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15.Nucleotide_motif_association_analysis-2.R
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15.Nucleotide_motif_association_analysis-2.R
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#######################
### Author: Xun Chen, Ph.D.
### Email: [email protected] or [email protected]
### ORCID: https://orcid.org/0000-0003-0327-1888
### Date: 2023/10/17
###
#######################
library(treeio)
library(ggtree)
library(ggmsa)
library(ggplot2)
library(gtable)
library(ggrepel)
library(ape)
library(dplyr)
library(grid)
library(gridExtra)
library(ggstance)
library(splitstackshape)
library(RColorBrewer)
library(ggtreeExtra)
library(ggnewscale)
library("Biostrings")
library(randomcoloR)
library(ComplexHeatmap)
library(pheatmap)
library(corrplot)
library(ggbeeswarm)
library(FSA)
library(ggpubr)
library(ComplexUpset)
#install.packages("ggplot2movies")
library(ggplot2movies)
library(ComplexUpset)
Date = "2023_9_8"
#########################
Family="MER11_combined_F1"
color_species = c("Ancient" = "#6a3d9a","Consensus" = "#6a3d9a","hg19"="#e31a1c","macFas5"="#1f78b4","panTro4"="#33a02c")
shape_species = c("Ancient" = 18,"Consensus" = 18,"hg19"=NA,"macFas5"=NA,"panTro4"=NA)
########################################################
######################### step 1 read annotation and subfamilyinfo previously generated
Summary_Table2 = read.csv("input/Summary_Table2_2023_1_5.subfamilyInfo.csv")
Summary_Table2 =Summary_Table2[!duplicated(Summary_Table2$uniqueID_new),]
#
Summary_Table2$instanceID.renamed = ifelse(is.na(Summary_Table2$instanceID.renamed),Summary_Table2$uniqueID_new,Summary_Table2$instanceID.renamed)
#
Summary_Table2$subfamily.name.final = ifelse(Summary_Table2$Group != "Instance",NA,Summary_Table2$subfamily.name.final)
# exclude positive and negative control
Summary_Table2 = Summary_Table2[!(Summary_Table2$Family %in% c("Positive","Negative")),]
# exclude redundant frames
Summary_Table2 = Summary_Table2[Summary_Table2$Is_included == "Included_MPRA",]
# keep high quality ones
Summary_Table2 = Summary_Table2[Summary_Table2$is_kept %in% c("highQuality.iPSC/NPC","highQuality.iPSC","highQuality.NPC"),]
# MPRA activity
#Summary_Table2 = data.frame(Summary_Table2[,c("Instance_coordinate","uniqueID_new","Family","uniqueID_MPRA","Group","Frame","Species","iPSC.activity.mean","NPC.activity.mean","iPSC.alpha","NPC.alpha","logFC","is_kept")])
Summary_Table2$log2.iPSC.alpha = log2(Summary_Table2$iPSC.alpha)
Summary_Table2$iPSC.alpha.range = ifelse(Summary_Table2$iPSC.alpha >=4 & !is.na(Summary_Table2$log2.iPSC.alpha) & Summary_Table2$iPSC.alpha.Zscore.isActive == "Active","alpha>=4","None")
Summary_Table2$iPSC.alpha.range = ifelse(Summary_Table2$iPSC.alpha >=2 & Summary_Table2$iPSC.alpha < 4 &
!is.na(Summary_Table2$iPSC.alpha) & Summary_Table2$iPSC.alpha.Zscore.isActive == "Active","alpha>=2",Summary_Table2$iPSC.alpha.range)
Summary_Table2$iPSC.alpha.range = ifelse(Summary_Table2$iPSC.alpha <2 &
!is.na(Summary_Table2$iPSC.alpha) & Summary_Table2$iPSC.alpha.Zscore.isActive == "Active","alpha<2",Summary_Table2$iPSC.alpha.range)
Summary_Table2$iPSC.alpha.range = ifelse(is.na(Summary_Table2$iPSC.alpha),"Missing",Summary_Table2$iPSC.alpha.range)
color_alpha.group = c("alpha>=4"="#bd0026","alpha>=2"="#fd8d3c","alpha<2"="#fdbd3c","None"="#f0f0f0","Missing"="#ffffff")
Summary_Table2 = Summary_Table2[order(-Summary_Table2$Order.final),]
Summary_Table2$Family.frame = ifelse(grepl("MER11",Summary_Table2$Family),paste("MER11_combined_",Summary_Table2$Frame,sep=""),NA)
Summary_Table2$Family.frame = ifelse(grepl("MER34",Summary_Table2$Family),paste("MER34_combined_",Summary_Table2$Frame,sep=""),Summary_Table2$Family.frame)
