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yaoyan.miRNA.R
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#---
# @author Yisong zhen
# @since 2017-03-30
# @updated 2017-10-27
# @parent
# yaoyan.miRNA.sh
#
# ageing
# database
# http://senescence.info/
# I downloaded the human ageing related gene
# http://genomics.senescence.info/genes/microarray.php
# unzip and get the
#---
pkgs <- c( 'tidyverse','Rsubread','org.Mm.eg.db','edgeR',
'limma', 'DESeq2', 'genefilter',
'openxlsx','pheatmap','gridExtra','ggrepel',
'QuasR','annotate','clusterProfiler',
'GGally','RColorBrewer',
'cluster','factoextra', 'cowplot',
'Rsamtools', 'devtools', 'gplots',
'reshape2', 'stringr','magrittr',
'multiMiR', 'biomaRt',
'igraph','org.Hs.eg.db', 'miRNATarget',
'DiagrammeR','VennDiagram', 'GEOquery',
'GenomicFeatures', 'rtracklayer',
'pathview','xlsx'
)
#--
# deprecated
# install.package(pkgs)
# this will install all necessary packages
# required in this project
#---
#install.lib <- lapply(pkgs, library, character.only = TRUE)
load.lib <- lapply(pkgs, require, character.only = TRUE)
working.env <- 'window'
linux.path <- file.path('/home/zhenyisong/biodata/wanglab/wangdata/yaoyan/rawdata/miRNA_bwa')
window.path <- file.path('D:\\yisong.data')
#---
#load('yaoyan.encode.Rdata')
#load('yaoyan.Rdata')
#---
switch( working.env, linux = { setwd(linux.path);
load('yaoyan.miRNA.Rdata')},
window = { setwd(window.path);
load('yaoyan.miRNA.Rdata')} )
figures.path <- file.path('E:\\FuWai\\dead.dir\\wangli.lab\\yaoyan\\yaoyanAgingpaper\\final.figures')
#---
# quantification pipleine digram.
# this is the whole project first step for
# quantifiaction of miR expression level
# pipeline digram, which helps readers to
# understand the results and inference procedure.
#
# This is the Figure 1A.
#---
graph <-
create_graph() %>%
set_graph_name('miRNA quantification pipeline') %>%
set_global_graph_attrs('graph', 'overlap', 'true') %>%
set_global_graph_attrs('node', 'color', 'white') %>%
set_global_graph_attrs('node', 'fontname', 'Arial') %>%
add_n_nodes(5) %>%
select_nodes_by_id(c(1:5)) %>%
set_node_attrs_ws('shape', 'retangle') %>%
set_node_attrs_ws('style', 'filled') %>%
set_node_attrs_ws('color', 'white') %>%
clear_selection %>%
add_edges_w_string(
'1->2 2->3 3->4 4->5', 'black') %>%
set_node_attrs('label',c( 'Reads mapping by the BWA algorithm',
'Preprocessing and quality control',
'miR reads counting by the HT-Count',
'Validation by the public dataset',
'Differential expression analysis using the edgeR')) %>%
set_node_attrs('fontsize',10) %>%
set_edge_attrs('arrowsize', 1)
render_graph(graph)
#---
# the following code is to test
# the multiplot arrangement
# but failed to save all plots in a figure
#---
"
sampleFile <- tempfile(pattern = 'zhen3temp', tmpdir = tempdir(), fileext = '.png')
#newsampleFile <- tempfile(pattern = 'zhen4temp', tmpdir = tempdir(), fileext = '.svg')
pipeline.svg <- export_graph(graph, file_name = sampleFile, file_type = 'png')
#convertPicture(sampleFile, newsampleFile)
#pipeline.img <- readPicture(newsampleFile)
pipeline.img <- readPNG(sampleFile)
pipeline.ggplot <- rasterGrob(pipeline.img, interpolate = TRUE)
ger.pic <- qplot() + theme_minimal() + annotation_custom(ger)
all.test <- c( coremiR.8.heatmap$gtable, kegg.pathway.plot,
Go.CC.ggplot, ggnet2(miR.targets.go.CC.network))
a <- gtable_add_grob(coremiR.8.heatmap$gtable, rect, 1, 1)
grob.temp <- arrangeGrob( a, kegg.pathway.plot,
Go.CC.ggplot, ggnet2(miR.targets.go.CC.network), ncol = 2)
grid.arrange(grobs = grob.temp)
plot2by2 <- plot_grid( coremiR.8.heatmap$gtable, kegg.pathway.plot,
Go.CC.ggplot, ggnet2(miR.targets.go.CC.network),
labels=c('A', 'B', 'C', 'D'), ncol = 2)
figure3.ggplot <- plot_grid(aging.miR23.lm.figure3B, plotlist = venns.ggplot2.figure3A, labels=c('A', 'B'), nrow = 2)
grid.arrange(arrangeGrob(aging.miR23.lm.figure3B), venns.ggplot2.figure3A)
"
# Agilen image raw data normlization method
#
# library(LVSmiRNA)
# raw data source from /141219_SN7001347_0224_AHB61HADXX/
# I did not retain the 141217 dir rawdata and did not merge
# them to find the all . discard 1:36
#---
miRNA.count.files <- list.files(pattern = 'Sample.*\\.txt$')
miRNA.count.matrix <- NULL
for( filename in miRNA.count.files) {
column <- read.delim(filename, header = F, row.names = 1)
miRNA.count.matrix <- cbind(miRNA.count.matrix , column$V2 )
}
rownames(miRNA.count.matrix) <- rownames(column)
colnames(miRNA.count.matrix) <- sub('\\.txt','',miRNA.count.files)
groups <- factor(c(rep(1,3),rep(2,3), rep(1,3),rep(2,3)), levels = 1:2, labels = c('Control','Patient'))
ages <- factor(c(rep(1,6),rep(2,6)), levels = 1:2, labels = c('AF60','AF70'))
AF.miRNA.trans.data <- DGEList(counts = miRNA.count.matrix[,7:18], group = groups ) %>%
calcNormFactors(method = 'TMM') %>%
estimateCommonDisp() %>%
estimateTagwiseDisp(trend = 'movingave')
#
# depracated exactTest for simple analysis
#---
#AF.miRNA.edgeR <- exactTest(AF.miRNA.trans.data)
#AF.miRNA.edgeR.pvalue <- AF.miRNA.edgeR$table$PValue
#AF.miRNA.edgeR.p.adj <- p.adjust(AF.miRNA.edgeR.pvalue, method = 'BH')
