Last updated: 2021-03-22
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Rmd | f3ed263 | aluetge | 2021-01-14 | code for supplementary figures |
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load packages
library(DESeq2)
library(tidyverse)
library(ggsci)
library(matrixStats)
library(piano)
library(reshape2)
library(genefilter)
library(Biobase)
library(ComplexHeatmap)
library(ggplot2)
library(gtable)
library(grid)
library(circlize)
library(gridExtra)
library(ggpubr)
library(RColorBrewer)
library(here)
library(clusterProfiler)
library(msigdbr)
library(org.Hs.eg.db)
library(enrichplot)
load data
data_dir <- here("data")
output_dir <- here("output")
figure_dir <- here("output/figures")
#dds data set. gene expression data + patmetadata
load(paste0(data_dir, "/ddsrnaCLL_150218.RData"))
variant <- "IGHV"
#filter for patients without NA in variant
ddsCLL <- ddsCLL[, !is.na(colData(ddsCLL)[,variant])]
#differentially expressed genes between IGHV groups (see differential expression.html)
diff_all <- read.csv(file=paste0(output_dir, "/diff_genes/", variant, "_diffGenes.csv"))
rownames(diff_all) <- diff_all$X
diff_all <- diff_all[which(diff_all$padj < 0.01 ),-1]
diff <- diff_all[which(abs(diff_all$stat) > 8),]
mutStatus <- data.frame(colData(ddsCLL)) %>% arrange(IGHV)
colnames(ddsCLL) <-colData(ddsCLL)$PatID
ddsCLL <- ddsCLL[, mutStatus$PatID]
#expression data
ddsCLL <- estimateSizeFactors(ddsCLL)
RNAnorm <- varianceStabilizingTransformation(ddsCLL, blind = T)
Expression matrix
#filter for sign. genes in variant
exprMat <- assay(RNAnorm)
exprVariant <- exprMat[rownames(diff),]
colnames(exprVariant) <- colData(ddsCLL)$PatID
exprVariant.new <- log2(exprVariant)
exprVariant.new <- t(scale(t(exprVariant.new)))
exprVariant.new[exprVariant.new > 4] <- 4
exprVariant.new[exprVariant.new < -4] <- -4
rownames(exprVariant.new) <- rowData(RNAnorm[rownames(diff),])$symbol
#colors
colors = colorRamp2(c(-4,-1,0,1,4), c("#2166ac","#4393c3", "#f7f7f7", "#d6604d","#b2182b"))
annocol <- get_palette("jco", 10)
annocolor <- list(IGHV = c("M" = annocol[1], "U" = annocol[2]))
rowcolors <-colorRampPalette(brewer.pal(5, "Set1"))(5)
rowcolors[6] <- "white"
feature <- as.data.frame(colData(ddsCLL)[,c(variant)])
colnames(feature) <- c(variant)
rownames(feature) <- colnames(RNAnorm)
ha_col <- HeatmapAnnotation(df = feature, col = annocolor, annotation_height = unit(c(rep(1.9, 1)), "cm"), annotation_legend_param = list(title_gp = gpar(fontsize = 17), labels_gp = gpar(fontsize = 15), grid_height = unit(0.7, "cm"), grid_width = unit(0.3, "cm")))
h1 <- Heatmap(exprVariant.new,
km = 2,
gap = unit(0.5, "cm"),
cluster_columns = F,
clustering_distance_rows = "pearson",
clustering_method_rows = "ward.D2",
column_title = paste0("Gene signature: ", variant),
col = colors,
column_title_gp = gpar(fontsize = 20, fontface = "bold"),
heatmap_legend_param = list(title = "expr",
title_gp = gpar(fontsize = 20),
grid_height = unit(0.7, "cm"),
grid_width = unit(0.3, "cm"),
gap = unit(2, "cm"),
labels_gp = gpar(fontsize = 15)),
column_dend_height = unit(1, "cm"),
show_row_dend = FALSE,
show_column_names = FALSE ,
row_title_gp = gpar(fontsize = 0),
show_row_names = FALSE,
row_names_gp = gpar(fontsize = 0),
top_annotation = ha_col)
#Annotate top 50 genes
sub_names <- diff[1:50,"Symbol"]
sub_names <- sub_names[-which(sub_names %in% "")]
sub_names <- c(as.character(sub_names), "CD38", "LPL", "ZAP70", "SEPT10", "ADAM29", "PEG10")
#
geneIDs <- which(rownames(exprVariant.new) %in% sub_names)
labels <- rownames(exprVariant.new)[geneIDs]
ha_genes <- rowAnnotation(link = row_anno_link(at = geneIDs, labels = labels, labels_gp = gpar(fontsize = 13)), width = unit(2.5, "cm"))
Warning: anno_link() is deprecated, please use anno_mark() instead.
