IGHV
aluetge
2019-06-29
Last updated: 2021-03-22
Checks: 7 0
Knit directory: transcriptome_cll/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f3ed263 | aluetge | 2021-01-14 | code for supplementary figures |
| html | 26ae099 | aluetge | 2019-11-19 | Build site. |
| Rmd | 6cd849b | aluetge | 2019-11-19 | wflow_publish(“analysis/IGHV.Rmd”) |
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| html | 829b2f2 | aluetge | 2019-11-17 | Build site. |
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IGHV signature
Differentially expressed genes
1. Differential expression analysis
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),])$symbolExpression signature
#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"))Top genes
#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)[[1]]
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| adccf9e | aluetge | 2019-11-19 |
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Gene set enrichment analysis
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 columnsWarning 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$ID, fromType = "ENSEMBL",
toType = c("ENTREZID", "SYMBOL"),
OrgDb = org.Hs.eg.db)'select()' returned 1:many mapping between keys and columnsWarning in bitr(diff_res$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$Description %in% c("B cell receptor signaling pathway",
"Chemokine signaling pathway",
"T cell receptor signaling pathway"
)),]
pnet_kegg <- cnetplot(kk2_sub, categorySize="pvalue", foldChange=gene_lfc) +
scale_color_gradient(high="blue", low="red") +
guides(size = FALSE) +
labs(color = "logFC")Scale for 'colour' is already present. Adding another scale for
'colour', which will replace the existing scale.pnet_kegg
saveRDS(pnet_kegg, file = paste0(output_dir, "/figures/r_objects/ighv/enrich_net_kegg.rds"))
saveRDS(p_net, file = paste0(output_dir, "/figures/r_objects/ighv/enrich_net_hm.rds"))heatplot
heatplot(em2, foldChange=gene_lfc, showCategory = 3)
heatplot(kk2x, foldChange=gene_lfc, showCategory = 3)
IG gene status
#load object with variant status
load(paste0(data_dir, "/2018-03-05_IGHV.RData"))
#sort patIDs by ddsCll object
IG_gene <- as.tibble(IG_gene_analysis_Uppsala) %>% mutate(IGHV1_69 = ifelse(Vgene %in% "IGHV1-69", 1, 0), PatID = patientID) %>% dplyr::select(IGHV1_69, PatID)Warning: `as.tibble()` is deprecated, use `as_tibble()` (but mind the new semantics).
This warning is displayed once per session.colData_tib <- as.tibble(colData(ddsCLL))
colDat_integrated <- left_join(colData_tib,IG_gene)Joining, by = "PatID"colData(ddsCLL)$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")
pWarning: Transformation introduced infinite values in continuous y-axisWarning: Transformation introduced infinite values in continuous y-axisWarning: 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