Carro and Massimo Squatrito. *eLife* (in press)
``` r
library(tidyverse)
library(readxl)
library(GSVA)
library(statmod)
library(Biobase)
library(ggpubr)
library(ggrepel)
library(ggridges)
library(cowplot)
library(ggplotify)
library(reshape2)
library(survival)
library(survminer)
library(limma)
library(chromVAR)
# library(biomaRt)
library(pheatmap)
library(clusterProfiler)
library(SummarizedExperiment)
library(ggraph)
library(karyoploteR)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(grid)
# library(RTN)
# library(snow)
library(devtools)
library(RColorBrewer)
source('./Scripts/plotqPCR.R', echo=F)
source('./Scripts/replotGSEA.R', echo=F)
source('./Scripts/ggplotLimdil.R', echo=F)
source('./Scripts/survPlot.R', echo=F)
source('./Scripts/statePlot.R', echo=F)
source('./Scripts/emaplot.R', echo=F)
source('./Scripts/plotDeviationTsne2.R', echo=F)
source('./Scripts/tracksPlot.R', echo=F)
set.seed(12345) #seed for reproducibility of the analysis
symnum.args <- list(cutpoints = c(0, 0.001, 0.01, 0.05, 1),
symbols = c("***", "**", "*","ns")) # symbols for pvalues
black_red <- c("#000000","#E41A1C")
black_red_green <- c("#000000","#E41A1C","#4DAF4A")
gray_black <- c("#808080","#000000")
paired_black_red <- c("#000000","#666666","#E41A1C","#FB9A99")
font_size <- font("xy.text", size = 8) + font("xlab", size = 10) + font("ylab", size = 10) + font("title",size = 10)
```
## Data
``` r
BTSCs_exprs <- read.delim("./Data/BTSCs_exprs.txt")
BTSCs_subtypes <- read.delim("./Data/BTSCs_subtypes.txt")
gsea_report_all_analysis <- read.delim("./Data/gsea_report_all_analysis.txt")
qPCR_data <- read.delim("./Data/qPCR_data_2021.txt",
stringsAsFactors=FALSE)
tcga_cgga_data <- read.delim("./Data/TCGA_CGGA_data_tableS4.txt")
figure_S1C_data <- read.delim("./Data/Figure_S1C.txt")
figure_S1D_data <- read.delim("./Data/Figure_S1D.txt")
figure_S1E_data <- read.delim("./Data/Figure_S1E.txt")
figure_S2B_data <- read.delim("./Data/Figure_S2B.txt")
figure_S2C_data <- read.delim("./Data/Figure_S2C.txt")
load("./Data/Figure_4_data.RData")
figure_4C_data <- read.delim("./Data/Figure_4C.txt")
figure_4F_data <- read.delim("./Data/Figure_4F.txt",
comment.char="#",
stringsAsFactors=FALSE)
gene_signatures <- read.delim("./Data/gene_signatures_2021.txt") %>%
.[-1,] %>% # exclude 1st row
as.list(.) %>% # convert to a list
lapply(., function(x) x[!is.na(x)]) # remove NA
figure_5A_data <- read.delim("./Data/Figure_5A.txt")
figure_5B_data <- read.delim("./Data/Figure_5B.txt") %>%
mutate(Phase = factor(Phase, levels = c("G1","S","G2")))
figure_5C_data <- read.delim("./Data/Figure_5C.txt")
figure_5D_expr <- read.delim("./Data/NSCs_Kras_sgFosl1_exprs.txt",
sep = "\t", stringsAsFactors = F)
figure_5D_pdata <- read.delim("./Data/NSCs_Kras_sgFosl1_pdata.txt",
sep = "\t", stringsAsFactors = F)
stem_diff_genes <- read.delim("./Data/stem_diff_genes.txt", sep = "\t")
figure_5E_data <- read.delim("./Data/Figure_5E.txt")
figure_6D_data <- read.delim("./Data/Figure_6D.txt") %>%
mutate(Area_scaled = Area/1e07)
figure_7C_data <- read.delim("./Data/Figure_7C.txt")
figure_7D_data <- read.delim("./Data/Figure_7D.txt")
figure_7E_data <- read.delim("./Data/Figure_7E.txt")
figure_7F_data <- read.delim("./Data/Figure_7F.txt",
stringsAsFactors=FALSE)
figure_7F_annotation <- read.delim("./Data/Figure_7F_annotation.txt",
stringsAsFactors=FALSE)
figure_7G_data <- read.delim("./Data/Figure_7G.txt",
stringsAsFactors=FALSE)
figure_S7A_data <- read.delim("./Data/Figure_S7A.txt")
figure_S8B_data <- read.delim("./Data/Figure_S8B.txt")
figure_S8C_data <- read.delim("./Data/Figure_S8C.txt")
figure_S8D_data <- read.delim("./Data/Figure_S8D.txt")
figure_S8G_data <- read.delim("./Data/Figure_S8G.txt")
figure_S8I_data <- read.delim("./Data/Figure_S8I.txt")
figure_S8K_data <- read.delim("./Data/Figure_S8K.txt")
```
## Figure 1
``` r
################################################################
# Data Processing:
# 1. Data were downloaded from GEO
# 2. Common probes among platform were selected
# 3. Subtypes were calculated using `runSsGSEAwithPermutation` with 1000 permutation (set.seed(12345))
# 4. Each dataset was normalized (mean = 0, sd = 1)
# 5. Datasets were then combined in one single dataset
BTSCs_subtypes <- BTSCs_subtypes %>%
mutate(concordant_subtype = case_when(Richards_2021 == "INJ" & Wang_2017 != "MES" ~ "NO",
TRUE ~ "YES"))
row.names(BTSCs_subtypes) <- BTSCs_subtypes$accession_ID
BTSCs_eset <- ExpressionSet(assayData = as.matrix(BTSCs_exprs),
phenoData=as(BTSCs_subtypes, "AnnotatedDataFrame"))
BTSCs_eset$Group <- ifelse(pData(BTSCs_eset)$Wang_2017 == "MES", "MES", "Non-MES")
###############################################################
# PCA
pdata <- pData(BTSCs_eset)
edata <- exprs(BTSCs_eset)
pc <- prcomp(t(edata))
pc_matrix <- data.frame(pc$x)
percentage <- round(pc$sdev^2/ sum(pc$sdev^2) * 100, 2)
percentage <- paste(colnames(pc_matrix), "(",
paste(as.character(percentage), "%", ")", sep=""),sep = "")
pc_matrix$Wang_2017 <- pdata$Wang_2017
pc_matrix$Richards_2021 <- pdata$Richards_2021
pc_matrix$Dataset <- pdata$dataset
pc_matrix$Concordant <- pdata$concordant_subtype
figure_1a_left <- pc_matrix %>%
ggscatter(x = "PC2", y = "PC1", size = 0.8,
color = "Wang_2017", shape = "Dataset",
palette = "Set1", ellipse = TRUE,
xlab = percentage[2],
ylab = percentage[1],
title = "Wang_2017",
legend = "right") +
font_size +
scale_shape_manual(values=seq(0,7))
figure_1a_left <- ggpar(figure_1a_left, font.legend = c(8,"plain","black"))
figure_1a_right <- pc_matrix %>%
ggscatter(x = "PC2", y = "PC1", size = 0.8,
color = "Richards_2021", shape = "Dataset",
palette = brewer.pal(9, "Set1")[4:5], ellipse = TRUE,
xlab = percentage[2],
ylab = percentage[1],
title = "Richards_2021",
legend = "right") +
font_size + rremove("ylab") +
scale_shape_manual(values=seq(0,7))
figure_1a_right <- ggpar(figure_1a_right, font.legend = c(8,"plain","black"))
figure_1a <- ggarrange(figure_1a_left,figure_1a_right, nrow = 1, common.legend = T)
figure_1a
```
<!-- -->
## Figure 1b
``` r
###############################################################
# DEG analysis
# limma
BTSCs_eset_sel <- BTSCs_eset[, BTSCs_eset$concordant_subtype == "YES"]
sml <- ifelse(pData(BTSCs_eset_sel)[,"Group"] == "MES","G1","G0")
fl <- as.factor(sml)
BTSCs_eset_sel$description <- fl
design <- model.matrix(~ description + 0 + dataset, BTSCs_eset_sel) # include dataset to correct for batch effect
fit <- lmFit(BTSCs_eset_sel, design)
cont.matrix <- makeContrasts(descriptionG1-descriptionG0, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2, 0.01)
combo_eset_tT <- topTable(fit2, adjust="fdr",
sort.by="logFC",
p.value = 0.05,
number=100) %>%
rownames_to_column(var = "Gene.Symbol") %>%
arrange(-logFC)
combo_eset_tT_all <- topTable(fit2, adjust="fdr",
sort.by="logFC",
p.value = 0.05,
n=Inf) %>%
rownames_to_column(var = "Gene.Symbol") %>%
arrange(-logFC)
# heatmap
combo_eset_expr <- exprs(BTSCs_eset_sel)[combo_eset_tT$Gene.Symbol,]
combo_eset_annotation <- pData(BTSCs_eset_sel)[,c("dataset", "Wang_2017", "Richards_2021")]
names(combo_eset_annotation) <- c("Dataset", "Wang_2017", "Richards_2021")
combo_colors <- list(Dataset = brewer.pal(8, "Set2")[1:6],
Wang_2017 = brewer.pal(9, "Set1")[1:3],
Richards_2021 = brewer.pal(9, "Set1")[4:5])
names(combo_colors$Dataset) <- levels(factor(combo_eset_annotation$Dataset))
names(combo_colors$Wang_2017) <- levels(factor(combo_eset_annotation$Wang_2017))
names(combo_colors$Richards_2021) <- levels(factor(combo_eset_annotation$Richards_2021))
figure_1b <- pheatmap(t(combo_eset_expr),
annotation_row = combo_eset_annotation,
scale = "column",
clustering_distance_rows = "correlation",
show_rownames = F,
show_colnames = T,
fontsize_col = 5,
border_color = NA,
cluster_col = F, cluster_rows = T,
annotation_colors = combo_colors,
# cutree_rows = 3,
color = colorRampPalette(c("steelblue","white","red"))(100),
silent = T)
figure_1b
```
<!-- -->
## Figure 1c
``` r
# ################################################################
# # Master regulator analysis
# # NOT RUN !# It's a bit heavy computationally, not needed for the github upload
# ################################################################
# TF_DatabaseExtract_v_1_01 <- read.delim("./Data/TF_DatabaseExtract_v_1_01.txt",sep = "\t")
# tf_genes <- subset(TF_DatabaseExtract_v_1_01,
# TF_DatabaseExtract_v_1_01$`Is.TF.` == "Yes" &
# TF_DatabaseExtract_v_1_01$`HGNC.symbol` %in% featureNames(BTSCs_eset_sel))
#
#
# # ################################################################
# # Prepare input for RTN
# gexp = exprs(BTSCs_eset_sel) # a named gene expression matrix (gexp)
# pheno = as.numeric(combo_eset_tT_all[,"logFC"]) # a named numeric vector with differential gene expression data (pheno)