# introduce the ages parameter to cancel the batch effect?
#
design <- model.matrix(~ 0 + groups + ages);
colnames(design) <- c('Control','Patient','Age')
contrast.matrix <- makeContrasts(Patient - Control, Age - Control,levels = design)
fit <- glmFit(AF.miRNA.trans.data, design)
model.result <- glmLRT(fit, contrast = contrast.matrix[,1])
AF.miRNA.edgeR.pvalue <- model.result$table$PValue
AF.miRNA.edgeR.p.adj <- p.adjust(AF.miRNA.edgeR.pvalue, method = 'BH')
#---
#AF.miRNA.edgeR.sig.table <- model.result$table[which(AF.miRNA.edgeR.p.adj < 0.05),]
# this step output zero gene below the threshold
# I instead not correct the p.value using BH method
#---
AF.miRNA.edgeR.sig.table <- model.result$table[which(AF.miRNA.edgeR.pvalue < 0.05),] %>%
arrange(PValue)
rownames(AF.miRNA.edgeR.sig.table) <- rownames(model.result$table)[which(AF.miRNA.edgeR.pvalue < 0.05)]
#---
# deprecated:
#
# require(XLConnect)
#setwd('C:\\Users\\Yisong\\Desktop')
#writeWorksheetToFile('AF-miNRA.xlsx', data = AF.miRNA.edgeR.sig.table,
# sheet = 'AF-exludedAge')
# thrown out error
# Error: NoSuchMethodError (Java):
# org.apache.poi.ss.usermodel.Cell.setCellType(Lorg/apache/poi/ss/usermodel/CellType;)V
#---
write.xlsx(x = AF.miRNA.edgeR.sig.table, file = 'AF-miNRA.xlsx', sheet = 'AF-exludedAge')
# now check the age variable influence on
# samples; we above model using age as the second param to isolate the AF patient, SR patient
# this is a double check.
#---
model.age.result <- glmLRT(fit, contrast = contrast.matrix[,2])
Aging.miRNA.edgeR.pvalue <- model.age.result$table$PValue
Aging.miRNA.edgeR.p.adj <- p.adjust(Aging.miRNA.edgeR.pvalue, method = 'BH')
model.age.result$table$p.adj <- Aging.miRNA.edgeR.p.adj
Aging.miRNA.edgeR.sig.table <- model.age.result$table[which(Aging.miRNA.edgeR.p.adj < 0.05),] %>%
arrange(PValue)
rownames(Aging.miRNA.edgeR.sig.table) <- rownames(model.age.result$table)[which(Aging.miRNA.edgeR.p.adj < 0.05)]
# check postive control
Aging.miRNA.edgeR.sig.table['hsa-miR-34a-5p',]
#--- AF project end
#---
# branched project
# using the current model to check the miRNA involved in
# aging process
#---
# follwoing code will generate the figure smiliar to
# NATURE paper regarding the distribution of
# aging specific miR-34a
#
sample.SR.info <- data.frame( treat = c( 'SR40','SR40','SR40','SR50','SR50', 'SR50',
'SR60','SR60','SR60','SR70','SR70', 'SR70') )
dge.tmm.miRNA.log <- miRNA.count.matrix[,c(1:9,13:15)] %>%
DESeqDataSetFromMatrix(colData = sample.SR.info, design = ~ treat) %>%
DESeq() %>%
counts(normalized = TRUE) %>% add(1) %>% log
rownames(dge.tmm.miRNA.log) <- rownames(column)
# validation or post-QC to check the miR-34a expression
age60.40.epxrs.log <- dge.tmm.miRNA.log[,c(7:9,1:3)]
aging.whole.df <- data.frame( avg = apply(age60.40.epxrs.log, 1, median),
ratio = apply(age60.40.epxrs.log[,1:3], 1, median) / apply(age60.40.epxrs.log[,4:6], 1, median))
aging.miR.set <- aging.whole.df['hsa-miR-34a-5p',]
# remove illagal letter, which can call the function in package
# library(IDPmisc)
# NaRV.omit(df)
# is much better?
#---
#
# this code is from yaoyan.R
# please re-check the code
#---
"
human.gsea.figure1B <- gseaplot(aging.1.result, 'aging.pathway')
"
aging.whole.df <- aging.whole.df[complete.cases(aging.whole.df) & !is.infinite(rowSums(aging.whole.df)),]
miR34.valid.figure1C <- ggplot(data = aging.whole.df, aes( x= avg, y = ratio)) +
geom_jitter(alpha = 0.5) +
geom_point( data = aging.miR.set, aes(x = avg , y = ratio),
color = 'red', size = 3.2, shape = 16) +
geom_text( data = aging.miR.set, aes(x = avg , y = ratio),
label = 'hsa-miR-34a', color = 'blue',
vjust = -2, hjust = 1) +
xlab('average expression counts (log)') +
ylab('ratio old(60)/young(40) (log)') +
theme_classic()
