epistasis_methylation
almut
11 Juli 2018
Last updated: 2020-09-13
Checks: 6 1
Knit directory: transcriptome_cll/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
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Aim: Do we find epistatis pattern between trisomy12 and IGHV in methylation data?
load packages
library(tidyverse)
library(ggpubr)
library(DESeq2)
library(limma)
library(RColorBrewer)
library(ComplexHeatmap)
library(ggpubr)
library(circlize)
library(here)
#library("BloodCancerMultiOmics2017")load datasets
output_dir <- here("output")
figure_dir <- here("output/figures")
load("/home/almut/Dokumente/masterarbeit/data/patmeta.RData")
load("/home/almut/Dokumente/masterarbeit/data/mutCOM.RData")
load("/home/almut/Dokumente/masterarbeit/data/methData.RData")filter CLL patients with IGHV and trisomy12 info
patmeta$PatID <- rownames(patmeta)
patCLL <- patmeta %>% filter(Diagnosis %in% "CLL") %>% filter(!is.na(IGHV))
mutations <-data.frame(assayData(mutCOM)$binary)
mutations$PatID <- rownames(mutations)
mutations_sel <- mutations %>% filter(!is.na(trisomy12)) %>% select(trisomy12,PatID)
metaData <- inner_join(patCLL, mutations_sel)Joining, by = "PatID"meth <- assay(methData)
metaData <- metaData %>% filter(PatID %in% colnames(meth)) %>% mutate(IGHV_status=ifelse(IGHV %in% "M", 1, 0))
methCLL <- meth[, metaData$PatID]Model epistatic interaction using limma
Differentiate into 4 groups
mutnames <- c("none", "IGHV-M", "trisomy12", "both")
metaGroups <- metaData %>% mutate(mutStatus = factor(mutnames[1 + IGHV_status + 2 * trisomy12], levels = mutnames)) %>% select(mutStatus, trisomy12, IGHV_status, PatID)Identify significant methylation sites
x <- tibble(varA = factor(metaData$trisomy12),
varB = factor(metaData$IGHV_status))
fit <- eBayes(lmFit(methCLL, design = model.matrix( ~ varA + varB + varA * varB, data = x)))
interaction <- tibble(methID = rownames(fit$p.value),
p = fit$p.value[, "varA1:varB1"]) %>% mutate(pBH = p.adjust(p, method = "BH")) %>% arrange(p)
sig_interaction <- interaction %>% filter(p <= 0.01)Plot significant methylation sites
Heatmap significant meth sites: order columns by variant status (both, varA, varB, none)
#methylation data
sig_meth = methCLL[sig_interaction$methID,]
#annotate genes
sig_genes <- rowData(methData[sig_interaction$methID,])$UCSC_RefGene_Name
sig_genes <- gsub('[A-z0-9]*\\;', '', sig_genes)
sig_gen <- data.frame(genNam =sig_genes,
genID = sig_interaction$methID)
sig_genes <- sig_genes[-which(sig_genes %in% "")]
sig_gen <- as.tibble(sig_gen) %>% filter(genNam %in% sig_genes)Warning: `as.tibble()` is deprecated, use `as_tibble()` (but mind the new semantics).
This warning is displayed once per session.#order by mutation groups
mutStatus <- metaGroups %>% arrange(mutStatus)
sig_meth <- sig_meth[,mutStatus$PatID]
#colors
#colors <- colorRampPalette( rev(brewer.pal(11,"RdBu")) )(255)
colors = colorRamp2(c(0,0.1,0.5,0.9,1), c("#2166ac","#4393c3", "#f7f7f7", "#d6604d","#b2182b"))
annocol <- get_palette("uchicago", 9)
annocolor <- list(Variant = c(annocol[3], annocol[5], annocol[7], annocol[9]))
names(annocolor$Variant) <- c("none", "IGHV-M", "trisomy12", "both")
#column annotation
mutationStatus <- data.frame(mutStatus$mut)
rownames(mutationStatus) <- mutStatus$PatID
colnames(mutationStatus) <- "Variant"
ha_col = HeatmapAnnotation(df = mutationStatus, col = annocolor,
simple_anno_size = unit(0.8, "cm"),
annotation_name_gp = gpar(fontsize = 25),
annotation_legend_param = list(title_gp = gpar(fontsize = 23),
labels_gp = gpar(fontsize = 18),
grid_height = unit(1, "cm"),
grid_width = unit(0.5, "cm"),
gap = unit(0.5, "cm")))
#rowcluster
meth_dist <- dist(sig_meth)
rowcluster = hclust(meth_dist, method = "ward.D2")
#heatmap
h1 <- Heatmap(sig_meth, col = colors ,
column_title = paste0("Methylation interactions:", "IGHV", "-", "trisomy12"),
column_title_gp = gpar(fontsize = 25, fontface = "bold"),
heatmap_legend_param = list(title = "expr",
title_gp = gpar(fontsize = 23),
grid_height = unit(1, "cm"),
grid_width = unit(0.5, "cm"),
gap = unit(2, "cm"),
labels_gp = gpar(fontsize = 18)),
row_dend_width = unit(0.5, "cm"),
show_row_dend = F,