# names(pheno) <- combo_eset_tT_all$Gene.Symbol
# hits = combo_eset_tT_all$Gene.Symbol # a character vector with genes differentially expressed (hits)
# tfs_all = tf_genes$`HGNC.symbol` # a named vector with transcriptions factors (tfs)
# names(tfs_all) <- tfs_all
# rowAnnotation <- data.frame(PROBEID = featureNames(BTSCs_eset),SYMBOL = featureNames(BTSCs_eset))
# row.names(rowAnnotation) <- rowAnnotation$SYMBOL
#
# # Create transcriptional network
# # http://bioconductor.org/packages/release/bioc/vignettes/RTN/inst/doc/RTN.html
# #Input 1: 'expData', a named gene expression matrix (samples on cols)
# #Input 2: 'regulatoryElements', a named vector with TF ids
# #Input 3: 'rowAnnotation', an optional data frame with gene annotation
# dt4rtn2 <- list(gexp = gexp,
# gexpIDs = NULL,
# pheno = pheno,
# phenoIDs = NULL,
# hits = hits,
# tfs = tfs_all)
# tfs <- dt4rtn2$tfs
# rtni2 <- tni.constructor(expData = dt4rtn2$gexp,
# regulatoryElements = dt4rtn2$tfs,
# rowAnnotation = rowAnnotation)
# # run parallel version with SNOW package!
# options(cluster=makeCluster(3, "SOCK"))
# rtni2 <- tni.permutation(rtni2, verbose = T,
# pValueCutoff = 0.05,
# nPermutations = 1000)
# rtni2 <- tni.bootstrap(rtni2, verbose = T)
# rtni2 <- tni.dpi.filter(rtni2,verbose = T)
#
# # Perform MRA
# #Input 1: 'object', a TNI object with a pre-processed transcripional network
# #Input 2: 'phenotype', a named numeric vector, usually with log2 differential expression values (limma toptable)
# #Input 3: 'hits', a character vector of gene ids considered as hits (significant DEG)
# #Input 4: 'phenoIDs', an optional data frame with anottation used to aggregate genes in the phenotype
# rtna2 <- tni2tna.preprocess(object=rtni2,
# phenotype=dt4rtn2$pheno,
# hits=dt4rtn2$hits)
#
# # The tna.mra function takes the TNA object and returns the results of the Master Regulator Analysis (RMA) (Carro et al. 2010)
# # over a list of regulons from a transcriptional network (with multiple hypothesis testing corrections).
# # The MRA computes the overlap between the transcriptional regulatory unities (regulons) and the input signature genes
# # using the hypergeometric distribution (with multiple hypothesis testing corrections).
#
# rtna2 <- tna.mra(rtna2)
# rtna2 <- tna.gsea1(rtna2,
# nPermutations=1000,
# pValueCutoff = 1) # Measure one tailed enrichment score for all the MR
# rtna2 <- tna.gsea2(rtna2, nPermutations=1000)
# stopCluster(getOption("cluster"))
# mra <- tna.get(rtna2, what="mra", ntop = -1) # get MRA results
# # gsea1 <- tna.get(rtna2, what="gsea1",ntop = -1) # get EScore and pvalues from GSEA
# # mra_gsea1_results <- full_join(mra,gsea1, by = c("Regulon"))
# # gsea2 <- tna.get(rtna2, what="gsea2")
#
# ## Figure_1c <- RTN analysis
# tna.plot.gsea1(rtna2, labPheno="abs(log2) diff. expression",
# tfs=mra$Regulon[1:10], plotpdf = FALSE,
# regulon.order = "score")
```
## Figure 1d
``` r
#############################################
BTSCs_df <- merge(pData(BTSCs_eset_sel),
t(exprs(BTSCs_eset_sel)),
by = "row.names")
sub_comparisons <- list( c("MES", "PN"),
c("MES", "CL"),
c("CL", "PN"))
figure_1d_left <- BTSCs_df %>%
ggboxplot(x = "Wang_2017", y = "FOSL1",
color = "Wang_2017",
palette = brewer.pal(9, "Set1")[1:3],
outlier.size = 0, outlier.stroke = 0,
add = "jitter", add.params = list(shape = "dataset"),
ylab = "FOSL1 mRNA (A.U.)",
ylim = c(-2,4.5),
legend = "right") + font_size +
theme(legend.position = "none") +
rremove("xlab") +
scale_shape_manual(values = seq(0,7)) +
stat_compare_means(comparisons = sub_comparisons,
symnum.args = symnum.args,
method = "t.test")
figure_1d_left <- ggpar(figure_1d_left, font.legend = c(8,"plain","black"))
# #t-test
# BTSCs_df %>%
# compare_means(FOSL1 ~ Wang_2017,
# comparisons = sub_comparisons,
# symnum.args = symnum.args,
# method = "t.test",
# data = .)
#
# #Anova with Tukey post-hoc
# BTSCs_df %>%
# aov(FOSL1 ~ Wang_2017, data = .) %>%
# TukeyHSD() %>% .$Wang_2017
figure_1d_right <- BTSCs_df %>%
ggboxplot(x = "Richards_2021", y = "FOSL1",
color = "Richards_2021",
palette = brewer.pal(9, "Set1")[4:5],
outlier.size = 0, outlier.stroke = 0,
add = "jitter", add.params = list(shape = "dataset"),
ylab = "FOSL1 mRNA (A.U.)",
ylim = c(-2,4.5),
legend = "right") + font_size +
rremove("xlab") + rremove("ylab") +
theme(legend.position = "none") +
scale_shape_manual(values = seq(0,7)) +
stat_compare_means(comparisons = list(c("DEV", "INJ")),
symnum.args = symnum.args,
label.y = 4,
method = "t.test")
figure_1d_right <- ggpar(figure_1d_right, font.legend = c(8,"plain","black"))
figure_1d <- plot_grid(figure_1d_left, figure_1d_right, nrow = 1, rel_widths = c(1, 0.7))
figure_1d
```
<!-- -->
## Figure 1e
``` r
mol_comparison <- list(c("IDHmut-codel","IDHmut-non-codel"),
c("IDHmut-codel","IDHwt"),
c("IDHmut-non-codel","IDHwt"))
figure_1e <- tcga_cgga_data %>%
group_by(dataset) %>%
mutate(FOSL1 = scale(FOSL1),
IDH_codel.subtype = factor(IDH_codel.subtype,
levels = c("IDHmut-codel","IDHmut-non-codel","IDHwt"))) %>%
filter(., IDH_codel.subtype!=is.na(IDH_codel.subtype)) %>%
ggboxplot(x = "IDH_codel.subtype", y = "FOSL1",
outlier.size = 0, outlier.stroke = 0,
add = "jitter",
add.params = list(size = 0.6, alpha = 0.3),
facet.by = "dataset", ylim = c(-3,6),
ylab = "FOSL1 mRNA (A.U.)") +
font_size + rremove("xlab") +
scale_x_discrete(labels=function(x){sub("\\-", "\n", x)}) +
stat_compare_means(method = "t.test", comparison = mol_comparison,
label.y = c(3.25,4.25,5.5),label = "p.signif",
symnum.args = symnum.args)
# # To get the statistic
# tcga_cgga_data %>%
# dplyr::filter(., IDH_codel.subtype!=is.na(IDH_codel.subtype)) %>%
# compare_means(FOSL1 ~ IDH_codel.subtype, method = "t.test", data = .,symnum.args = symnum.args, group.by = "dataset")
#
# #Anova with Tukey post-hoc
# tcga_cgga_data %>%
# dplyr::filter(., dataset == "CGGA", IDH_codel.subtype!=is.na(IDH_codel.subtype)) %>%
# mutate(FOSL1 = scale(FOSL1)) %>%
# aov(FOSL1 ~ IDH_codel.subtype, data = .) %>%
# TukeyHSD() %>% .$IDH_codel.subtype
#
# tcga_cgga_data %>%
# dplyr::filter(., dataset == "TCGA", IDH_codel.subtype!=is.na(IDH_codel.subtype)) %>%
# mutate(FOSL1 = scale(FOSL1)) %>%
# aov(FOSL1 ~ IDH_codel.subtype, data = .) %>%
# TukeyHSD() %>% .$IDH_codel.subtype
figure_1e
```
<!-- -->
## Figure 1f
``` r
# Panel 1f, survival
figure_1f_left <- survPlot(subset(tcga_cgga_data, dataset == "CGGA")) +
ggtitle("CGGA")
figure_1f_right <- survPlot(subset(tcga_cgga_data, dataset == "TCGA")) +
ggtitle("TCGA")
figure_1f <- plot_grid(figure_1f_left$plot, figure_1f_right$plot)
figure_1f
```
<!-- -->
## Figure 1-figure supplement 1a (Figure S1a)
``` r
# Expression data of the top 10 TF
figure_s1_data <- BTSCs_df %>%
.[,c("dataset","Group", c("FOSL1","VDR","SP100","ELF4", "BNC2",
"OLIG2","SOX11","ASCL1","SALL2","POU3F3"))] %>%
reshape2::melt(.)