#-- figure code end
#---
# post-QC: to check the tissue source
# as this miRNA data set is from human right artium
# using heart specific marker to distinguish the
# human heart tissue and other tissues
# please refer the reference
# 1: Chistiakov DA, Orekhov AN, Bobryshev YV. Cardiac-specific miRNA in
# cardiogenesis, heart function, and cardiac pathology (with focus on myocardial
# infarction). J Mol Cell Cardiol. 2016 May;94:107-21. doi:
# 10.1016/j.yjmcc.2016.03.015. Epub 2016 Apr 4. Review. PubMed PMID: 27056419.
# I curated the heart specific miRNA from this paper.
#---
# this will generate the figure for cardiac specifc
# miRs distribution in SR (yaoyan samples)
# cardiac specific miRs were curated from
#
# PMID: 27056419
# Title:
# Cardiac-specific miRNA in cardiogenesis,
# heart function, and cardiac pathology
# (with focus on myocardial infarction)
# author:
# Chistiakov DA1, Orekhov AN2, Bobryshev YV3.
#---
heart.specific.miRNA <-c( 'miR-499$','miR-133a-5p','miR-208a',
'miR-1-3p','miR-1-5p','miR-208b','miR-133a-3p')
sample.SR.info <- data.frame( treat = c('SR40','SR40','SR40','SR50','SR50', 'SR50',
'SR60','SR60','SR60','SR70','SR70', 'SR70') )
SR.samples.exprs.miRNA.log <- miRNA.count.matrix[,c(1:9,13:15)] %>%
DESeqDataSetFromMatrix(colData = sample.SR.info, design = ~ treat) %>%
DESeq() %>% counts(normalized = TRUE) %>% add(1) %>% log %>%
apply(1,median) %>% as.data.frame()
# check the matrix manipulation is correct
# colSums(SR.samples.exprs.miRNA)
rownames(SR.samples.exprs.miRNA.log) <- rownames(column)
colnames(SR.samples.exprs.miRNA.log) <- c('SR.median.exprs.log')
SR.samples.exprs.miRNA.filter.log <- filter(SR.samples.exprs.miRNA.log, SR.median.exprs.log != 0)
rownames(SR.samples.exprs.miRNA.filter.log) <- rownames(column)[SR.samples.exprs.miRNA.log$SR.median.exprs.log != 0]
match.pattern <- paste(heart.specific.miRNA,collapse = '|')
heart.ind <- grep(match.pattern,rownames(SR.samples.exprs.miRNA.filter.log))
(rownames(SR.samples.exprs.miRNA.filter.log)[heart.ind])
heart.miRNA.exprs <- data.frame(exprs = SR.samples.exprs.miRNA.filter.log[heart.ind,1])
heart.miRNA.names <- rownames(SR.samples.exprs.miRNA.filter.log)[heart.ind]
heart.specific.jitter <- jitter(rep(1, dim(heart.miRNA.exprs)[1]), amount = 0.4)
ggplot(data = SR.samples.exprs.miRNA.filter.log, aes(x = 1, y = SR.median.exprs.log)) +
geom_point( position = 'jitter' ) +
# this label method is deprecated.
#geom_label_repel( data = heart.miRNA.exprs, mapping = aes(x = 1, y = exprs, label = heart.miRNA.names ))
geom_point( data = heart.miRNA.exprs,
aes( x = heart.specific.jitter, y = exprs),
color = 'green', size = 2.4) +
geom_text( data = heart.miRNA.exprs,
aes(x = heart.specific.jitter, y = exprs, label = heart.miRNA.names),
color = 'blue', vjust = .5, hjust = -.2)
#-- tissue specfici QC end
sample.info <- data.frame( treat = c('AF40','AF40','AF40','AF50','AF50', 'AF50',
'AF60','AF60','AF60','AF70','AF70', 'AF70') )
dds.miRNA <- DESeqDataSetFromMatrix( countData = dge.tmm.miRNA,
colData = sample.info,
design = ~ treat)
vsd.exprs.miRNA <- varianceStabilizingTransformation(dds.miRNA, blind = FALSE) %>%
assay()
colnames(vsd.exprs.miRNA) <- c('AF40-1','AF40-2','AF40-3','AF50-1','AF50-2', 'AF50-3',
'AF60-1','AF60-2','AF60-3','AF70-1','AF70-2', 'AF70-3')
aging.miRNA.sd <- apply(vsd.exprs.miRNA, 1, sd)
hist(aging.miRNA.sd)
estimator.sd <- shorth(aging.miRNA.sd)
aging.miRNA.filtered <- vsd.exprs.miRNA[aging.miRNA.sd > 0.3,]
heatmap.result <- heatmap.2( aging.miRNA.filtered , col = greenred(75), scale = 'row',
Rowv = TRUE,Colv = FALSE, density.info = 'none',
key = TRUE, trace='none', symm = F,symkey = F,symbreaks = T,
cexCol = 0.5,srtCol = 30,
distfun = function(d) as.dist(1 - cor(t(d), method = 'spearman')),
hclustfun = function(d) hclust(d, method = 'complete'),
dendrogram = 'no', labRow = NA);
#aging.dist.miRNA <- cor(t(aging.miRNA.filtered), method = 'spearman') %>% as.dist()
aging.hclust.miRNA <- hclust(d = aging.dist.miRNA, method = 'complete')
# updated since 2017-06-29
# now every figure generated depended on
# the single raw data -- matrix counts
#---
# generate the dge.tmm.miRNA.log for
# later usage. The new count matrix
# were generated from
#---
sample.SR.info <- data.frame( treat = c('SR40','SR40','SR40','SR50','SR50', 'SR50',