Summary_Table2$Family.frame = ifelse(grepl("MER52",Summary_Table2$Family),paste("MER52_combined_",Summary_Table2$Frame,sep=""),Summary_Table2$Family.frame)
# iPSC alpha.Zscore group
Summary_Table2$iPSC.alpha.Zscore.isActive.group = ifelse(Summary_Table2$iPSC.alpha.Zscore.isActive == "None","No activity",NA)
Summary_Table2$iPSC.alpha.Zscore.isActive.group = ifelse(Summary_Table2$iPSC.alpha.Zscore.isActive != "None" &
Summary_Table2$iPSC.alpha.Zscore < 2,"<2", Summary_Table2$iPSC.alpha.Zscore.isActive.group)
Summary_Table2$iPSC.alpha.Zscore.isActive.group = ifelse(Summary_Table2$iPSC.alpha.Zscore.isActive != "None" &
Summary_Table2$iPSC.alpha.Zscore >= 2 & Summary_Table2$iPSC.alpha.Zscore < 4 ,"2-4" ,Summary_Table2$iPSC.alpha.Zscore.isActive.group)
Summary_Table2$iPSC.alpha.Zscore.isActive.group = ifelse(Summary_Table2$iPSC.alpha.Zscore.isActive != "None" &
Summary_Table2$iPSC.alpha.Zscore >= 4 & Summary_Table2$iPSC.alpha.Zscore < 6 ,"4-6", Summary_Table2$iPSC.alpha.Zscore.isActive.group)
Summary_Table2$iPSC.alpha.Zscore.isActive.group = ifelse(Summary_Table2$iPSC.alpha.Zscore.isActive != "None" &
Summary_Table2$iPSC.alpha.Zscore >= 6 & Summary_Table2$iPSC.alpha.Zscore < 8 ,"6-8", Summary_Table2$iPSC.alpha.Zscore.isActive.group)
Summary_Table2$iPSC.alpha.Zscore.isActive.group = ifelse(Summary_Table2$iPSC.alpha.Zscore.isActive != "None" &
Summary_Table2$iPSC.alpha.Zscore >= 8 , ">=8", Summary_Table2$iPSC.alpha.Zscore.isActive.group)
Summary_Table2$iPSC.alpha.Zscore.isActive.group = factor(Summary_Table2$iPSC.alpha.Zscore.isActive.group,levels=c(">=8","6-8","4-6","2-4","<2","No activity"))
iPSC.alpha.Zscore.isActive.group.color = c(">=8" = "#67001f","6-8"="#b2182b","4-6"="#d6604d","2-4"="#f4a582","<2"="#fddbc7","No activity"="#f0f0f0")
# load the TF expression in Taka's data
Motif_exp = read.csv("input/rsem_fpkmTable.csv")
Motif_exp$logFC =log2(as.numeric(as.character(Motif_exp$X72h))+0.01)-log2(as.numeric(as.character(Motif_exp$X0h))+0.01)
Motif_exp$Gene.Symbol = gsub("POU5F1_variant2","POU5F1",Motif_exp$Gene.Symbol)
Motif_exp$Gene.Symbol = gsub("FIGLA_variant1","FIGLA",Motif_exp$Gene.Symbol)
Motif_exp$Gene.Symbol = gsub("KLF14_variant1","KLF14",Motif_exp$Gene.Symbol)
Motif_exp$Gene.Symbol = gsub("ZBTB12_variant1","ZBTB12",Motif_exp$Gene.Symbol)
Motif_exp$Gene.Symbol = gsub("_variant1","",Motif_exp$Gene.Symbol)
Motif_exp[grepl("ZFP",Motif_exp$Gene.Symbol),]
Motif_exp$X0h = as.numeric(as.character(Motif_exp$X0h))
Motif_exp$X72h = as.numeric(as.character(Motif_exp$X72h))
#################### motif cluster and order
# # motif and tree order
load("input/JASPAR.tree.RData")
tree_labels = tree$labels[tree$order]
JASPAR_tree = data.frame("order" = 1:length(tree_labels),"label" = tree_labels)
JASPAR_tree$motifIDs = JASPAR_tree$label
JASPAR_tree$motifIDs = gsub("_MA"," MA",JASPAR_tree$motifIDs)
JASPAR_tree$motifIDs = gsub("_MA"," ",JASPAR_tree$motifIDs)
JASPAR_tree = cSplit(JASPAR_tree,"motifIDs",sep=" ",type.convert = as.character)
JASPAR_tree$motifIDs_uniq = JASPAR_tree$motifIDs_2
JASPAR_tree = cSplit(JASPAR_tree,"motifIDs_uniq",sep="_",type.convert = as.character)
JASPAR_tree$motifIDs_2 = gsub("_",".",JASPAR_tree$motifIDs_2)
JASPAR_tree = JASPAR_tree[order(JASPAR_tree$order),]
# motif cluster
JASPAR_tree.color <- read.csv(file="input/JASPAR.parsed_tree_cluster_1.json.csv")
JASPAR_tree.color <- as.data.frame(JASPAR_tree.color)
# combine
JASPAR_tree.final = data.frame(merge(JASPAR_tree,JASPAR_tree.color,by.x="motifIDs_2",by.y="motifID",all=T))
JASPAR_tree.final$motif.name = toupper(JASPAR_tree.final$motif.name)
JASPAR_tree.final$gene.name = JASPAR_tree.final$motif.name