figure_s1a <- figure_s1_data %>%
ggplot(aes(x = Group, y = value)) +
geom_boxplot(outlier.size = 0, outlier.stroke = 0) +
geom_jitter(position = position_jitter(width = .25),
size = 2, alpha = 0.75,
aes(color=Group, shape = dataset)) +
ylim(-3,5) +
scale_color_manual(values = c("#F5AE26", "#EA549D")) +
labs(y = "mRNA (A.U.)", x = "Subtype",
color = "Subtype", shape = "Dataset") +
scale_shape_manual(values=seq(0,7)) + theme_bw() +
stat_compare_means(symnum.args = symnum.args, label.y = 4, label.x = 1.4,
method = "t.test", label = "p.signif") +
facet_wrap(~variable,nrow = 2,ncol = 5)
figure_s1a
```
<!-- -->
## Figure 1-figure supplement 1b (Figure S1b)
``` r
# figure_s1b <- tna.plot.gsea2(rtna2, file="tna_gsea2", labPheno="abs(log2) diff. expression", tfs= c("FOSL1","VDR","OLIG2","SOX11"))
```
## Figure 1-figure supplement 1c (Figure S1c)
``` r
# figure s1c left Richards GSCs ssGSEA score subtypes
row.names(figure_S1C_data) <- figure_S1C_data$Sample
subtypes_annotation <- figure_S1C_data[,c("Richards_2021","Wang_2017")]
subtypes_gsea_colors <- list(Wang_2017 = brewer.pal(8, "Set1")[1:3],
Richards_2021 = brewer.pal(9, "Set1")[4:5])
names(subtypes_gsea_colors$Wang_2017) <- levels(factor(subtypes_annotation$Wang_2017))
names(subtypes_gsea_colors$Richards_2021) <- levels(factor(subtypes_annotation$Richards_2021))
figure_s1c_left <- pheatmap(t(figure_S1C_data[2:6]),
annotation_col = subtypes_annotation,
scale = "row",
clustering_distance_cols = "correlation",
show_colnames = F, fontsize_col = 5,
border_color = NA,
cluster_col = T, cluster_rows = F,
annotation_colors = subtypes_gsea_colors,
color = colorRampPalette(c("steelblue","white","red"))(100),
silent = T)
# figure s1c right FOSL1 expression Richards GSCs
figure_s1c_right_a <- figure_S1C_data %>%
subset(., Wang_2017 != is.na(Wang_2017)) %>%
ggboxplot(x = "Wang_2017", y = "FOSL1",
color = "Wang_2017",
palette = brewer.pal(9, "Set1")[1:3],
outlier.size = 0, outlier.stroke = 0,
add = "jitter", ylim = c(5,18),
ylab = "FOSL1 mRNA (A.U.)",
legend = "right") +
theme(legend.position = "none") + font_size + ggtitle("") +
rremove("xlab") +
stat_compare_means(comparisons = list( c("MES", "PN"),
c("MES", "CL"),
c("CL", "PN")),
symnum.args = symnum.args,
method = "t.test")
figure_s1c_right_a <- ggpar(figure_s1c_right_a, font.legend = c(8,"plain","black"))
figure_s1c_right_b <- figure_S1C_data %>%
subset(., Wang_2017 != is.na(Wang_2017)) %>%
ggboxplot(x = "Richards_2021", y = "FOSL1",
color = "Richards_2021",
palette = brewer.pal(9, "Set1")[4:5],
outlier.size = 0, outlier.stroke = 0,
add = "jitter",ylim = c(5,18),
ylab = "FOSL1 mRNA (A.U.)",
legend = "right") + font_size + ggtitle("") +
rremove("xlab") + rremove("ylab") +
theme(legend.position = "none") +
stat_compare_means( comparisons = list(c("DEV", "INJ")),
symnum.args = symnum.args,
method = "t.test")
figure_s1c_right_b <- ggpar(figure_s1c_right_b, font.legend = c(8,"plain","black"))
figure_s1c <- plot_grid(figure_s1c_left$gtable,
figure_s1c_right_a,
figure_s1c_right_b,
nrow = 1, rel_widths = c(3,1, 0.7))
figure_s1c
```
<!-- -->
## Figure 1-figure supplement 1d (Figure S1d)
``` r
# figure s1d ssGSEA score Richards GSCs scRNAseq
figure_S1D_data_long <- pivot_longer(data = figure_S1D_data[,-1],
cols = Richards_DEV_2021:Wang_CL_2017)
figure_s1d <- figure_S1D_data_long %>%
mutate(name = factor(name, levels=c("Wang_CL_2017","Wang_PN_2017","Wang_MES_2017",
"Richards_DEV_2021", "Richards_INJ_2021"))) %>%
ggplot(aes(x = X, y = Y)) +
geom_point(aes(color = value), alpha = 0.75, size = 0.5) +
labs(x="PC1",y="PC2") +
scale_colour_gradient2(low="blue", midpoint = 0.35, high="red") +
theme_bw() +
facet_wrap(~name, nrow = 1)
figure_s1d
```
<!-- -->
## Figure 1-figure supplement 1e (Figure S1e)
``` r
# figure s1e Top 10 TFs Richards GSCs scRNAseq
figure_S1E_data_long <- pivot_longer(data = figure_S1E_data[,-1],cols = FOSL1:POU3F3) %>%
mutate(name = factor(name, levels = c("FOSL1","VDR","SP100","ELF4", "BNC2", "OLIG2","SOX11","ASCL1","SALL2","POU3F3")),
group = case_when(name %in% c("FOSL1","VDR","SP100","ELF4", "BNC2") ~ 'MES',
name %in% c("OLIG2","SOX11","ASCL1","SALL2","POU3F3") ~ 'Non-MES'))
# two-dimensional state plot
figure_s1e <- figure_S1E_data_long %>%
ggplot(aes(x = X, y = Y)) +
geom_point(aes(color = value), alpha = 0.75, size = 0.5) +
labs(x="PC1",y="PC2") +
theme_bw() +
facet_wrap(.~name, nrow = 2) +
scale_color_gradient2(low = "white", mid = "#FFFFCC", high = "red")
figure_s1e
```
<!-- -->
## Figure 1-figure supplement 2a (Figure S2a)
``` r
# figure s2a FOSL1 expression CGGA and TCGA stratified by subtypes
sub_comparisons <- list( c("MES", "PN"),
c("MES", "CL"),
c("CL", "PN"))
figure_s2a_left <- tcga_cgga_data %>%
.[!is.na(.$Wang_2017),] %>%
group_by(dataset) %>%
mutate(FOSL1 = scale(FOSL1),
Wang_2017 = factor(Wang_2017, levels= c("CL","MES","PN"))) %>%
filter(., IDH_codel.subtype == "IDHwt") %>%
ggboxplot(x = "Wang_2017", y = "FOSL1",
color = "Wang_2017",
palette = brewer.pal(9, "Set1")[1:3],
outlier.size = 0, outlier.stroke = 0,
add = "jitter",
add.params = list(size = 0.8, alpha = 0.5),
facet.by = "dataset", ylim = c(-3,4),
ylab = "FOSL1 mRNA (A.U.)") +
font_size + rremove("xlab") +
stat_compare_means(comparisons = sub_comparisons,
symnum.args = symnum.args,
method = "t.test")
figure_s2a_left <- ggpar(figure_s2a_left, font.legend = c(8,"plain","black"))
# #Anova with Tukey post-hoc
# tcga_cgga_data %>%
# .[!is.na(.$Wang_2017),] %>%
# group_by(dataset) %>%
# mutate(FOSL1 = scale(FOSL1),
# Wang_2017 = factor(Wang_2017, levels= c("CL","MES","PN"))) %>%
# filter(., IDH_codel.subtype == "IDHwt") %>%
# aov(FOSL1 ~ Wang_2017, data = .) %>%
# TukeyHSD() %>% .$Wang_2017
figure_s2a_right <- tcga_cgga_data %>%
.[!is.na(.$Richards_2021),] %>%
group_by(dataset) %>%
mutate(FOSL1 = scale(FOSL1),
Richards_2021 = factor(Richards_2021, levels= c("DEV","INJ"))) %>%
filter(., IDH_codel.subtype == "IDHwt") %>%
ggboxplot(x = "Richards_2021", y = "FOSL1",
color = "Richards_2021",
palette = brewer.pal(9, "Set1")[4:5],
outlier.size = 0, outlier.stroke = 0,
add = "jitter",
add.params = list(size = 0.8, alpha = 0.5),
facet.by = "dataset", ylim = c(-3,4),
ylab = "FOSL1 mRNA (A.U.)") +
font_size + rremove("xlab") +
stat_compare_means(comparisons = list(c("DEV", "INJ")),
symnum.args = symnum.args,
method = "t.test")
figure_s2a_right <- ggpar(figure_s2a_right, font.legend = c(8,"plain","black"))
figure_s2a <- plot_grid(figure_s2a_left, figure_s2a_right)
figure_s2a
```
<!-- -->
## Figure 1-figure supplement 2b (Figure S2b)
``` r
# figure s2b ssGSEA score Neftel tumor scRNAseq
figure_S2B_data_long <- pivot_longer(data = figure_S2B_data[,-1],
cols = Richards_DEV_2021:Wang_CL_2017)