'SR60','SR60','SR60','SR70','SR70', 'SR70') )
dge.tmm.miRNA.log <- miRNA.count.matrix[,c(1:9,13:15)] %>%
DESeqDataSetFromMatrix(colData = sample.SR.info, design = ~ treat) %>%
DESeq() %>%
counts(normalized = TRUE) %>% add(1) %>% log
rownames(dge.tmm.miRNA.log) <- rownames(column)
# miR aging DGE this is the core code
#---
aging.group <- factor( c(rep(1:4, each = 3)),
levels = 1:4,
labels = c('SR40','SR50','SR60','SR70'))
miRNA.aging.data <- DGEList( counts = miRNA.count.matrix[,c(1:9,13:15)], group = aging.group ) %>%
calcNormFactors(method = 'TMM') %>%
estimateCommonDisp() %>%
estimateTagwiseDisp(trend = 'movingave')
design <- model.matrix(~ 0 + aging.group);
colnames(design) <- levels(aging.group)
contrast.matrix <- makeContrasts(SR60 - SR40, SR70 - SR50, levels = design)
fit <- glmFit(miRNA.aging.data, design)
model.result.1 <- glmLRT(fit, contrast = contrast.matrix[,1])
model.result.2 <- glmLRT(fit, contrast = contrast.matrix[,2])
ageing.miRNA.edgeR.pvalue <- model.result.1$table$PValue
ageing.miRNA.edgeR.sig.table.1 <- model.result.1$table[which(ageing.miRNA.edgeR.pvalue < 0.05),] %>%
arrange(PValue)
rownames(ageing.miRNA.edgeR.sig.table.1) <- rownames(model.result.1$table)[which(ageing.miRNA.edgeR.pvalue < 0.05)]
ageing.miRNA.edgeR.pvalue <- model.result.2$table$PValue
ageing.miRNA.edgeR.sig.table.2 <- model.result.2$table[which(ageing.miRNA.edgeR.pvalue < 0.05),] %>%
arrange(PValue)
rownames(ageing.miRNA.edgeR.sig.table.2) <- rownames(model.result.2$table)[which(ageing.miRNA.edgeR.pvalue < 0.05)]
aging.names <- intersect(rownames(ageing.miRNA.edgeR.sig.table.1), rownames(ageing.miRNA.edgeR.sig.table.2))
aging.names.plus <- append(aging.names,'hsa-miR-495')
aging.specific.miRNA <- dge.tmm.miRNA.log[aging.names,]
extra.miRNA <- dge.tmm.miRNA.log['hsa-miR-495-3p',] + dge.tmm.miRNA.log['hsa-miR-495-5p',]
aging.specific.miRNA.plus <- rbind(aging.specific.miRNA, extra.miRNA)
rownames(aging.specific.miRNA.plus) <- aging.names.plus
colnames(aging.specific.miRNA.plus) <- c('SR40-1','SR40-2','SR40-3','SR50-1','SR50-2', 'SR50-3',
'SR60-1','SR60-2','SR60-3','SR70-1','SR70-2', 'SR70-3')
# see guo howto draw table in ggplot
# this is the Figure S2.
# in corresponding to yao's suggestion, I
# change the miR-34a-5p to mi
#---
table.rownames <- rownames(aging.specific.miRNA.plus )
table.rownames[3] <- 'hsa-miR-34a'
figureS2 <- grid.newpage() %>%
{tableGrob( round(aging.specific.miRNA.plus, digits = 2),
rows = table.rownames)} %>%
grid.draw()
setwd(figures.path)
grid.newpage() %>%
{tableGrob( round(aging.specific.miRNA.plus, digits = 2),
rows = table.rownames)} %>%
ggsave( 'FigureS2.jpeg', plot = ., dpi = 600,
width = 270, height = 80, units = 'mm')
# deprecated, since 2017-06-29
#---
'
g <- tableGrob(rownames(ageing.miRNA.edgeR.sig.table.1), rows = NULL)
grid.newpage()
grid.draw(g)
'
#---
# heatmap of core8 aging miRNA
# core 8, miR list overlap for show
# Figure 2A need ordering
#---
draw_colnames_45 <- function (coln, gaps, ...) {
coord <- pheatmap:::find_coordinates( length(coln), gaps)
x <- coord$coord - 0.5 * coord$size
res <- textGrob( coln, x = x, y = unit(1, 'npc') - unit(3,'bigpts'),
vjust = 0.5, hjust = 1, rot = 45, gp = gpar(...))
return(res)
}
assignInNamespace(x = 'draw_colnames', value = 'draw_colnames_45',
ns = asNamespace('pheatmap'))
color.bar <- colorRampPalette(c('midnightblue', 'grey', 'mediumvioletred'))(100)
#---
# in corresponding yao's request
# I change the miR name
#---
aging.matrix <- aging.specific.miRNA.plus
rownames(aging.matrix)[3] <- 'hsa-miR-34a'
coremiR.8.figure2A <- pheatmap( aging.matrix, cluster_rows = TRUE, cluster_cols = FALSE,
clustering_distance_rows = 'correlation', color = color.bar,
scale = 'row', fontsize_col = 10, cellwidth = 23,
cellheight = 23, cuttree_rows = 2)
#--- end figure 2A
#---
# miRNA and lm() graph
#---
aging.correlation <- data.frame( miRNA.exprs = unlist(aging.specific.miRNA['hsa-miR-23a-5p',]),
age = factor(rep(1:4, each = 3), levels = 1:4,
labels = c('Age40','Age50','Age60','Age70'),
ordered = TRUE) )
#
# this code is extracted from
# http://stackoverflow.com/questions/7549694/adding-regression-line-equation-and-r2-on-graph
#---
lm.equation <- function(df){
m <- lm(miRNA.exprs ~ age, df);