#svg(filename=paste0(figure_dir, "/", variant, "_gene_expr.svg"), width=30, height=45)
#pdf(file=paste0(figure_dir, "/", variant, "_gene_expr.pdf"), width=22, height=25)
p1 <- draw(h1 + ha_genes )
#dev.off()
saveRDS(p1, file = paste0(output_dir, "/figures/r_objects/ighv/ighv_heatmap.rds"))
#function to create stripchart plots for specific genes
gene_count <- function(gene_nam){
geneEnsID <- rownames(ddsCLL)[which(rowData(ddsCLL)$symbol %in% gene_nam)]
gc <- plotCounts(ddsCLL, gene = geneEnsID, intgroup = variant, returnData=TRUE)
p <- ggboxplot(gc, x = variant, y = "count",
color = variant,
size = 1.2,
palette = "jco",
add = "jitter",
add.params = list(size = 2.5),
outlier.shape = NA,
yscale = "log10",
title = paste(gene_nam),
font.x = 20, font.y = 20, font.legend = 20,
ylab = "normalized counts") + font("xy.text", size = 20) + font("title", size = 20, face = "bold")
saveRDS(p, file = paste0(output_dir, "/figures/r_objects/ighv/de_genes/", gene_nam, ".rds"))
p
}
diff <- diff_all[which(abs(diff_all$stat) > 10),]
geneList <- as.character(diff$Symbol)
geneList <- geneList[-which(geneList %in% "")]
geneList <- c(geneList, "CD38", "LPL", "ZAP70", "SEPT10", "ADAM29", "PEG10", "EGR3", "NFAT5", "BCAT1")
lapply(geneList, gene_count)
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Gene sets
#load gene set collection
#Hallmark
gsc <- loadGSC("/home/almut/Dokumente/masterarbeit/data/h.all.v6.0.symbols.gmt", type="gmt")
#Kegg
gsc_Kegg <- loadGSC("/home/almut/Dokumente/masterarbeit/data/c2.cp.kegg.v6.0.symbols.gmt", type="gmt")
#get all de outputs
load(paste0(output_dir,"/desRes_250720.RData"))
difftab <- function(condition){
dataTab <- data.frame(res_list[[condition]])
dataTab$ID <- rownames(dataTab)
#filter using pvalues
dataTab <- dataTab %>%
arrange(padj) %>%
mutate(Symbol = rowData(ddsCLL[ID,])$symbol)# %>%
#filter(abs(log2FoldChange) > 2)
dataTab <- dataTab[!duplicated(dataTab$Symbol),]
dataTab <- dataTab[!is.na(dataTab$Symbol),]
rownames(dataTab) <- dataTab$ID
dataTab
}
diff_res <- difftab(variant)
#clusterProfiler
diff_res <- diff_res[-which(diff_res$Symbol %in% c("", NA)),]
gene_list <- diff_res$stat %>% set_names(diff_res$Symbol)
gene_list <- sort(gene_list, decreasing = TRUE)
gene_lfc <- diff_res$log2FoldChange %>% set_names(diff_res$Symbol)
gene_lfc <- sort(gene_lfc, decreasing = TRUE)
de_gene <- diff_res %>% filter(padj < 0.01)
de_gene <- de_gene$Symbol
de_ens <- diff_res %>% filter(padj < 0.01)
de_ens <- de_ens$ID
#Get Gene IDs
gene_id <- bitr(de_ens, fromType = "ENSEMBL",
toType = c("ENTREZID", "SYMBOL"),
OrgDb = org.Hs.eg.db)
'select()' returned 1:many mapping between keys and columns
Warning in bitr(de_ens, fromType = "ENSEMBL", toType = c("ENTREZID",
"SYMBOL"), : 11.09% of input gene IDs are fail to map...