figure_s2b <- figure_S2B_data_long %>%
mutate(name = factor(name, levels=c("Wang_CL_2017","Wang_PN_2017","Wang_MES_2017",
"Richards_DEV_2021","Richards_INJ_2021"))) %>%
ggplot(aes(x = X, y = Y)) +
geom_point(aes(color = value), alpha = 0.75, size = 0.5) +
ylim(-2.75,2.75) + xlim(-2.75,2.75)+
geom_hline(yintercept = 0, size = 0.25) +
geom_vline(xintercept = 0, size = 0.25) +
labs(x="",y="") +
scale_colour_gradient2(low="blue", midpoint = 0.5, high="red") +
theme_bw() +
theme(axis.ticks.length=unit(0, "cm"), axis.text.x=element_blank(),
axis.text.y=element_blank()) +
facet_wrap(.~name, nrow =1)
figure_s2b
```
<!-- -->
## Figure 1-figure supplement 2c (Figure S2c)
``` r
# figure s2c Top 10 TFs Neftel tumor scRNAseq, hexbins on scaled expression
figure_S2C_data_long <- pivot_longer(data = figure_S2C_data[,-1],cols = FOSL1:POU3F3)
figure_s2c <- figure_S2C_data_long %>%
mutate(name = factor(name, levels= c("FOSL1","VDR","SP100","ELF4", "BNC2", "OLIG2","SOX11","ASCL1","SALL2","POU3F3"))) %>%
ggplot(aes(x = X, y = Y, z = value)) +
stat_summary_hex(bins=100, fun = "median") +
ylim(-2.75,2.75) + xlim(-2.75,2.75)+
geom_hline(yintercept = 0, size = 0.25) +
geom_vline(xintercept = 0, size = 0.25) +
labs(x="",y="") +
scale_fill_gradientn(colours = c(brewer.pal(n = 8, name = "YlOrRd"))) +
theme_bw() +
theme(axis.ticks.length=unit(-0.1, "cm"), axis.text.x=element_blank(),
axis.text.y=element_blank()) +
facet_wrap(.~name,nrow = 2,ncol = 5)
figure_s2c
```
<!-- -->
## Figure 2a
``` r
# select only TCGA IDH-wt samples
tcga_idwt_samples <- as.character(
filter(tcga_cgga_data, dataset == "TCGA" &
IDH_codel.subtype == "IDHwt")$Sample)
# ssGSEA values
tumor_gsva_annotation <- tcga_cgga_data %>%
filter(dataset == "TCGA" & IDH_codel.subtype == "IDHwt") %>%
.[ ,c("Sample", "Wang_2017","Richards_2021","NF1_status")] %>%
mutate(NF1_status = factor(NF1_status),
Wang_2017 = factor(Wang_2017),
Richards_2021 = factor(Richards_2021)) %>%
arrange(Richards_2021, Wang_2017, NF1_status) %>%
column_to_rownames("Sample")
tumor_gsva_colors <- list(Wang_2017 = brewer.pal(9, "Set1")[1:3],
Richards_2021 = brewer.pal(9, "Set1")[4:5],
NF1_status = c("#FC8D62","#66C2A5"))
names(tumor_gsva_colors$Wang_2017) <- levels(tumor_gsva_annotation$Wang_2017)
names(tumor_gsva_colors$Richards_2021) <- levels(tumor_gsva_annotation$Richards_2021)
names(tumor_gsva_colors$NF1_status) <- levels(tumor_gsva_annotation$NF1_status)
tumor_data <- tcga_cgga_data %>%
filter(dataset == "TCGA" & IDH_codel.subtype == "IDHwt") %>%
.[,c("Sample","Developmental_ssGSEA","Injury_ssGSEA",
"Classical_ssGSEA","Mesenchymal_ssGSEA","Proneural_ssGSEA")] %>%
column_to_rownames("Sample")
tumor_data <- tumor_data[row.names(tumor_gsva_annotation),]
figure_2a <- pheatmap(t(tumor_data),
scale = "row",
cluster_cols = F,
cluster_rows = F,
annotation_col = tumor_gsva_annotation,
show_colnames = F, fontsize_row = 8,
border_color = NA,
annotation_colors = tumor_gsva_colors,
clustering_distance_cols = "correlation",
color = colorRampPalette(c("steelblue","white","red"))(100),
silent = T)
figure_2a
```
<!-- -->
## Figure 2b
``` r
tumor_NF1_status <- tumor_gsva_annotation %>%
mutate(concordant_subtype = case_when(Richards_2021 == "INJ" & Wang_2017 != "MES" ~ "NO",
Wang_2017 == "MES" & Richards_2021 != "INJ" ~ "NO",
TRUE ~ "YES")) %>%
filter(concordant_subtype == "YES") %>%
mutate(MES_status = ifelse(Richards_2021 == "INJ","MES","Non-MES"))
fisher <- fisher.test(table(tumor_NF1_status$NF1_status,
tumor_NF1_status$MES_status),
alternative="two.sided")
figure_2b_data <- table(tumor_NF1_status$MES_status, tumor_NF1_status$NF1_status) %>%
reshape2::melt(., varnames = c("Subtype_group","NF1_status"), id.vars = "Subtype_group")
figure_2b <- figure_2b_data %>%
group_by(Subtype_group) %>%
mutate(perc = round(value/sum(value),2)*100) %>%
ggplot(aes(x = Subtype_group, y = perc,
fill = NF1_status, cumulative = TRUE)) +
geom_col(width = 0.75) + ylab("percentage") +
theme_pubr(legend = "none") + font_size +
scale_fill_manual(values = c("#FC8D62","#66C2A5")) +
geom_text(aes(label = paste0(perc,"%")),
position = position_stack(vjust = 0.5),
color = "white", size = 3.5) +
ggtitle(sprintf("Fisher's exact test:\n P value = %s",
signif(fisher$p.value,digits = 4)))
figure_2b <- ggpar(figure_2b, font.main = c(8,"black","plain"))
figure_2b
```
<!-- -->
## Figure 2c
``` r
idhwt_nf1_data <- tcga_cgga_data %>%
filter(.,dataset == "TCGA" &
IDH_codel.subtype == "IDHwt" &
NF1_status!=is.na(NF1_status)) %>%
mutate(NF1_status = factor(NF1_status, levels = c("NotAltered","Altered")))
# Panel 2c, NF1_status
figure_2c <- idhwt_nf1_data %>%
ggboxplot(x = "NF1_status", y = "FOSL1",
add = "jitter",
outlier.size = 0, outlier.stroke = 0,
add.params = list(color = "NF1_status",
size = 0.8, alpha = 0.5),
palette = "Set2", legend = "none", title ="",
ylab = "FOSL1 mRNA (log2)",
xlab = "NF1 status") + font_size +
stat_compare_means(method = "t.test", label = "p.format")
# idhwt_nf1_data %>%
# compare_means(FOSL1 ~ NF1_status, method = "t.test",
# data = .,symnum.args = symnum.args)
figure_2c
```
<!-- -->
## Figure 2d
``` r
# Panel 2d, NF1mRNA correlation
figure_2d <- idhwt_nf1_data %>%
ggscatter(x = "NF1", y = "FOSL1", color = "NF1_status",
palette = "Set2", alpha = 0.5, size = 0.8,
add = "reg.line", # Add regression line
add.params = list(color = "black", fill = "gray"),
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE,
legend = "none", title = "",
cor.coeff.args = list(method = "pearson",
label.x = 8, label.y = 3.5,
label.sep = "\n"),
ylab = "FOSL1 mRNA (log2)",
xlab = "NF1 mRNA (log2)") + font_size
# idhwt_nf1_data %>% do(tidy(cor.test(.$NF1, .$FOSL1)))
figure_2d
```
<!-- -->
## Figure 2e
``` r
figure_2e <- qPCR_data %>%
plotqPCR(panel = "2E",
normalizer = "18s",
ref_group = "Ctrl",
levels =c("Ctrl","GRD"),
pvalue = T,
facet_by = "Cells",
legend = "none", palette = black_red,
ylim = c(0,1.25),
ylab = "FOSL1 mRNA (A.U.)")
figure_2e
```
<!-- -->
## Figure 2g
``` r
figure_2g <- gsea_report_all_analysis %>%
filter(Analysis == "BTSC233_NF1_GRD") %>%
ggplot(aes(x = reorder(NAME,NES), y = NES)) +
geom_col(aes(fill = TYPE, color =`FDR.q.val`<0.05), width = 0.75) +
coord_flip() + xlab("") +
theme_cowplot() + theme(axis.text.y=element_text(size = 7)) +
scale_fill_manual(values = c("#F5AE26", "#EA549D")) +
scale_color_manual(values = c("gray","black"))
figure_2g
```
<!-- -->
## Figure 2h
``` r
figure_2h <- qPCR_data %>%
plot_normqPCR(panel = "2H",
normalizer = "18s",
ref_group = "shCtrl",
levels = c("shCtrl","shNF1_1","shNF1_4","shNF1_5"),
pvalue = T,
facet_by = "Cells",
legend = "none",
palette = "Set1",
ylim = c(0,2.5),
ylab = "FOSL1 mRNA (A.U.)")