JASPAR_tree.final$gene.name = gsub("-",":",JASPAR_tree.final$gene.name) ## update on 2023/2/21
JASPAR_tree.final = data.frame(cSplit(JASPAR_tree.final,"gene.name",sep=":"))
JASPAR_tree.final = merge(JASPAR_tree.final,Motif_exp[,c("Gene.Symbol","Biotype","X0h","X72h")],by.x="gene.name_1",by.y="Gene.Symbol",all.x=T)
# some did not find matched genes
JASPAR_tree.final = JASPAR_tree.final[order(JASPAR_tree.final$order),] ### order by the tree order
JASPAR_tree.final = JASPAR_tree.final[!duplicated(JASPAR_tree.final$motif.name),] ### remove redundant motifs
JASPAR_tree.final$X0h.modified = ifelse(is.na(JASPAR_tree.final$X0h),0,JASPAR_tree.final$X0h)
# updated on 2023/9/8
list_MER11.distance = read.csv(file="input/TEwide_group.list_2023_9_4.functional_group_edited.csv")
list_MER11.distance = list_MER11.distance[list_MER11.distance$subfamily2_1 %in% c("MER11A","MER11B","MER11C"),]
list_MER11.distance = list_MER11.distance[!is.na(list_MER11.distance$subfamily.species),c("functional.group","subfamily.species","branch.length.ToKeptNode","subfamily2_1","Order","functional.group")]
colnames(list_MER11.distance) = c("group","subfamily","distance","family","order.tree","FG")
list_MER11.distance$group = gsub("PG","G",list_MER11.distance$group)
list_MER11.distance$FG = gsub("PG","G",list_MER11.distance$FG)
head(list_MER11.distance)
# kept the order based on the combined tree
list_MER11.distance = list_MER11.distance[order(list_MER11.distance$order.tree),]
list_MER11.distance = list_MER11.distance[!is.na(list_MER11.distance$subfamily),]
# subfamily of macaque
# list_MER11.macFas5.distance = read.csv(file="input/macFas5_MER11A_subfamily.mafft.prank.best.fas.nonrev_dna_rerooted.csv")
list_MER11.macFas5.distance = read.csv(file="input_trees_consensus/macFas5_MER11ABC_v1.mafft.prank.best.fas.nonrev_dna.keptroot.csv")
list_MER11.macFas5.distance$FG = NA
# list_MER11.macFas5.distance[1:2,]$FG = "G2"
# list_MER11.macFas5.distance[3:9,]$FG = "G1"
# list_MER11.macFas5.distance[10:11,]$FG = "G2"
# list_MER11.macFas5.distance[12:20,]$FG = "G3"
list_MER11.macFas5.distance[1:7,]$FG = "G1"
list_MER11.macFas5.distance[8:11,]$FG = "G2"
list_MER11.macFas5.distance[12:20,]$FG = "G3"
list_MER11.macFas5.distance[21:nrow(list_MER11.macFas5.distance),]$FG = "G4"
## new order, revised on 2023 Mar. 27
list_MER11 = list_MER11.distance
######################### add the macaque subfamiliy information to the summary table
macaque.tree = read.csv("input_trees/macFas5_rmsk_TE_0bp_MER11A.family.rename_200bp_10_0.02_all_95_group_final.csv")
# load the macaque subfamily information
Summary_Table2.kept = merge(Summary_Table2,macaque.tree[,c("label","label.final.correctedName")],by.x="instanceID.renamed",by.y="label",all.x=T)
Summary_Table2.kept$label.final.correctedName = ifelse(Summary_Table2.kept$Family == "MER11A" &
Summary_Table2.kept$species == "macFas5",
Summary_Table2.kept$label.final.correctedName,
Summary_Table2.kept$subfamily.name.final)
Summary_Table2.kept$label.final.correctedName = ifelse(Summary_Table2.kept$Group != "Instance",NA,Summary_Table2.kept$label.final.correctedName)
##################################################
######################### step 2: prepare the input files for the motif and nucleotide association analysis
Summary_Table2.fimoPlot = Summary_Table2.kept[!is.na(Summary_Table2.kept$iPSC.alpha.range),] ## exclude positive and negative
# load the fimo results
Fimo.all = read.delim("input/MER11_34_52_frame_fimo.tsv_2023_1_7.gz",header=F,sep="")
# JASPAR
Fimo.all = Fimo.all[Fimo.all$V2 != "KZNF",]
colnames(Fimo.all) = c("Family.frame","DB","motif_id","motif_name","uniqueID.new","start","stop","strand","score","p_value","q_value","matched_sequence")