gene_list_id <- bitr(diff_res$ID, fromType = "ENSEMBL",
toType = c("ENTREZID", "SYMBOL"),
OrgDb = org.Hs.eg.db)
'select()' returned 1:many mapping between keys and columns
Warning in bitr(diff_res$ID, fromType = "ENSEMBL", toType = c("ENTREZID", :
18.07% of input gene IDs are fail to map...
names(gene_list_id) <- c("ID", "ENTREZID", "Symbol")
diff_id <- left_join(gene_list_id, diff_res)
Joining, by = c("ID", "Symbol")
gene_list_id <- diff_id$stat %>% set_names(diff_id$ENTREZID)
gene_list_id <- sort(gene_list_id, decreasing = TRUE)
gene_lfc_id <- diff_id$log2FoldChange %>% set_names(diff_id$ENTREZID)
gene_lfc_id <- sort(gene_lfc_id, decreasing = TRUE)
#convert gsc
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, human_gene_symbol)
#Hallmark
em2 <- GSEA(gene_list, TERM2GENE = m_t2g, pvalueCutoff = 0.1)
preparing geneSet collections...
GSEA analysis...
leading edge analysis...
done...
em <- enricher(de_gene, TERM2GENE = m_t2g)
#Kegg
kk <- enrichKEGG(gene_id$ENTREZID,
organism = 'hsa',
pvalueCutoff = 0.2)
kk2 <- gseKEGG(geneList = gene_list_id,
organism = 'hsa',
nPerm = 1000,
minGSSize = 50,
pvalueCutoff = 0.2,
verbose = FALSE)
kk2x <- setReadable(kk2, 'org.Hs.eg.db', 'ENTREZID')
Visualize ClusterProfiler results
barplot(kk, showCategory=5)
barplot(em, showCategory=5)
dot1 <- clusterProfiler::dotplot(em2, showCategory=10) + ggtitle("GSEA for IGHV") +
theme_pubr() +
theme(legend.position="right") +
theme(plot.title = element_text(face = "bold"))
wrong orderBy parameter; set to default `orderBy = "x"`
dot1
dotplot(em, showCategory=10) + ggtitle("Enrichment for IGHV")
wrong orderBy parameter; set to default `orderBy = "x"`
dotplot(kk2, showCategory=10) + ggtitle("GSEA for IGHV")
wrong orderBy parameter; set to default `orderBy = "x"`
dot2 <- clusterProfiler::dotplot(kk, showCategory=10) + ggtitle("Enrichment for IGHV") +
theme_pubr() +
theme(legend.position="right") +
theme(plot.title = element_text(face = "bold"))
wrong orderBy parameter; set to default `orderBy = "x"`
dot2
ridgeplot(em2)
Picking joint bandwidth of 0.691
ridgeplot(kk2)
Picking joint bandwidth of 0.573
gseaplot2(em2, geneSetID = 3, title = em2$Description[3])
gseaplot2(kk2, geneSetID = 2, title = kk2$Description[2])
saveRDS(dot1, file = paste0(output_dir, "/figures/r_objects/ighv/enrich_dot_hm.rds"))
saveRDS(dot2, file = paste0(output_dir, "/figures/r_objects/ighv/enrich_dot2.rds"))
network plot
# Networks Hallmark
em2_sub <- em2
em2_sub@result <- em2@result[which(em2@result$Description %in% c("HALLMARK_TNFA_SIGNALING_VIA_NFKB",
"HALLMARK_KRAS_SIGNALING_DN",
"HALLMARK_P53_PATHWAY",
"HALLMARK_ANGIOGENESIS")),]
p_net <- cnetplot(em2_sub, categorySize="pvalue", foldChange=gene_lfc) +
scale_colour_gradientn(colors = c("#581845", "#900C3F", "#C70039", "#FF5733", "#FFC300", "#DAF7A6")) +
guides(size = FALSE) +
labs(color = "logFC")
Scale for 'colour' is already present. Adding another scale for
'colour', which will replace the existing scale.