figure_2h
```
<!-- -->
## Figure 2i
``` r
figure_2i <- gsea_report_all_analysis %>%
filter(Analysis == "BTSC3021_shNF1") %>%
ggplot(aes(x = reorder(NAME,NES), y = NES)) +
geom_col(aes(fill = TYPE, color =`FDR.q.val`<0.05), width = 0.75) +
coord_flip() + xlab("") +
theme_cowplot() + theme(axis.text.y=element_text(size = 7)) +
scale_fill_manual(values = c("#F5AE26", "#EA549D")) +
scale_color_manual(values = c("gray","black"))
figure_2i
```
<!-- -->
## Figure 2j
``` r
figure_2j_left_a <- qPCR_data %>%
filter(Cells == "BTSC 232" & Gene %in% c("18s","FOSL1")) %>%
plotqPCR(panel = "2J",
normalizer = "18s",
ref_group = "Ctrl+Ctrl",
levels = c("Ctrl+Ctrl", "Ctrl+FOSL1",
"GRD+Ctrl","GRD+FOSL1"),
pvalue = F, legend = "none",
palette = paired_black_red,
ylab = NULL)
figure_2j_right_a <- qPCR_data %>%
filter(Cells == "BTSC 232" & Gene != "FOSL1") %>%
plotqPCR(panel = "2J",
normalizer = "18s",
ref_group = "Ctrl+Ctrl",
levels = c("Ctrl+Ctrl", "Ctrl+FOSL1",
"GRD+Ctrl","GRD+FOSL1"),
pvalue = F, legend = "right",
palette = paired_black_red, ylab = NULL) +
rremove("ylab")
figure_2j_left_b <- qPCR_data %>%
filter(Cells == "BTSC 233" & Gene %in% c("18s","FOSL1")) %>%
plotqPCR(panel = "2J",
normalizer = "18s",
ref_group = "Ctrl+Ctrl",
levels = c("Ctrl+Ctrl", "Ctrl+FOSL1",
"GRD+Ctrl","GRD+FOSL1"),
pvalue = F, legend = "none",
palette = paired_black_red, ylab = NULL)
figure_2j_right_b <- qPCR_data %>%
filter(Cells == "BTSC 233" & Gene != "FOSL1") %>%
plotqPCR(panel = "2J",
normalizer = "18s",
ref_group = "Ctrl+Ctrl",
levels = c("Ctrl+Ctrl", "Ctrl+FOSL1",
"GRD+Ctrl","GRD+FOSL1"),
pvalue = F, legend = "right",
palette = paired_black_red, ylab = NULL) +
rremove("ylab")
figure_2j <- ggarrange(figure_2j_left_a, figure_2j_right_a,
figure_2j_left_b, figure_2j_right_b,
widths = c(0.35,1,0.35,1), nrow = 1,
legend = 'right',common.legend = T)
figure_2j
```
<!-- -->
## Figure 2-figure supplement 1d (Figure S3)
``` r
figure_S3d <- as.ggplot(~ replotGSEA(path = "./Data/GSEA/Freiburg_BTSC233_NF1_GRD.Gsea.1557494919416",
gene.set = "BILD_HRAS_ONCOGENIC_SIGNATURE", class.name = "NF1-GRD positively correlated"))
figure_S3d
```
<!-- -->
## Figure 2-figure supplement 2b (Figure S4b)
``` r
figure_S4b <- as.ggplot(~ replotGSEA(path = "./Data/GSEA/Freiburg_BTSC233_NF1_GRD.Gsea.1612877445786",
gene.set = "FOSL1_REGULON",
class.name = "NF1-GRD positively correlated"))
figure_S4b
```
<!-- -->
## Figure 2-figure supplement 2c (Figure S4c)
``` r
figure_S4c_left <- qPCR_data %>%
filter(Cells == "BTSC 233") %>%
plot_normqPCR(panel = "S4C",
ref_group = "Ctrl",
pvalue = T,
facet_by = "Cells",
palette = black_red,
ylim = c(0,1.75),
pvalues_y = 1.6)
figure_S4c_right <- qPCR_data %>%
filter(Cells == "BTSC 232") %>%
plot_normqPCR(panel = "S4C",
ref_group = "Ctrl",
pvalue = T,
facet_by = "Cells",
ylab = FALSE,
palette = black_red,
ylim = c(0,1.75),
remove_y = T,
pvalues_y = 1.6)
figure_S4c <- ggarrange(figure_S4c_left,
figure_S4c_right,
ncol = 2,
common.legend = T,
widths = c(2.25,2),
legend = "right")
figure_S4c
```
<!-- -->
## Figure 2-figure supplement 2f (Figure S4f)
``` r
figure_S4f <- as.ggplot(~ replotGSEA(path = "./Data/GSEA/Freiburg_BTSC3021_NF1_shNF1.Gsea.1612877131389",
gene.set = "FOSL1_REGULON",
class.name = "shNF1 positively correlated"))
figure_S4f
```
<!-- -->
## Figure 2-figure supplement 2g (Figure S4g)
``` r
figure_S4g_left <- qPCR_data %>%
filter(Cells == "BTSC 3021") %>%
plot_normqPCR(panel = "S4G",
ref_group = "shCtrl",
pvalue = F,
facet_by = "Cells",
legend = "right",
palette = brewer.pal(4,"Set1")[c(1,2,4)],
ylim = c(0,3),
pvalues_y = 2.75)
figure_S4g_right <- qPCR_data %>%
filter(Cells == "BTSC 3047") %>%
plot_normqPCR(panel = "S4G",
ref_group = "shCtrl",
pvalue = F,
facet_by = "Cells",
ylab = FALSE,
legend = "right",
palette = brewer.pal(4,"Set1")[c(1,3,4)],
ylim = c(0,3),
remove_y = T,
pvalues_y = 2.75)
figure_S4g <- ggarrange(figure_S4g_left,
figure_S4g_right,
ncol = 2,
common.legend = T,
widths = c(2.25,2),
legend = "right")
figure_S4g
```
<!-- -->
## Figure 2-figure supplement 2h (Figure S4h)
``` r
figure_S4h <- qPCR_data %>%
plot_normqPCR(panel = "S4H",
ref_group = "Ctrl",
pvalue = T,
facet_by = "Cells",
legend = "right",
palette = black_red,
ylim = c(0,1.75),
pvalues_y = 1.6)
figure_S4h
```
<!-- -->
## Figure 2-figure supplement 2i (Figure S4i)
``` r
figure_S4i <- qPCR_data %>%
plot_normqPCR(panel = "S4I",
ref_group = "shCtrl",
pvalue = T,
facet_by = "Cells",
legend = "right",
palette = "Set1",
pvalues_y = 5.5,
ylim = c(0,6))
figure_S4i
```
<!-- -->
## Figure 3b
``` r
figure_3b <- qPCR_data %>%
plotqPCR(panel = "3B",
normalizer = "Gapdh",
ref_group = "Parental",
levels = c("Parental","shNf1","sgNf1","KrasG12V"),
pvalue = T,
legend = "top",
palette = "Set1",
ylim = c(0,8))
figure_3b
```
<!-- -->
## Figure 3d
``` r
figure_3d <- gsea_report_all_analysis %>%
filter(Analysis == "NSCs_Kras_sgFosl1") %>%
ggplot(aes(x = reorder(NAME,NES), y = NES)) +
geom_col(aes(fill = TYPE, color =`FDR.q.val`<0.05), width = 0.75) +
coord_flip() + xlab("") +
theme_cowplot() + theme(axis.text.y=element_text(size = 7)) +
scale_fill_manual(values = c("#F5AE26", "#EA549D")) +
scale_color_manual(values = c("gray","black"))
figure_3d
```
<!-- -->
## Figure 3e
``` r
figure_3e <- qPCR_data %>%
plotqPCR(panel = "3E",
normalizer = "Gapdh",
ref_group = "sgCtrl",
levels = c("sgCtrl","sgFosl1"),
pvalue = T,
legend = "none",
palette = black_red,
ylim = c(0,1.5),
title = "MES genes")
figure_3e
```
<!-- -->
## Figure 3f
``` r
figure_3f <- qPCR_data %>%
filter(Gene != "Dll3") %>%
plotqPCR(panel = "3F",
normalizer = "Gapdh",
ref_group = "sgCtrl",
levels = c("sgCtrl","sgFosl1"),
pvalue = T,
legend = "right",
palette = black_red, ylim = c(0,10),
title = "PN genes")
figure_3f
```
<!-- -->
## Figure 2-figure supplement 3b (Figure S5b)
``` r
figure_S5b_left <- qPCR_data %>%
filter(Cells == "BTSC 3021") %>%
plotqPCR(panel = "S5B",
normalizer = "18s",
ref_group = "shCtrl_DMSO",
levels = c("shCtrl_DMSO","shCtrl_GDC-0623",
"shNF1_5_DMSO","shNF1_5_GDC-0623"),
pvalue = F,
legend = "right",
palette = "Set1",
ylim = c(0,5),
title = "BTSC 3021")
figure_S5b_right <- qPCR_data %>%
filter(Cells == "BTSC 3047") %>%
plotqPCR(panel = "S5B",
normalizer = "18s",
ref_group = "shCtrl_DMSO",
levels = c("shCtrl_DMSO","shCtrl_GDC-0623",
"shNF1_5_DMSO","shNF1_5_GDC-0623"),
pvalue = F,
legend = "right",
palette = "Set1",
ylim = c(0,3),
title = "BTSC 3047")
figure_S5b <- ggarrange(figure_S5b_left,
figure_S5b_right,
common.legend = T,
ncol = 1)
figure_S5b
```
<!-- -->
## Figure 2-figure supplement 3d (Figure S5d)
``` r
figure_S5d <- qPCR_data %>%
plotqPCR(panel = "S5D", normalizer = "Actin",
ref_group = "Parental_DMSO",
levels = c("Parental_DMSO","Parental_Trametinib","Parental_U0126",
"shNf1_DMSO","shNf1_Trametinib","shNf1_U0126",
"KrasG12V_DMSO","KrasG12V_Trametinib","KrasG12V_U0126"),
pvalue = F,legend = "top", palette = "Set1")
figure_S5d
```
<!-- -->
## Figure 2-figure supplement 3f (Figure S5f)
``` r
figure_S5f <- gsea_report_all_analysis %>%
filter(Analysis == "NSCs_shNF1_sgFosl1") %>%
ggplot(aes(x = reorder(NAME,NES), y = NES)) +
geom_col(aes(fill = TYPE, color =`FDR.q.val`< 0.1), width = 0.75) +
coord_flip() + xlab("") +
theme_cowplot() + theme(axis.text.y=element_text(size = 7)) +
scale_fill_manual(values = c("#F5AE26", "#EA549D")) +
scale_color_manual(values = c("gray","black"))
figure_S5f
```
<!-- -->
## Figure 2-figure supplement 3g (Figure S5g)
``` r
figure_S5g_left <- qPCR_data %>%
plotqPCR(panel = "S5G_left", normalizer = "Gapdh",
ref_group = "sgCtrl",
levels = c("sgCtrl","sgFosl1_1","sgFosl1_3"),
pvalue = F,
ylim = c(0,1.5),
legend = "right",
palette = black_red_green,
title = "MES genes")
figure_S5g_right <- qPCR_data %>%