eq <- substitute(italic(y) == a + b %.% italic(x)*','~~italic(r)^2~'='~r2,
list( a = format(coef(m)[1], digits = 2),
b = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
# regressin line using miR-23a
# mimic
# Figure 3?
#---
aging.miR23.lm.figure3B <- ggplot(data = aging.correlation, aes(x = as.numeric(age), y = miRNA.exprs)) +
geom_smooth(method = lm, se = TRUE) +
geom_point(aes(color = age), show.legend = F) +
xlim('age.40','age.50', 'age.60','age.70') +
xlab('human ages') +
ylab('miR-23a expression level(log), y') +
geom_text( aes(x = 3, y = 0.4),
label = lm.equation(aging.correlation),
parse = TRUE) +
theme_classic()
#
# overlapped miRNA genes AF40.60 ~ AF50.70
# prediction their mRNA target
# construct miRNA network concerned with aging process
#---
data(TBL2_HS)
miRNA.map <- dimnames(TBL2)
ref.seq.homo <- miRNA.map[[1]][miRNA.map[[2]] %in% aging.names]
data(HS_refseq_to_entrezgene)
miRNA.target.geneID <- unique(id_conv[id_conv$refseq_mrna_ncrna %in% ref.seq.homo,2])
kegg.table <- enrichKEGG( miRNA.target.geneID, organism = 'human',
pvalueCutoff = 0.05,
pAdjustMethod = 'BH',
qvalueCutoff = 0.1)
kegg.result <- summary(kegg.table)
kegg.qvalue <- -log(kegg.result$qvalue)
kegg.pathway.name <- kegg.result$Description
miR.targets.go.CC <- enrichGO( gene = miRNA.target.geneID,
OrgDb = org.Hs.eg.db,
ont = 'CC',
pAdjustMethod = 'BH',
pvalueCutoff = 0.01,
qvalueCutoff = 0.05)
summary(miR.targets.go.CC)
Go.CC.ggplot <- summary(miR.targets.go.CC) %>%
as.data.frame %>% mutate( Golabels = paste(ID, Description, sep = ' '),
LogQvalue = - log(qvalue)) %>%
ggplot(aes(x = reorder(Golabels, LogQvalue), y = LogQvalue)) +
geom_bar( stat = 'identity', width = 0.4,
position = position_dodge(width = 0.1), size = 20) +
theme(axis.text.x = element_text(angle = 60,hjust = 1, size = 9),
axis.text.y = element_text(hjust = 1, size = 9)) +
ylab('-log(qvalue)') + xlab('GO CC term enrichment by clusterProfiler') +
coord_flip()
miR.targets.go.CC.network <- enrichMap( miR.targets.go.CC, vertex.label.cex = 1.2,
layout = igraph::layout.kamada.kawai)
# deprecated miR targets method, I chose the
# new database for miR targets, multiMiR
#---
"
data(HS_refseq_to_hgnc_symbol)
# these are Core8 miR targets
miRNA.target.Symbol <- unique(id_conv[id_conv$refseq_mrna_ncrna %in% ref.seq.homo,2])
sinkplot()
miRNA.target.Symbol
sinkplot('plot', col = 'black')
miR.target.DGEOverlap.1 <- intersect(miRNA.target.Symbol,aging.result.1.names$SYMBOL)
miR.target.DGEOverlap.2 <- intersect(miRNA.target.Symbol,aging.result.2.names$SYMBOL)
miR.common.targets <- intersect(miR.target.DGEOverlap.1, miR.target.DGEOverlap.2)
miR.common.targets <- miR.target.DGEOverlap.1
aging.rpkm.mRNA[miR.common.targets,]
pheatmap( aging.rpkm.mRNA[miR.common.targets,], cluster_rows = TRUE, cluster_cols = FALSE,
clustering_distance_rows = 'correlation', color = color.bar,
scale = 'row', fontsize_col = 10, cellwidth = 23, cellheight = 23,
labels_col = c( 'SR40-1','SR40-2','SR40-3','SR50-1','SR50-2','SR50-3',
'SR60-1','SR60-2','SR60-3','SR70-2','SR70-3','SR70-1'))
"
# new miR targets code
#
#---
# these data were from yaoyan.R, please see
# you have to load the yaoyan.Rdata and
# get the aging.rpkm.mRNA
# Figure 2B
# co-8miRNA expression genes (mRNAs)
#---
aging.core8.targets <- get.multimir( mirna = rownames(aging.specific.miRNA.plus),
org = 'hsa', table = 'predicted', predicted.cutoff = 1,
predicted.cutoff.type = 'p') %$%
unique(predicted$target_symbol) %>% as.character
miR.target.40.60 <- intersect(aging.core8.targets,aging.result.1$SYMBOL) %>% na.omit
color.bar <- colorRampPalette(c('midnightblue', 'grey', 'mediumvioletred'))(100)
aging.core8.targets.figure2B <- miR.target.40.60 %>% sample(50) %>%
{rownames(aging.rpkm.mRNA) %in% . } %>%
aging.rpkm.mRNA[.,] %>%
pheatmap(cluster_rows = FALSE, cluster_cols = FALSE,
clustering_distance_rows = 'correlation', color = color.bar,
scale = 'row', fontsize_col = 4.5, fontsize_row = 5,
cellwidth = 8, cellheight = 5,
labels_col = c( 'SR40-1','SR40-2','SR40-3',
'SR50-1','SR50-2','SR50-3',
'SR60-1','SR60-2','SR60-3',
'SR70-2','SR70-3','SR70-1'))
#---
keytypes(org.Hs.eg.db)
miR.kegg.df <- mapIds( org.Hs.eg.db, keys = miR.target.40.60,
column = 'ENTREZID', keytype = 'SYMBOL',
multiVals = 'CharacterList') %>%
unlist %>% as.integer %>%
enrichKEGG( organism = 'human',
pAdjustMethod = 'none',
qvalueCutoff = 1) %>%
summary() %$%
{data.frame( kegg.pvalue = -log(pvalue),
kegg.pathway = Description )}
kegg.ggplot.figure2C <- miR.kegg.df[1:13,] %>%
ggplot( aes( x = reorder(kegg.pathway, kegg.pvalue),
y = kegg.pvalue)) +
geom_bar( stat = 'identity', width = 0.8,
position = position_dodge(width = 0.1), size = 10) +
theme( text = element_text(size = 9),
axis.text.x = element_text(angle = 60,hjust = 1, size = 9),
axis.text.y = element_text(hjust = 1, size = 9)) +
ylab('-log(pvalue)') +
xlab('KEGG pathway GSEA analysis of miRNAs targets') +
coord_flip()
miR.targets.go.CC <- mapIds( org.Hs.eg.db, keys = miR.target.40.60,
column = 'ENTREZID', keytype = 'SYMBOL',
multiVals = 'CharacterList') %>%
unlist %>% as.integer %>%
enrichGO( OrgDb = org.Hs.eg.db,
ont = 'BP',
pAdjustMethod = 'none',
pvalueCutoff = 0.05,
qvalueCutoff = 1)
summary(miR.targets.go.CC)
Go.CC.ggplot.figure2D <- summary(miR.targets.go.CC) %>%
as.data.frame %>% mutate( Golabels = paste(ID, Description, sep = ' '),
LogPvalue = - log(pvalue)) %>%
arrange(pvalue) %>% filter(pvalue < 0.005) %>%
ggplot(aes(x = reorder(Golabels, LogPvalue), y = LogPvalue)) +
geom_bar( stat = 'identity', width = 0.4,
position = position_dodge(width = 0.05), size = 4) +
theme( text = element_text(size = 9),
axis.text.x = element_text(angle = 60,hjust = 1, size = 9),
axis.text.y = element_text(hjust = 1, size = 9)) +
ylab('-log(pvalue)') + xlab('GO CC term enrichment analysis') +
coord_flip()
miR.network.figure2E <- enrichMap( miR.targets.go.CC, vertex.label.cex = 0.8,
layout = igraph::layout.kamada.kawai)