p_net
# Networks KEGG
kk2_sub <- kk2x
kk2_sub@result <- kk2x@result[which(kk2x@result$Description %in% c("B cell receptor signaling pathway",
"Chemokine signaling pathway",
"T cell receptor signaling pathway"
)),]
pnet_kegg <- cnetplot(kk2_sub, categorySize="pvalue", foldChange=gene_lfc) +
scale_color_gradient(high="blue", low="red") +
guides(size = FALSE) +
labs(color = "logFC")
Scale for 'colour' is already present. Adding another scale for
'colour', which will replace the existing scale.
pnet_kegg
saveRDS(pnet_kegg, file = paste0(output_dir, "/figures/r_objects/ighv/enrich_net_kegg.rds"))
saveRDS(p_net, file = paste0(output_dir, "/figures/r_objects/ighv/enrich_net_hm.rds"))
heatplot
heatplot(em2, foldChange=gene_lfc, showCategory = 3)
heatplot(kk2x, foldChange=gene_lfc, showCategory = 3)
#load object with variant status
load(paste0(data_dir, "/2018-03-05_IGHV.RData"))
#sort patIDs by ddsCll object
IG_gene <- as.tibble(IG_gene_analysis_Uppsala) %>% mutate(IGHV1_69 = ifelse(Vgene %in% "IGHV1-69", 1, 0), PatID = patientID) %>% dplyr::select(IGHV1_69, PatID)
Warning: `as.tibble()` is deprecated, use `as_tibble()` (but mind the new semantics).
This warning is displayed once per session.
colData_tib <- as.tibble(colData(ddsCLL))
colDat_integrated <- left_join(colData_tib,IG_gene)
Joining, by = "PatID"
colData(ddsCLL)$IGHV1_69 <- as.factor(colDat_integrated$IGHV1_69)
geneEnsID <- rownames(ddsCLL)[which(rowData(ddsCLL)$symbol %in% "IGHV1-69")]
geneNum <- counts(ddsCLL)[geneEnsID,]
mutPat <- as.data.frame(colData(ddsCLL)[, c("IGHV", "IGHV1_69")])
colnames(mutPat) <- c("genotype", "IGHV1_69_variant")
geneDat <- cbind(mutPat, geneNum)
colnames(geneDat) <- c("genotype", "IGHV1_69_variant","counts")
geneDat <- geneDat[!is.na(geneDat$IGHV1_69_variant),]
# geneTib <- as.tibble(geneDat) %>% mutate(PatID = rownames(geneDat)) %>%
# dplyr::filter(IGHV1_69_variant == 1) %>%
# arrange(counts)
p <- ggboxplot(geneDat, x = "IGHV1_69_variant", y = "counts",
color = "IGHV1_69_variant",
size = 1.2,
palette = c("#00AFBB", "#FC4E07"),
add = "jitter",
add.params = list(size = 2.5),
outlier.shape = NA,
yscale = "log10",
title = "IGHV1-69 gene expression",
font.x = 20, font.y = 20, font.legend = 20,
ylab = "normalized counts", xlab = "IGHV1-69 variant",
legend = "right") + font("xy.text", size = 20) + font("title", size = 20, face = "bold")
p
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Removed 13 rows containing non-finite values (stat_boxplot).