show_column_names =FALSE,
show_row_names =FALSE,
top_annotation = ha_col,
show_column_dend = FALSE,
cluster_columns = FALSE,
cluster_rows = rowcluster,
split = 3,
gap = unit(0.2,"cm"),
column_order = mutStatus$PatID)
geneIDs <- which(rownames(sig_meth) %in% sig_gen$genID)
labels <- sig_gen$genNam
ha_genes <- rowAnnotation(link = row_anno_link(at = geneIDs, labels = labels, labels_gp = gpar(fontsize = 45)), width = unit(9, "cm"))Warning: anno_link() is deprecated, please use anno_mark() instead. #svg(filename="~/git/figures_thesis/gene_expr/epistatsisTri12IGHV.svg", width=30, height=50)
#pdf(file="/home/almut/Dokumente/git/Transcriptome_CLL/paper/figures/epistasis_methylation.pdf", width=35, height=45)
draw(h1) 
#dev.off()
IGHVTri12 <- list("sig_meth" = rownames(sig_meth), "MethExp" = sig_meth, "h1"= h1)
saveRDS(h1, file = paste0(output_dir, "/figures/r_objects/epistasis/epi_methylation_heatmap.rds"))#load differential data freom epistasis_Deseq_IGHV_tri12. Rmd
load("/home/almut/Dokumente/git/Transcriptome_CLL/dataset/epistaticGenes_Deseq2_IGHTri12.RData")
resOrdered <- res[order(res$pvalue),]
resSig <- subset(resOrdered, padj < 0.1)
resTab <- as.data.frame(resSig)
#count data for annotation
load("/home/almut/Dokumente/git/Transcriptome_CLL/dataset/ddsrnaCLL_150218.RData")
resTab$symbol <- rowData(ddsCLL[rownames(resSig),])$symbol
sig_expr <- resTab$symbol
#sig_meth data
sig_genes <- gsub('[A-z0-9]*\\;', '', sig_genes)
overlap <- as.tibble(sig_expr) %>% filter(!value %in% "") %>% filter(value %in% sig_genes)
#function to create stripchart plots for specific genes
meth_count <- function(gene_nam){
meth_loc <- sig_gen$genID[which(sig_gen$genNam %in% gene_nam)]
gc <- data.frame("intensity" = sig_meth[meth_loc,],
"variant" = mutationStatus)
p <- ggboxplot(gc, x = "Variant", y = "intensity",
color = "Variant",
size = 1.2,
palette = c(annocol[3], annocol[5], annocol[7], annocol[9]),
outlier.shape = NA,
add = "jitter",
add.params = list(size = 2.5),
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/epi_meth/de_genes/", gene_nam, ".rds"))
p
}
lapply(overlap[[1]], meth_count)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 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] here_0.1 circlize_0.4.6
[3] ComplexHeatmap_2.0.0 RColorBrewer_1.1-2
[5] limma_3.40.2 DESeq2_1.24.0
[7] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
[9] BiocParallel_1.18.0 matrixStats_0.54.0
[11] Biobase_2.44.0 GenomicRanges_1.36.0
[13] GenomeInfoDb_1.20.0 IRanges_2.18.1
[15] S4Vectors_0.22.0 BiocGenerics_0.30.0
[17] ggpubr_0.2 magrittr_1.5
[19] forcats_0.4.0 stringr_1.4.0
[21] dplyr_0.8.1 purrr_0.3.2
[23] readr_1.3.1 tidyr_0.8.3
[25] tibble_2.1.3 ggplot2_3.1.1
[27] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 rjson_0.2.20 rprojroot_1.3-2
[4] htmlTable_1.13.1 XVector_0.24.0 GlobalOptions_0.1.0
[7] base64enc_0.1-3 fs_1.3.1 clue_0.3-57
[10] rstudioapi_0.10 bit64_0.9-7 AnnotationDbi_1.46.0
[13] lubridate_1.7.4 xml2_1.2.0 splines_3.6.0
[16] geneplotter_1.62.0 knitr_1.23 Formula_1.2-3
[19] jsonlite_1.6 workflowr_1.4.0 broom_0.5.2
[22] annotate_1.62.0 cluster_2.1.0 png_0.1-7
[25] compiler_3.6.0 httr_1.4.0 backports_1.1.4
[28] assertthat_0.2.1 Matrix_1.2-17 lazyeval_0.2.2
[31] cli_1.1.0 acepack_1.4.1 htmltools_0.3.6
[34] tools_3.6.0 gtable_0.3.0 glue_1.3.1
[37] GenomeInfoDbData_1.2.1 Rcpp_1.0.1 cellranger_1.1.0
[40] nlme_3.1-140 xfun_0.7 rvest_0.3.4
[43] XML_3.98-1.20 zlibbioc_1.30.0 scales_1.0.0
[46] hms_0.4.2 yaml_2.2.0 memoise_1.1.0
[49] gridExtra_2.3 rpart_4.1-15 latticeExtra_0.6-28
[52] stringi_1.4.3 RSQLite_2.1.1 genefilter_1.66.0
[55] checkmate_1.9.3 shape_1.4.4 rlang_0.3.4
[58] pkgconfig_2.0.2 bitops_1.0-6 evaluate_0.14
[61] lattice_0.20-38 labeling_0.3 htmlwidgets_1.3
[64] bit_1.1-14 tidyselect_0.2.5 ggsci_2.9
[67] plyr_1.8.4 R6_2.4.0 generics_0.0.2
[70] Hmisc_4.2-0 DBI_1.0.0 pillar_1.4.1
[73] haven_2.1.0 foreign_0.8-71 withr_2.1.2
[76] survival_2.44-1.1 RCurl_1.95-4.12 nnet_7.3-12
[79] modelr_0.1.4 crayon_1.3.4 rmarkdown_1.13
[82] GetoptLong_0.1.7 locfit_1.5-9.1 readxl_1.3.1
[85] data.table_1.12.2 blob_1.1.1 git2r_0.25.2
[88] digest_0.6.19 xtable_1.8-4 munsell_0.5.0