# deprecated code!
#
'
par(mar = c(12,4,1,1), fin = c(4,4))
x = barplot( kegg.qvalue, cex.lab = 0.8,cex.axis= 0.8, width = 0.5,
main = 'KEGG enrichment anlysis', cex.main = 0.8,
ylab = '-log(q-value of enrichment)')
text( cex = 0.75, x = x - 0.25, y = -1.25,
kegg.pathway.name,
xpd = TRUE, srt = 60, pos = 2)
'
# the above is the oeverall aging pathway enrichment
#---
# get the correlation between miRNA core 8 with mRNA
aging.mRNA.sd <- apply(aging.rpkm.mRNA, 1, sd)
shorth(aging.mRNA.sd)
aging.mRNA.filtered <- aging.rpkm.mRNA[aging.mRNA.sd > 0.6,]
rownames(aging.mRNA.filtered) <- rownames(aging.rpkm.mRNA)[aging.mRNA.sd > 0.6]
aging.mRNA.filtered.reCol <- aging.mRNA.filtered[,c(1:9,12,11,10)]
aging.total.data <- rbind(aging.mRNA.filtered.reCol, aging.specific.miRNA.plus)
cor.all.result <- cor(t(aging.total.data), method = 'spearman')
miRNA.cor.excerpt <- cor.all.result[,(dim(cor.all.result)[1] - 7):dim(cor.all.result)[1]]
summary(as.vector(miRNA.cor.excerpt))
hist(as.vector(miRNA.cor.excerpt), breaks = 100)
spearman.data <- data.frame(cor.data = as.vector(miRNA.cor.excerpt))
figureS3A <- ggplot(data = spearman.data, aes(x = cor.data)) +
geom_histogram( aes(y = ..density..), binwidth = .01,
colour = 'black', fill = 'white') +
geom_density(alpha = .2, fill = 'lawngreen') +
xlab('spearman correlation coefficiency') +
theme_classic() +
theme(aspect.ratio = 1)
qqPlot(spearman.data$cor.data, ylab = 'Spearman Correlation Coef')
figureS3B <- ggplot(spearman.data, aes(sample = cor.data)) +
stat_qq() +
theme_classic() +
theme(aspect.ratio = 1)
figureS3 <- plot_grid( figureS3A, figureS3B,
labels = c('A', 'B'), ncol = 2)
setwd(figures.path)
ggsave( 'FigureS3.jpeg', plot = figureS3,
width = 250, height = 180, units = 'mm',dpi = 600)
names.vector <- rownames(miRNA.cor.excerpt)
miR.list <- lapply(as.data.frame(miRNA.cor.excerpt), function(x) names.vector[abs(x) > 0.8])
miR.cor.names <- unique(unlist(miR.list))
geneid.2.table <- toTable(org.Hs.egSYMBOL)
miR.cor.geneID <- unique(geneid.2.table$gene_id[geneid.2.table$symbol %in% miR.cor.names])
miR.cor.kegg.df <- enrichKEGG( miR.cor.geneID , organism = 'human',
pAdjustMethod = 'none',
qvalueCutoff = 1) %>%
summary() %$%
{data.frame( kegg.pvalue = -log(pvalue),
kegg.pathway = Description)} %>%
filter(kegg.pvalue > 4)
#---
# in 2017-10-30
# I think this is the new Figure 2D
#---
kegg.pathway.figure2F <- miR.cor.kegg.df[1:12,] %>% ggplot( aes( x = reorder(kegg.pathway, kegg.pvalue),
y = kegg.pvalue)) +
geom_bar( stat = 'identity', width = 0.4,
position = position_dodge(width = 0.1), size = 20) +
theme(axis.text.x = element_text(angle = 60,hjust = 1, size = 8),
axis.text.y = element_text(hjust = 1, size = 10)) +
ylab('-log(pvalue)') +
xlab('KEGG pathway enrichment analysis of miR co-expressed mRNA') +
coord_flip()
# test the igraph result
rownames(aging.specific.miRNA.plus)
dim(cor.all.result)
matrix.names <- names(cor.all.result[,14089])[cor.all.result[,14089] > 0.8]
adj.matrix <- matrix(0, 220, 220)
adj.matrix[220,] <- 1
colnames(adj.matrix) <- matrix.names
rownames(adj.matrix) <- matrix.names
i.graph <- graph_from_adjacency_matrix( adj.matrix, mode = 'directed',
add.colnames = NULL,
add.rownames = NA)
#plot(i.graph, layout = layout.kamada.kawai, vertex.size = 3)
plot.igraph( i.graph, layout = layout_with_fr, vertex.size = 5,
vertex.label.cex = 0.8, edge.arrow.size = 0.2)
# pathway mao
setwd('C:\\Users\\Yisong\\Desktop')
aging.pathway.genelist <- aging.result.1$logFC
names(aging.pathway.genelist) <- aging.result.1$GeneID
aging.pathway <- pathview(gene.data = aging.pathway.genelist,
pathway.id = 'hsa05202',
species = 'hsa',
limit = list(gene = max(abs(aging.pathway.genelist)), cpd = 1))
aging.pathway.2 <- pathview(gene.data = aging.pathway.genelist,
pathway.id = 'hsa05322',
species = 'hsa',
limit = list(gene = max(abs(aging.pathway.genelist)), cpd = 1))
aging.pathway.3 <- pathview(gene.data = aging.pathway.genelist,
pathway.id = 'hsa05034',
species = 'hsa',
limit = list(gene = max(abs(aging.pathway.genelist)), cpd = 1))