plotqPCR(panel = "S5G_right", normalizer = "Gapdh",
ref_group = "sgCtrl",
levels = c("sgCtrl","sgFosl1_1","sgFosl1_3"),
pvalue = F,
ylim = c(0,4),
legend = "right",
palette = black_red_green,
title = "PN genes")
figure_S5g <- ggarrange(figure_S5g_left,
figure_S5g_right,
common.legend = T,
legend = "right",
nrow = 1,widths = c(1.5,1))
figure_S5g
```
<!-- -->
## Figure 4a
``` r
ATACSeq_sample_cor <- getSampleCorrelation(Figure_4_data)
ATACSeq_cor_annotation <- as.data.frame(colData(Figure_4_data))[,c("depth", "gRNA")]
ATACSeq_cor_colors <- list(gRNA = black_red_green)
names(ATACSeq_cor_colors$gRNA) <- levels(factor(ATACSeq_cor_annotation$gRNA))
figure_4a <- pheatmap(as.dist(ATACSeq_sample_cor),
annotation_row = ATACSeq_cor_annotation,
annotation_colors = ATACSeq_cor_colors,
clustering_distance_rows = as.dist(1-ATACSeq_sample_cor),
clustering_distance_cols = as.dist(1-ATACSeq_sample_cor),
silent = T)
figure_4a
```
<!-- -->
## Figure 4b
``` r
# Getting differential deviation between control and KO samples
difdev <- differentialDeviations(Figure_4_data, "Cell_type")
difdev_sig <- difdev[difdev$p_value_adjusted < 0.05,]
difdev_sig <- difdev_sig[order(difdev_sig$p_value_adjusted),]
difdev_sig$TFs <- gsub(".*_", "", rownames(difdev_sig))
#Plot tSNE maps based with samples clustered based on chromatin deviations
set.seed(1234)
tsne_results <- deviationsTsne(Figure_4_data, threshold = 3)
tsne_plots <- plotDeviationsTsne2(Figure_4_data,
tsne_results,
annotation = head(difdev_sig$TFs,6),
sample_column = "gRNA")
#plot the top 5 differentially deviating motifs between Fosl1 WT and KO cells
figure_4b <- ggarrange(tsne_plots[[1]]+ xlab(""),
tsne_plots[[2]]+ xlab("") + ylab(""),
tsne_plots[[3]]+ xlab("") + ylab(""),
tsne_plots[[4]],
tsne_plots[[5]]+ ylab(""),
tsne_plots[[6]]+ ylab(""),
widths = 80, heights = 80, ncol = 3, nrow = 2)
figure_4b
```
<!-- -->
## Figure 4c
``` r
# motifs with decreased accessibility upon Fosl1 KO
KO_down <- figure_4C_data[figure_4C_data$zmean_diff < 0,]
KO_down <- KO_down[order(KO_down$zmean_diff),]
# motifs with increased accessibility upon Fosl1 KO
KO_up <- figure_4C_data[figure_4C_data$zmean_diff > 0,]
KO_up <- KO_up[order(-KO_up$zmean_diff),]
figure_4c <-figure_4C_data %>%
ggplot(aes(x = zmean_diff, y = -log10(p_value_adjusted),
labels=TFs, color = zmean_diff)) +
geom_point(alpha = 0.5)+
geom_vline(xintercept = 0, colour="grey", linetype="dashed") +
geom_point(size = 1) +
scale_color_gradient2(name = "",
mid = "lightgray",
low = "blue",
high = "red") +
ylim(0,8) + xlim(-19,19) +
ggtitle("Fosl1 KO vs Ctrl") +
geom_text_repel(data = head(KO_down,5), aes(label=TFs),
size = 3,
box.padding = 0.35,
point.padding = 0.5,
segment.size = 0.2,
force = 60,
segment.color = "grey50") +
geom_text_repel(data = head(KO_up,5), aes(label=TFs),
size = 3,
box.padding = 0.35,
point.padding = 0.5,
segment.size = 0.2,
force = 60,
segment.color = "grey50") +
labs(x = "Bias corrected deviations", y = "-log10(padj)") +
theme_pubr(margin = T) + font_size +
theme(legend.position = c(0.8,0.8),legend.key.size = unit(0.75,"line"),
legend.direction = "horizontal", legend.text = element_text(size = 8))
figure_4c
```
<!-- -->
## Figure 4d
``` r
ego_down <- enrichGO(gene = as.vector(KO_down$TFs),
OrgDb = 'org.Hs.eg.db',
keyType = 'SYMBOL',
pvalueCutoff= 0.01,
maxGSSize = 500,
pAdjustMethod = "BH",
ont = "BP")
ego_up <- enrichGO(gene = as.vector(KO_up$TFs),
OrgDb = 'org.Hs.eg.db',
keyType = 'SYMBOL',
pvalueCutoff= 0.01,
maxGSSize = 500,
pAdjustMethod = "BH",
ont = "BP")
figure_4d <- emaplot(ego_down, showCategory=10,
pie_scale = 0.5,
line_scale = 0.5,
color = "p.adjust",
text_size = 25) +
theme(text = element_text(size = 8)) +
ggtitle("GO pathways enriched \nin Fosl1-KO closed regions")
figure_4d
```
<!-- -->
## Figure 4e
``` r
figure_4e <-emaplot(ego_up, showCategory=10,
pie_scale=0.5,
line_scale = 0.5,
color = "p.adjust",
text_size = 25) +
theme(text = element_text(size = 8)) +
ggtitle("GO pathways enriched \nin Fosl1-KO open regions")
figure_4e
```
<!-- -->
## Figure 4f
``` r
seqmonk_count <- figure_4F_data
DE_probes <- data.frame(name = seqmonk_count$Feature,
baseMean = rowMeans(seqmonk_count[16:27]),
log2FoldChange = -seqmonk_count$Log2.Fold.Change..LIMMA.stats.p.1.0.after.correction.,
padj = seqmonk_count$FDR..LIMMA.stats.p.1.0.after.correction.,
stringsAsFactors = F)
DE_probes <- arrange(DE_probes,-log2FoldChange)
Wang_MES <- c(gene_signatures[["Wang_MES_2017"]])
Wang_PN <- c(gene_signatures[["Wang_PN_2017"]])
BTSC_MES <- subset(combo_eset_tT, logFC>0)$Gene.Symbol
BTSC_NonMES <- subset(combo_eset_tT, logFC<0)$Gene.Symbol
DE_probes <- DE_probes %>%
mutate(Wang_2017 = case_when(toupper(name) %in% Wang_MES ~ "MES",
toupper(name) %in% Wang_PN ~ "PN",
TRUE ~ "Other"),
Wang_2017 = factor(Wang_2017,
levels = c("MES","PN","Other")),
BTSCs_DE = case_when(toupper(name) %in% BTSC_MES ~ "MES",
toupper(name) %in% BTSC_NonMES ~ "NonMES",
TRUE ~ "Other"))
figure_4f <- DE_probes %>%
ggplot(aes(x=log2FoldChange, y = Wang_2017)) +
geom_density_ridges(aes(fill = Wang_2017),
alpha = 0.7,
scale = 1) +
xlim(-4,4) +
geom_vline(xintercept = c(-1,0,1),
linetype = "dashed",
size = 0.25) +
scale_fill_manual(values = c("#F5AE26", "#EA549D","#4DAF4A")) +
theme_pubr(legend = "none") + font_size +
ggtitle("Wang_2017") +
rremove("ylab")
figure_4f
```
<!-- -->
## Figure 4g
``` r
figure_4g <- DE_probes %>%
ggplot(aes(x=log2FoldChange, y = BTSCs_DE)) +
geom_density_ridges(aes(fill = BTSCs_DE),
alpha = 0.7,
scale = 1) +
xlim(-4,4) +
geom_vline(xintercept = c(-1,0,1),
linetype = "dashed",
size = 0.25) +
scale_fill_manual(values = c("#F5AE26", "#EA549D","#4DAF4A")) +
theme_pubr(legend = "none") + font_size +
ggtitle("BTSC_DE") +
rremove("ylab")
figure_4g
```
<!-- -->
## Figure 4h
``` r
figure_4h_left <- tracksPlot(figure_4F_data,
gene = "Bnc2",
region.min = -120000,
region.max = 5000)
figure_4h_right <- tracksPlot(figure_4F_data,
gene = "Sox11",
bigwig.ymax = 50)
```
<img src="README/README-Figure 4h-1.png" width="50%" /><img src="README/README-Figure 4h-2.png" width="50%" />
## Figure 5a
``` r
mean_d1 <- figure_5A_data %>%
filter(day == "d1") %>%
group_by(Sample, day) %>%
summarise_all(mean) %>%
dplyr::select(-day) %>%
dplyr::rename(d1_mean = value)
figure_5A_data <- full_join(figure_5A_data, mean_d1,"Sample") %>%
mutate(value_norm = value/d1_mean)
figure_5a <- figure_5A_data %>%
ggline(x = "day", y = "value_norm",
add = c("mean_sd", "jitter"),
ylab = "Cell growth (A.U.)",
add.params =list(size = 0.5),
color = "Sample",
palette = black_red_green) +
ylim(0,30) + font_size
# # Two-way ANOVA
# figure_5A_data %>%
# filter(Sample != "sgFosl1_1") %>%
# aov(value_norm ~ Sample * day, data = .) %>%
# summary()
#
# figure_5A_data %>%
# filter(Sample != "sgFosl1_3") %>%
# aov(value_norm ~ Sample * day, data = .) %>%
# summary()
figure_5a
```
<!-- -->
## Figure 5b
``` r
figure_5b <- figure_5B_data %>%
ggbarplot(x = "Phase", y = "Percentage",
add = c("mean_sd", "jitter"),
ylab = "% cell population",
position = position_dodge(0.8),
color = "Sample",
palette = black_red_green) +
scale_y_continuous(expand = c(0, 0), limits = c(0,50))
# figure_5B_data %>%
# compare_means(Percentage ~ Sample, ref.group = "sgCtrl", method = "t.test",
# symnum.args = symnum.args,data = ., group.by = "Phase")
figure_5b
```
<!-- -->
## Figure 5c
``` r
elda_5C <- elda(response = figure_5C_data$Response, dose = figure_5C_data$Dose,
tested = figure_5C_data$Tested,group = figure_5C_data$Group)
# elda_bar(elda_5C,c("Black","Red"))
figure_5c <- ggplotlimdil(elda_5C)
# elda_5C # Pvalue for the limited dilution assay
figure_5c
```
<!-- -->
## Figure 5d
``` r
QuantSeq_gset <- ExpressionSet(assayData = as.matrix(t(figure_5D_expr)),
phenoData=as(figure_5D_pdata,
"AnnotatedDataFrame"))