Version | Author | Date |
---|---|---|
118c367 | aluetge | 2019-11-15 |
saveRDS(p, file = paste0(output_dir, "/figures/r_objects/ighv/ighv_gene_status.rds"))
sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.7 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C
[3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] enrichplot_1.4.0 org.Hs.eg.db_3.8.2
[3] AnnotationDbi_1.46.0 msigdbr_7.0.1
[5] clusterProfiler_3.12.0 here_0.1
[7] RColorBrewer_1.1-2 ggpubr_0.2
[9] magrittr_1.5 gridExtra_2.3
[11] circlize_0.4.6 gtable_0.3.0
[13] ComplexHeatmap_2.0.0 genefilter_1.66.0
[15] reshape2_1.4.3 piano_2.0.2
[17] ggsci_2.9 forcats_0.4.0
[19] stringr_1.4.0 dplyr_0.8.1
[21] purrr_0.3.2 readr_1.3.1
[23] tidyr_0.8.3 tibble_2.1.3
[25] ggplot2_3.1.1 tidyverse_1.2.1
[27] DESeq2_1.24.0 SummarizedExperiment_1.14.0
[29] DelayedArray_0.10.0 BiocParallel_1.18.0
[31] matrixStats_0.54.0 Biobase_2.44.0
[33] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
[35] IRanges_2.18.1 S4Vectors_0.22.0
[37] BiocGenerics_0.30.0
loaded via a namespace (and not attached):
[1] shinydashboard_0.7.1 tidyselect_0.2.5 RSQLite_2.1.1
[4] htmlwidgets_1.3 munsell_0.5.0 DT_0.17
[7] withr_2.1.2 colorspace_1.4-1 GOSemSim_2.10.0
[10] knitr_1.23 rstudioapi_0.10 DOSE_3.10.2
[13] labeling_0.3 git2r_0.25.2 slam_0.1-45
[16] urltools_1.7.3 GenomeInfoDbData_1.2.1 polyclip_1.10-0
[19] bit64_0.9-7 farver_2.0.3 rprojroot_1.3-2
[22] generics_0.0.2 xfun_0.7 sets_1.0-18
[25] R6_2.4.0 clue_0.3-57 graphlayouts_0.6.0
[28] locfit_1.5-9.1 bitops_1.0-6 fgsea_1.10.0
[31] gridGraphics_0.5-0 assertthat_0.2.1 promises_1.0.1
[34] scales_1.0.0 ggraph_2.0.2 nnet_7.3-15
[37] tidygraph_1.1.2 workflowr_1.4.0 rlang_0.3.4
[40] GlobalOptions_0.1.0 splines_3.6.3 lazyeval_0.2.2
[43] acepack_1.4.1 broom_0.5.2 europepmc_0.3
[46] checkmate_1.9.3 BiocManager_1.30.4 yaml_2.2.0
[49] modelr_0.1.4 backports_1.1.4 httpuv_1.5.1
[52] qvalue_2.16.0 Hmisc_4.2-0 tools_3.6.3
[55] relations_0.6-8 ggplotify_0.0.5 gplots_3.0.1.1
[58] ggridges_0.5.2 Rcpp_1.0.1 plyr_1.8.4
[61] base64enc_0.1-3 visNetwork_2.0.7 progress_1.2.2
[64] zlibbioc_1.30.0 RCurl_1.95-4.12 prettyunits_1.0.2
[67] rpart_4.1-15 GetoptLong_0.1.7 viridis_0.5.1
[70] cowplot_0.9.4 haven_2.1.0 ggrepel_0.8.1
[73] cluster_2.1.1 fs_1.3.1 data.table_1.12.2
[76] DO.db_2.9 triebeard_0.3.0 whisker_0.3-2
[79] hms_0.4.2 shinyjs_1.0 mime_0.7
[82] evaluate_0.14 xtable_1.8-4 XML_3.98-1.20
[85] readxl_1.3.1 shape_1.4.4 compiler_3.6.3
[88] KernSmooth_2.23-15 crayon_1.3.4 htmltools_0.3.6
[91] later_0.8.0 Formula_1.2-3 geneplotter_1.62.0
[94] lubridate_1.7.4 DBI_1.0.0 tweenr_1.0.1
[97] MASS_7.3-53.1 Matrix_1.3-2 cli_1.1.0
[100] marray_1.62.0 gdata_2.18.0 igraph_1.2.4.1
[103] pkgconfig_2.0.2 rvcheck_0.1.8 foreign_0.8-76
[106] xml2_1.2.0 annotate_1.62.0 XVector_0.24.0
[109] rvest_0.3.4 digest_0.6.19 rmarkdown_1.13
[112] cellranger_1.1.0 fastmatch_1.1-0 htmlTable_1.13.1
[115] shiny_1.3.2 gtools_3.8.1 rjson_0.2.20
[118] nlme_3.1-152 jsonlite_1.6 viridisLite_0.3.0
[121] limma_3.40.2 pillar_1.4.1 lattice_0.20-38
[124] httr_1.4.0 survival_2.44-1.1 GO.db_3.8.2
[127] glue_1.3.1 UpSetR_1.4.0 png_0.1-7
[130] bit_1.1-14 ggforce_0.3.1 stringi_1.4.3
[133] blob_1.1.1 latticeExtra_0.6-28 caTools_1.17.1.2
[136] memoise_1.1.0