#---
# QC data aging
# GSE43556
#
# Boon RA, Iekushi K, Lechner S, Seeger T et al.
# MicroRNA-34a regulates cardiac ageing and function.
# Nature 2013 Mar 7;495(7439):107-10. PMID: 23426265
# this QC data will generate the positve set for
# miRNAs correlated with aging process
# and whole aging miRNA dataset from DGE analysis
# DGE (differential gene expression)
#
#---
setwd('E:\\FuWai\\PaperPublished\\CardiacAgeing\\GSE43556')
gse <- getGEO(filename = 'GSE43556_family.soft.gz')
gsmlist <- GSMList(gse)
gpl <- GPLList(gse)
probesets <- Table(GPLList(gse)[[2]])$ID
data.matrix <- do.call('cbind',lapply(gsmlist,function(x)
{tab <- Table(x)
mymatch <- match(probesets,tab$ID_REF)
return(tab$VALUE[mymatch])
}))
data.matrix <- apply(data.matrix,2,function(x) {as.numeric(as.character(x))})
boxplot(data.matrix)
anno.df <- dataTable(gpl$GPL7732)@table
gene.ind <- match(probesets, anno.df$ID)
gene.symbol <- anno.df$'miRNA_ID'[gene.ind]
gene.symbol <- make.names(gene.symbol, unique = TRUE)
rownames(data.matrix) <- NULL
rownames(data.matrix) <- gene.symbol
aging.miRNA.pub <- na.omit(data.matrix[,-c(1:8)]) %>% log
group <- factor(rep(1:2, each = 4), levels = 1:2, labels = c('Old','Young'))
design <- model.matrix(~ 0 + group)
colnames(design) <- levels(group)
contrast.matrix <- makeContrasts( Old - Young, levels = design)
fit <- lmFit(aging.miRNA.pub, design)
fit2 <- contrasts.fit(fit,contrast.matrix)
fit2.miRNA <- eBayes(fit2)
miRNA.result <- topTable( fit2.miRNA,
number = Inf,
adjust.method = 'BH',
sort.by = 'p',
p = 0.05);
miRNA.result['mmu.miR.34a',]
# logFC AveExpr t P.Value adj.P.Val B
# mmu.miR.34a 0.3961234 6.922052 4.698403 0.001157251 0.01327598 -0.9671097
dim(miRNA.result)
hsa.miRNA.aging.matchnames <- sub('mmu\\.','hsa-', rownames(miRNA.result))
hsa.miRNA.aging.matchnames <- gsub('\\.','-',hsa.miRNA.aging.matchnames)
hsa.miRNA.aging.gsub <- gsub('(hsa-miR-\\w+)-.*','\\1',hsa.miRNA.aging.matchnames,perl = TRUE)
# next is GSEA enrichment analysis
ageing.miRNA.edgeR.1.exprs <- model.result.1$table$logFC
names(ageing.miRNA.edgeR.1.exprs) <- seq( 1:length(ageing.miRNA.edgeR.1.exprs ))
ageing.miRNA.edgeR.1.sorted.exprs <- sort(ageing.miRNA.edgeR.1.exprs, decreasing = TRUE)
aging.index.id <- names(ageing.miRNA.edgeR.1.exprs)[rownames(model.result.1$table) %in% hsa.miRNA.aging.matchnames ]
aging2gene <- data.frame( diseaseId = unlist(as.character(rep('aging.miRNA',length(aging.index.id )))),
geneId = unlist(as.integer(aging.index.id)), check.names = TRUE)
aging.GSEA <- GSEA( ageing.miRNA.edgeR.1.sorted.exprs,
TERM2GENE = aging2gene, maxGSSize = 5000, pvalueCutoff = 1)
aging.microarray.plot <- gseaplot(aging.GSEA, 'aging.miRNA')
#---
# venns's digram
# AF60 ~ AF40
# AF70 ~ AF50
# Old ~ Young Mouse from Nature miRNA data set
#
#---
homo.AF60.AF40 <- gsub('(hsa-miR-\\w+)-.*','\\1', rownames(ageing.miRNA.edgeR.sig.table.1), perl = TRUE)
homo.AF70.AF50 <- gsub('(hsa-miR-\\w+)-.*','\\1', rownames(ageing.miRNA.edgeR.sig.table.2), perl = TRUE)
mouse.aging <- hsa.miRNA.aging.gsub
area.1 <- length(homo.AF60.AF40)
area.2 <- length(homo.AF70.AF50)
area.3 <- length(mouse.aging)
n.12 <- length( intersect(homo.AF60.AF40,homo.AF70.AF50) )
n.13 <- length( intersect(homo.AF60.AF40,mouse.aging) )
n.23 <- length( intersect(homo.AF70.AF50,mouse.aging) )
n.123 <- length( intersect(intersect(homo.AF70.AF50,mouse.aging), intersect(homo.AF60.AF40, mouse.aging)) )
dev.off()
venns.ggplot2.figure3A <- grid.newpage() %>%
{draw.triple.venn( area.1, area.2, area.3, n.12, n.23, n.13, n.123,
alpha = rep(0.5, 3), fill = c('cyan','limegreen','navyblue'),
col = rep('gray97',3), lwd = rep(0,3), euler.d = T, scaled = F,
cex = c(1.5,1.5,1.5,1.5,1.5,1.5,1.5),
category = c('homo.SR60.SR40','homo.SR70.SR50','mouse.aging'),
cat.cex = 1.5, cat.dist = c(0.08,0.08,0.08)) }