col_annotation <- stem_diff_genes %>%
dplyr::rename(Marker = Group) %>%
filter(., !Gene_symbol %in% c("Nanog","Sall4")) %>% # filter out genes with very low expr
mutate(Marker = as.factor(Marker)) # %>% column_to_rownames("Gene_symbol")
QuantSeq_colors <- list(Marker = brewer.pal(9, "Set1")[3:6],
sgRNA = c("#808080","#000000"))
names(QuantSeq_colors$Marker) <- levels(col_annotation$Marker)
names(QuantSeq_colors$sgRNA) <- levels(factor(pData(QuantSeq_gset)[,"sgRNA"]))
figure_5d <- exprs(QuantSeq_gset[featureNames(QuantSeq_gset) %in% row.names(stem_diff_genes),]) %>%
data.frame(.) %>% # reorder based on the order of stem_diff_genes
.[row.names(col_annotation),] %>% # reorder based on the order of stem_diff_genes
na.omit(.) %>% # Pou5f1 (Oct4) it's not expressed
pheatmap(.,
annotation_col = pData(QuantSeq_gset)[,"sgRNA",drop = F],
annotation_row = col_annotation[,"Marker", drop = F],
gaps_row=c(13,18,24),
scale = "row",
show_rownames = T,
show_colnames = F,
fontsize_row = 8,
annotation_colors = QuantSeq_colors,
border_color = NA,
cluster_rows = F,
cluster_cols = F,
annotation_legend = F,
color = colorRampPalette(c("steelblue","white","red"))(100),
silent = T)
figure_5d
```
<!-- -->
## Figure 5e
``` r
figure_5e <- figure_5E_data %>%
ggplot(aes(x = sgRNA, y = norm_value, color = sgRNA)) +
geom_boxplot(outlier.size = 0, outlier.stroke = 0) +
geom_jitter(position = position_jitter(width = .25),
size = 1, alpha = 0.5) +
ylab("Fold change (A.U.)") + xlab("") + theme_bw() +
facet_wrap(.~ Marker, scales = "free") +
theme(legend.position = "none",
axis.title.y = element_text(size = 10)) +
scale_color_manual(values = black_red_green) +
stat_compare_means(method = "t.test",
ref.group = "sgCtrl",
label = "p.signif",
symnum.args = symnum.args)
figure_5e
```
<!-- -->
## Figure 5i
``` r
figure_5i <- qPCR_data %>%
plotqPCR(panel = "5I",
normalizer = "Gapdh",
ref_group = "sgCtrl-T4",
levels = c("sgCtrl-T4","sgFosl1-T3","sgFosl1-T4"),
pvalue = T,
legend = "none",
palette = black_red_green,
ylim = c(0,1.5),
title = "MES genes")
figure_5i
```
<!-- -->
## Figure 5j
``` r
figure_5j <- qPCR_data %>%
plotqPCR(panel = "5J",
normalizer = "Gapdh",
ref_group = "sgCtrl-T4",
levels = c("sgCtrl-T4","sgFosl1-T3","sgFosl1-T4"),
pvalue = T,
legend = "right",
palette = black_red_green,
ylim = c(0,50),
title = "PN genes")
figure_5j
```
<!-- -->
## Figure 5-figure supplement 1a (Figure S6a)
``` r
cyclins <- c("Ccn1","Ccn2", "Ccn4", "Ccnb1", "Ccnb2","Ccnd2", "Ccne1","Ccne2","Cdk1")
figure_S6a <- exprs(QuantSeq_gset[featureNames(QuantSeq_gset) %in% cyclins,]) %>%
data.frame(.) %>%
.[cyclins,] %>% # reorder based on the order of cyclins
t(.) %>%
pheatmap(.,
annotation_row = pData(QuantSeq_gset)[,"sgRNA",drop = F],
scale = "column",
show_rownames = F,
show_colnames = T,
fontsize_row = 8,
annotation_colors = QuantSeq_colors,
border_color = NA,
cluster_rows = F,
cluster_cols = F,
annotation_legend = T,
color = colorRampPalette(c("steelblue","white","red"))(100),
silent = T)
figure_S6a
```
<!-- -->
## Figure 6b
``` r
figure_6b_left <- qPCR_data %>%
filter(Gene %in% c("Gapdh","Fosl1")) %>%
plotqPCR(panel = "6B",
normalizer = "Gapdh",
ref_group = "Mock",
levels = c("Mock","Dox"),
pvalue = T,
legend = "none",
palette = gray_black,
ylim=c(0,200),
title = "MES genes")
figure_6b_right <- qPCR_data %>%
filter(Gene!= "Fosl1") %>%
plotqPCR(panel = "6B",
normalizer = "Gapdh",
ref_group = "Mock",
levels = c("Mock","Dox"),
pvalue = T, ylab = FALSE,
legend = "none",
palette = gray_black,
ylim=c(0,15),
title = " ")
figure_6b <- plot_grid(figure_6b_left,
figure_6b_right,
rel_widths = c(0.3,1))
figure_6b
```
<!-- -->
## Figure 6c
``` r
figure_6c <- qPCR_data %>%
plotqPCR(panel = "6C",
normalizer = "Gapdh",
ref_group = "Mock",
levels = c("Mock","Dox"),
pvalue = T,
legend = "right",
palette = gray_black,
ylim=c(0,1.5),
title = "PN genes")
figure_6c
```
<!-- -->
## Figure 6d
``` r
figure_6d <- figure_6D_data %>%
ggplot(aes(x = Group, y = Area_scaled, color = Group)) +
geom_boxplot(outlier.size = 0, outlier.stroke = 0) +
geom_jitter(position = position_jitter(width = .25), size = 1) +
ylab("Tumor Area") + xlab("") + theme_bw() + ylim(0,2.8) +
theme(legend.position = "none", axis.title.y = element_text(size = 10)) +
scale_color_manual(values = c("#A6CEE3", "#1F78B4")) +
stat_compare_means(method = "t.test", ref.group = "Mock",
label = "p.format", symnum.args = symnum.args)
figure_6d
```
<!-- -->
## Figure 6f
``` r
figure_6f_left <- qPCR_data %>%
filter(Gene %in% c("Gapdh","Fosl1")) %>%
plotqPCR(panel = "6F",
normalizer = "Gapdh",
ref_group = "Mock",
levels = c("Mock","Dox"),
pvalue = T,
legend = "none",
palette = c("#A6CEE3", "#1F78B4"),
ylim=c(0,40),
title = "MES genes")
figure_6f_right <- qPCR_data %>%
filter(Gene!= "Fosl1") %>%
plotqPCR(panel = "6F",
normalizer = "Gapdh",
ref_group = "Mock",
levels = c("Mock","Dox"),
pvalue = T, ylab = FALSE,
legend = "none",
palette = c("#A6CEE3", "#1F78B4"),
ylim=c(0,6),
title = " ")
figure_6f <- plot_grid(figure_6f_left,
figure_6f_right,
rel_widths = c(0.3,1))
figure_6f
```
<!-- -->
## Figure 6g
``` r
figure_6g <- qPCR_data %>%
plotqPCR(panel = "6G",
normalizer = "Gapdh",
ref_group = "Mock",
levels = c("Mock","Dox"),
pvalue = T,
legend = "right",
palette = c("#A6CEE3", "#1F78B4"),
ylim=c(0,2),
title = "PN genes")
figure_6g
```
<!-- -->
## Figure 6i
``` r
figure_6i <- qPCR_data %>%
plotqPCR(panel = "6I",
normalizer = "Gapdh",
ref_group = "Dox",
levels = c("Dox","Mock"),
pvalue = T,
legend = "none",
palette = c("#E31A1C","#FB9A99"),
ylim=c(0,2.5),
title = "MES genes")
figure_6i
```
<!-- -->
## Figure 6j
``` r
figure_6j <- qPCR_data %>%
plotqPCR(panel = "6J",
normalizer = "Gapdh",
ref_group = "Dox",
levels = c("Dox","Mock"),
pvalue = T,
legend = "right",
palette = c("#E31A1C","#FB9A99"),
ylim=c(0,10),
title = "PN genes")
figure_6j
```
<!-- -->
## Figure 7c
``` r
mean_7C_d1 <- figure_7C_data %>%
filter(day == "d1") %>%
group_by(Sample, Treatment, day) %>%
summarise_all(mean) %>%
dplyr::select(-day) %>%
dplyr::rename(d1_mean = value)
figure_7C_data_norm <- full_join(figure_7C_data, mean_7C_d1,
by = c("Sample", "Treatment")) %>%
mutate(value_norm = value/d1_mean,
Sample_treatment = paste(Sample, Treatment, sep = ""))
figure_7c <- figure_7C_data_norm %>%
ggline(x = "day", y = "value_norm",
add = c("mean_sd", "jitter"),
ylab = "Cell growth (A.U.)",
add.params =list(size = 1),
color = "Sample_treatment",
palette = "Paired") +
theme(legend.position = c(.05, .95),
legend.justification = c("left", "top")) +
font_size
# figure_7C_data_norm %>%
# filter(Sample == "shFOSL1_10") %>%
# aov(value_norm ~ Treatment + day, data = .) %>%
# summary()
#
# figure_7C_data_norm %>%
# filter(Sample == "shFOSL1_3") %>%
# aov(value_norm ~ Treatment + day, data = .) %>%
# summary()
# figure_7C_data_norm %>%
# compare_means(value_norm ~ Treatment,
# group1 = "Mock",
# group2 = "Dox",
# method = "anova",
# symnum.args = symnum.args,data = .,
# group.by = "Sample")
figure_7c
```
<!-- -->
## Figure 7d
``` r
figure_7d <- figure_7D_data %>%
mutate(Sample = factor(Sample,
levels = c("shGFP", "shFOSL1_3","shFOSL1_10")),
Sample_treatment = paste(Sample, Treatment, sep = "")) %>%
ggbarplot(x = "Sample",
y = "BrdU",
position = position_dodge(0.8),
add = c("mean_sd", "jitter"),
ylab = "% BrdU+ cells",
color = "Sample_treatment",
palette = "Paired", legend = "none") +
scale_y_continuous(expand = c(0, 0), limits = c(0,40)) + font_size
# figure_7D_data %>%
# compare_means(BrdU ~ Treatment,
# ref.group = "-Dox",
# method = "t.test",
# symnum.args = symnum.args,data = .,
# group.by = "Sample", var.equal = T)
# figure_7D_data %>%
# compare_means(BrdU ~ Treatment, group1 = "-Dox", group2 = "+Dox", method = "anova",
# symnum.args = symnum.args,data = ., group.by = "Sample")
figure_7d
