Rで遺伝子オントロジー解析&可視化

##参考

Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140. doi: 10.1093/bioinformatics/btp616.

McCarthy DJ, Chen Y, Smyth GK (2012). “Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.” Nucleic Acids Research, 40(10), 4288-4297. doi: 10.1093/nar/gks042.

Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010). “Gene ontology analysis for RNA-seq: accounting for selection bias.” Genome Biology, 11, R14.

##準備：edgeRで発現変動解析

###サンプルデータを使って解析

suppressPackageStartupMessages(library(edgeR))
suppressPackageStartupMessages(library(goseq))
suppressPackageStartupMessages(library(org.Hs.eg.db))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(patchwork))

### DE analysis ###

table.summary = read.table(system.file("extdata", "Li_sum.txt", package = "goseq"), 
                         sep = "\t", header = TRUE, stringsAsFactors = FALSE)

counts = table.summary[,-1]
rownames(counts) = table.summary[,1]
grp = factor(rep(c("Control", "Treated"),times = c(4, 3)))
summarized = DGEList(counts,lib.size = colSums(counts), group = grp)

disp = estimateCommonDisp(summarized)
disp$common.dispersion

tested = exactTest(disp)
topTags(tested)

###edgeRの解析結果（の一部）

	logFC	logCPM	PValue	FDR
ENSG00000127954	11.557868	6.680748	2.574972e-80	1.274766e-75
ENSG00000151503	5.398963	8.499530	1.781732e-65	4.410322e-61
ENSG00000096060	4.897600	9.446705	7.983756e-60	1.317479e-55

###発現変動遺伝子 (DEGs) をラベル

• 1 for DEGs, 0 for not DEGs

genes = as.integer(p.adjust(tested$table$PValue[tested$table$logFC != 0],
                          method = "BH") < 0.05)

genes = as.integer(p.adjust(tested$table$PValue[tested$table$logFC != 0], method = "BH") < 0.05)
names(genes) = row.names(tested$table[tested$table$logFC != 0,])
table(genes)

##goseqで遺伝子オントロジー解析

###PWF (Probability Weighting Function)を得る

### GO analysis ###
pwf = nullp(genes, "hg19", "ensGene")
head(pwf)

GO.wall = goseq(pwf, "hg19", "ensGene")
head(GO.wall)

###goseqの解析結果（の一部）

category	over_represented_pvalue	under_represented_pvalue	numDEInCat	numInCat	term	ontology
GO:0005737	9.843483e-10	1	1954	8980	cytoplasm	CC

##ggplot2で解析結果を可視化

###可視化に使うカラムを選択

• termはGO termの名前
• numInCatはそのGO termに属するすべての遺伝子の数
• numDEInCatはそのGO termに属する発現変動遺伝子の数

res <- GO.wall %>%
  dplyr::select(term, over_represented_pvalue, numInCat, numDEInCat, ontology)
head(res)

###go_plot関数を定義

• p valueのトップ10を選択

go_plot <- function(x){
  x %>%
    top_n(10, wt = -over_represented_pvalue) %>% 
    mutate(hitsPerc = numDEInCat * 100 / numInCat) %>% 
    ggplot(aes(x = -log10(over_represented_pvalue), 
               y = reorder(term, -over_represented_pvalue),
               colour = hitsPerc, 
               size = numDEInCat)) +
    geom_point() +
    guides(size = guide_legend(order = 1, reverse = TRUE), 
           colour = guide_colourbar(order = 2)) +
    labs(x = "-log10 (p value)", y = "GO term", colour = "Hits (%)", size = "Count") +
    theme_grey(base_size = 20)
}

###オントロジーごとに分けてプロット

BP <- res %>%
  filter(ontology == "BP") 
MF <- res %>%
  filter(ontology == "MF")
CC <- res %>%
  filter(ontology == "CC")

p1 <- go_plot(BP) + labs(title = "Biological Processes")
p2 <- go_plot(MF) + labs(title = "Molecular Functions")
p3 <- go_plot(CC) + labs(title = "Cellular Components")

p1 / p2 / p3

##プロット結果

• p value順に並べた
• CountはGO termに属する発現変動遺伝子の数
• HitsはGO termに属するすべての遺伝子のうちの発現変動遺伝子の割合