#---
# please check the Nature figure 1.D
# PMID: 23426265
# QC
# this is the QC to check if the data analysis
# is corrected and evedenced by miNRA analysi by me
#
#---
exprs.df <- data.frame( avg = apply(aging.miRNA.pub, 1, median),
ratio = apply(aging.miRNA.pub[,1:4], 1, median) / apply(aging.miRNA.pub[,5:8], 1, median))
exprs.df['mmu.miR.34a',]
ggplot(data = exprs.df, aes( x= avg, y = ratio)) +
geom_point(alpha = 0.02) +
geom_point(aes(x = exprs.df['mmu.miR.34a', 1] , y = exprs.df['mmu.miR.34a',2]), color = 'red')
#----
# QC for miRNA pipeline
# RNA
# data was download from ENCODE
# please check yaoyan.miRNA.sh
# this preocessed data was save
# as yaoyan.encode.Rdata
# for tissue specific selection
#
#---
setwd('/home/zhenyisong/biodata/cardiodata/encodeHeart/humanHeart/miRNA/miRNA_bwa')
miRNA.count.files.encode <- list.files(pattern = 'ENC.*\\.txt$')
miRNA.count.matrix.encode <- NULL
column.encode <- NULL
for( filename in miRNA.count.files.encode) {
column.encode <- read.delim(filename, header = F, row.names = 1)
miRNA.count.matrix.encode <- cbind(miRNA.count.matrix.encode , column.encode$V2 )
}
rownames(miRNA.count.matrix.encode) <- rownames(column.encode)
#colnames(miRNA.count.matrix) <- sub('\\.txt','',miRNA.count.files)
miRNA.count.files.encode
colnames(miRNA.count.matrix.encode) <- c('parietal_lobe','temporal_lobe','spinal_cord',
'tongue','occipital_lobe',
'spinal_cord','diencephalon','heart',
'skin_of_body','skeletal_muscle','temporal_lobe','uterus',
'occipital_lobe','metanephros','parietal_lobe',
'lung','skeletal_muscle','skin_of_body',
'heart','cerebellum', 'frontal_cortex',
'uterus','cerebellum','frontal_cortex',
'lung','thyroid_gland','diencephalon',
'thyroid_gland','metanephros',
'tongue','urinary_bladder','liver')
data.colName <- colnames(miRNA.count.matrix.encode)[c(1:5,7:10,12,16,21,26,31,32)]
colnames(miRNA.count.matrix.encode) <- NULL
dge.tmm.miRNA.encode <- (miRNA.count.matrix.encode[,c(1:5,7:10,12,16,21,26,31,32)]) %>%
DESeqDataSetFromMatrix(colData = data.colName, design = ~ 1 ) %>%
DESeq() %>% counts(normalized = TRUE) %>% add(1) %>% log
rownames(miRNA.exprs.log.encode) <- rownames(column.encode)
miRNA.exprs.sd.encode <- apply(miRNA.exprs.log.encode, 1, sd)
hist(miRNA.exprs.sd.encode)
estimator.sd.encode <- shorth(miRNA.exprs.sd.encode)
miRNA.exprs.filtered.encode <- miRNA.exprs.log.encode[miRNA.exprs.sd.encode > 0.7,]
heatmap.result <- heatmap.2( miRNA.exprs.filtered.encode , col = greenred(75), scale = 'row',
Rowv = TRUE,Colv = FALSE, density.info = 'none',
key = TRUE, trace='none', symm = F,symkey = F,symbreaks = T,
cexCol = 0.8,srtCol = 30,
distfun = function(d) as.dist(1 - cor(t(d), method = 'spearman')),
hclustfun = function(d) hclust(d, method = 'complete'),
dendrogram = 'no',labRow = NA);
#---
# index of heart is 7
#---
tissue.max <- apply(miRNA.exprs.filtered.encode, 1, which.max)
tissue.max[tissue.max != 7] <- 0
tissue.max[tissue.max == 7] <- 1
#------------------------------------------------------------------------
# PMID: 15388519
# Bioinformatics. 2005 Mar 1;21(5):650-9. Epub 2004 Sep 23.
# Genome-wide midrange transcription profiles reveal expression
# level relationships in human tissue specification.
# see also:
# PMID: 26891983
# see also:
# https://www.biostars.org/p/209984/
#------------------------------------------------------------------------
tao.func <- function(x) {
max.val <- max(x)
size <- length(x)
norm.val <- x/max.val
norm.val <- 1 - norm.val
norm.val <- sum(norm.val)
norm.val <- norm.val/(size - 1)
return(norm.val)
}
tao.index <- apply(miRNA.exprs.filtered.encode, 1, tao.func)
tissue.score <- tao.index * tissue.max
miRNA.heart.names <- rownames(miRNA.exprs.filtered.encode)[tissue.score > 0.8]
miRNA.heart.exprs <- miRNA.exprs.filtered.encode[tissue.score > 0.8,]