```
<!-- -->
## Figure 7e
``` r
elda_7e_gfp <- figure_7E_data %>%
filter(shRNA =="shGFP") %>%
with(., elda(response = Response,
dose = Dose,
tested = Tested,
group = Group))
elda_7e_FOSL1_3 <- figure_7E_data %>%
filter(shRNA =="shFOSL1_3") %>%
with(., elda(response = Response,
dose = Dose,
tested = Tested,
group = Group))
elda_7e_FOSL1_10 <- figure_7E_data %>%
filter(shRNA =="shFOSL1_10") %>%
with(., elda(response = Response,
dose = Dose,
tested = Tested,
group = Group))
# elda_7e_gfp; elda_7e_FOSL1_3; elda_7e_FOSL1_10 # Pvalue for the limited dilution assay
# elda_bar(elda_7e_gfp, brewer.pal(6,"Paired")[5:6])
# elda_bar(elda_7e_FOSL1_3, brewer.pal(6,"Paired")[3:4])
# elda_bar(elda_7e_FOSL1_10, brewer.pal(6,"Paired")[1:2])
#
figure_7e <- plot_grid(ggplotlimdil(elda_7e_gfp,
col.group = brewer.pal(6,"Paired")[5:6]) +
font_size,
ggplotlimdil(elda_7e_FOSL1_3,
col.group = brewer.pal(6,"Paired")[3:4]) +
font_size,
ggplotlimdil(elda_7e_FOSL1_10,
col.group = brewer.pal(6,"Paired")[1:2]) +
font_size,
nrow = 1)
figure_7e
```
<!-- -->
## Figure 7f
``` r
#PCA
pc <- prcomp(t(figure_7F_data[,-c(1:12)]))
pc_matrix <- data.frame(pc$x)
percentage <- round(pc$sdev^2/ sum(pc$sdev^2) * 100, 2)
percentage <- paste(colnames(pc_matrix), "(",
paste(as.character(percentage), "%", ")", sep=""),sep = "")
pc_matrix$Group <- figure_7F_annotation$Group
figure_7f <- pc_matrix %>%
ggscatter(x = "PC1", y = "PC2",
size = 1.5,
color = "Group",
palette = c("orange","maroon1"),
ellipse = F,
xlab = percentage[1],
ylab = percentage[2],
legend = c(0.8,0.25)) +
font_size
figure_7f
```
<!-- -->
## Figure 7g
``` r
# BTSC_MES_probes <- figure_7G_data %>%
# filter(Feature %in% BTSC_MES) %>%
# arrange(-Log2.Fold.Change)
# Wang_MES_probes <- figure_7G_data %>%
# filter(Feature %in% Wang_MES) %>%
# arrange(-Log2.Fold.Change)
#Volcano plot
figure_7g <- figure_7G_data %>%
mutate(Fold_group = case_when(Log2.Fold.Change > 2 ~ "Lfc>",
Log2.Fold.Change < -2 ~ "Lfc<-",
TRUE ~ "Lfc")) %>%
ggplot(aes(x = Log2.Fold.Change, y = -log10(P.value))) +
geom_point(aes(col = Fold_group),size = 0.01, alpha = 0.5) +
scale_color_manual(values = c("gray","steelblue","red")) +
xlim(-5.5,5.5) + ylim(0,45) +
geom_vline(xintercept = 0, colour="grey", linetype="dashed") +
theme_pubr(legend = "none") + font_size +
ggtitle("MES vs Non-MES")
figure_7g
```
<!-- -->
## Figure 7j
``` r
figure_7j <- qPCR_data %>%
plotChIP(panel = "7J",
ref_group = "IgG",
levels = c("IgG","FRA1"),
palette = gray_black,
pvalue =T,
ylim=c(0,0.03),
pvalues_y = 0.028)
figure_7j
```
<!-- -->
## Figure 7-figure supplement 1b (Figure S8b)
``` r
mean_S8B_d1 <- figure_S8B_data %>%
filter(day == "d1") %>%
group_by(Sample, Treatment, day) %>%
summarise_all(mean) %>%
dplyr::select(-day) %>%
dplyr::rename(d1_mean = value)
figure_S8B_data_norm <- full_join(figure_S8B_data,mean_S8B_d1,
by = c("Sample", "Treatment")) %>%
mutate(value_norm = value/d1_mean,
Sample_treatment = paste(Sample, Treatment, sep = " "))
# figure_S8B_data_norm %>%
# filter(Sample == "shFOSL1_3") %>%
# aov(value_norm ~ Treatment * day, data = .) %>%
# summary()
figure_S8b <- figure_S8B_data_norm %>%
ggline(x = "day", y = "value_norm",
add = c("mean_sd", "jitter"),
ylab = "Cell growth (A.U.)",
add.params =list(size = 1),
color = "Sample_treatment",
palette = "Paired")
figure_S8b
```
<!-- -->
## Figure 7-figure supplement 1c (Figure S8c)
``` r
figure_S8c <- figure_S8C_data %>%
mutate(Sample = factor(Sample, levels = c("shGFP", "shFOSL1_3")),
Sample_treatment = paste(Sample, Treatment, sep = "")) %>%
ggbarplot(x = "Sample", y = "BrdU",
position = position_dodge(0.8),
add = c("mean_sd", "jitter"),
ylab = "% BrdU+ cells",
color = "Sample_treatment",
palette = "Paired",
legend = "none") +
scale_y_continuous(expand = c(0, 0), limits = c(0,25))
# figure_S8C_data %>%
# compare_means(BrdU ~ Treatment, group1 = "Mock", group2 = "Dox", method = "anova",
# symnum.args = symnum.args,data = ., group.by = "Sample")
# figure_S8C_data %>%
# compare_means(BrdU ~ Treatment, ref.group = "Mock", method = "t.test",
# symnum.args = symnum.args,data = ., group.by = "Sample", alternative = "less", var.equal = T)
figure_S8c
```
<!-- -->
## Figure 7-figure supplement 1d (Figure S8d)
``` r
elda_S8d <- elda(response = figure_S8D_data$Response,
dose = figure_S8D_data$Dose,
tested = figure_S8D_data$Tested,
group = figure_S8D_data$Group)
figure_S8d <- ggplotlimdil(elda_S8d,
col.group = brewer.pal(6,"Paired")[1:2]) +
font_size
# elda_bar(elda_S8d, brewer.pal(6,"Paired")[1:2])
# elda_S8d # Pvalue for the limited dilution assay
figure_S8d
```
<!-- -->
## Figure 7-figure supplement 1e (Figure S8e)
``` r
figure_S8e_left <- qPCR_data %>%
plotqPCR(panel = "S8E_left",
normalizer = "GAPDH",
ref_group = "Mock",
levels = c("Mock","Dox"),
pvalue = T,
title = "MES genes",
legend = "none",
palette = c("#A6CEE3", "#1F78B4"),
ylim=c(0,2)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
figure_S8e_right <- qPCR_data %>%
plotqPCR(panel = "S8E_right",
normalizer = "GAPDH",
ref_group = "Mock",
levels = c("Mock","Dox"),
pvalue = T,
title = "PN genes",
ylab = FALSE,
legend = "right",
palette = c("#A6CEE3", "#1F78B4"),
ylim=c(0,4), pvalues_y = 3.7) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
figure_S8e <- plot_grid(figure_S8e_left,
figure_S8e_right,
rel_widths = c(1.2,0.75),
align = "h")
figure_S8e
```
<!-- -->
## Figure 7-figure supplement 1f (Figure S8g)
``` r
mean_S8G_d1 <- figure_S8G_data %>%
filter(day == "d1") %>%
group_by(Sample, Treatment, day) %>%
summarise_all(mean) %>%
dplyr::select(-day) %>%
dplyr::rename(d1_mean = value)
figure_S8G_data_norm <- full_join(figure_S8G_data,mean_S8G_d1,
by = c("Sample", "Treatment")) %>%
mutate(value_norm = value/d1_mean,
Sample_treatment = paste(Sample, Treatment, sep = " "))
# figure_S8G_data_norm %>%
# filter(Sample == "shGFP") %>%
# aov(value_norm ~ Treatment * day, data = .) %>%
# summary()
figure_S8g <- figure_S8G_data_norm %>%
ggline(x = "day", y = "value_norm",
add = c("mean_sd", "jitter"),
ylab = "Cell growth (A.U.)",
add.params =list(size = 1),
color = "Sample_treatment",
palette = "Paired",
legend = "right")
figure_S8g
```
<!-- -->
## Figure 7-figure supplement 1i (Figure S8i)
``` r
mean_S8I_d1 <- figure_S8I_data %>%
filter(day == "d1") %>%
group_by(Cell_line, Group, day) %>%
summarise_all(mean) %>%
dplyr::select(-day) %>%
dplyr::rename(d1_mean = value)
figure_S8I_data_norm <- full_join(figure_S8I_data,
mean_S8I_d1,
by = c("Cell_line", "Group")) %>%
mutate(value_norm = value/d1_mean)
figure_S8i <- figure_S8I_data_norm %>%
ggline(x = "day", y = "value_norm",
facet.by = "Cell_line",
add = c("mean_sd", "jitter"),
ylab = "Cell growth (A.U.)",
add.params =list(size = 1),
color = "Group",
palette = "Set1",
legend = "right")
figure_S8i
```
<!-- -->
## Figure 7-figure supplement 1k (Figure S8k)
``` r
mean_S8K_d1 <- figure_S8K_data %>%
filter(day == "d1") %>%
group_by(Cell_line, Sample, Treatment, day) %>%
summarise_all(mean) %>%
dplyr::select(-day) %>%
dplyr::rename(d1_mean = value)
figure_S8K_data_norm <- full_join(figure_S8K_data, mean_S8K_d1,
by = c("Cell_line","Sample", "Treatment")) %>%
mutate(value_norm = value/d1_mean,
Sample_treatment = paste(Sample, Treatment, sep = " "))
figure_S8k <- figure_S8K_data_norm %>%
ggline(x = "day", y = "value_norm",
add = c("mean_sd", "jitter"),
facet.by = "Cell_line",
ylab = "Cell growth (A.U.)",
add.params =list(size = 1),
color = "Sample_treatment",
palette = "Paired",
legend = "right")
# figure_S8K_data_norm %>%
# filter("Cell_line" == "h543" & Sample == "shGFP") %>%
# aov(value_norm ~ Treatment * day, data = .) %>%
# summary()
figure_S8k
```
<!-- -->
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