Fimo.all = Fimo.all[,!(colnames(Fimo.all) == "motif_id")]
Fimo.all$uniqueID.new.DB.motif_name = paste(Fimo.all$Family.frame,Fimo.all$uniqueID.new,Fimo.all$DB,Fimo.all$motif_name)
# JASPAR for now
Fimo.all.sum = data.frame(Fimo.all[Fimo.all$DB == "JASPAR2022",] %>% group_by(Family.frame,DB,uniqueID.new,motif_name) %>% dplyr::count())
#
Family = "MER11_combined_F2"
Families = c("MER11_combined_F1","MER11_combined_F2","MER34_combined_F1","MER52_combined_F1","MER52_combined_F2")
##################################################
########################we then performed the motif association analysis using plink with the default parameters
##################################################
######################## step 3: motif association plot; we then plot the p value
Kept_motifs = data.frame("Family"=NA,"Group"=NA,"Motif"=NA,"p-value"=NA)
linear.regression.output.kept = read.delim("input/MER11_34_52.glm.linear_2023_7_10",header=F,sep="")
colnames(linear.regression.output.kept) = c("Species","Family","Combination","Type","CHROM","POS","ID","REF","ALT","A1","TEST","OBS_CT","BETA","SE","T_STAT","P","ERRCODE")
linear.regression.output.kept$Combination.type = paste(linear.regression.output.kept$Combination,linear.regression.output.kept$Type)
linear.regression.output.kept$Combination.type = paste(linear.regression.output.kept$Combination,linear.regression.output.kept$Type)
linear.regression.output.kept$ID = toupper(linear.regression.output.kept$ID)
linear.regression.output.kept$BETA = ifelse(linear.regression.output.kept$REF == "B",-(linear.regression.output.kept$BETA),linear.regression.output.kept$BETA)
linear.regression.output.kept$BETA.group = ifelse(linear.regression.output.kept$BETA >0,"Positive","Negative")
linear.regression.output.kept$log10.P = -log10(linear.regression.output.kept$P)
# motif
linear.regression.output = linear.regression.output.kept[grepl("motif",linear.regression.output.kept$Type),]
linear.regression.output = merge(linear.regression.output,JASPAR_tree.final,by.x="ID",by.y="motif.name",all.x=T)
topN = 10
color_isActive = c("Active"="red","None"= "black")
color_isActive2 = c("Active"="red","None"= "white")
color_motif = c("Single"="#969696","Multiple"= "#000000")
shape_genotype = c("Positive" = 24,"Negative" = 25)
Family = "MER11_combined_F2"
kept_motifs_MER11_F2 = data.frame("Frame"=NA,"Family"=NA,"Type"=NA,"Motif"=NA,"Species"=NA,"thredhold"=NA,"is_top"=NA)
for(Family in Families[grepl("_combined_F",Families)]){
#
Summary_Table2.fimoAssic = Summary_Table2.kept[!is.na(Summary_Table2.kept$iPSC.alpha.range),] ## exclude positive and negative
Annotation_plot.group = Summary_Table2.fimoAssic[Summary_Table2.fimoAssic$Family.frame == Family &
!is.na(Summary_Table2.fimoAssic$Family.frame) &
!(Summary_Table2.fimoAssic$Family %in% "MER11D"),] ## there is no MER11D frame was designed
Annotation_plot.group$subfamily.name.final = ifelse(is.na(Annotation_plot.group$subfamily.name.final),paste(gsub("MER","",Annotation_plot.group$Family),"_N",sep=""),Annotation_plot.group$subfamily.name.final)
Annotation_plot.group = Annotation_plot.group[order(-Annotation_plot.group$iPSC.alpha.Zscore),] # order by the iPSC.alpha.Zscore
if (grepl("^MER11",Family)){
#Groupings = data.frame("Group2"=c("All","G1","G2","G3","G4"))
Groupings = data.frame("Group2"=c("All","G4"))
Groupings$Group1 = ifelse(Groupings$Group2 == "All","All",NA)
Groupings$Group1 = ifelse(grepl("^G",Groupings$Group2),"group",Groupings$Group1)
} else {
Groupings = data.frame("Group2"=c("All"))
Groupings$Group1 = ifelse(Groupings$Group2 == "All","All",NA)
}
#
rowID = 1
Species = "All"
for (Species in c("All","hg19","hg19_panTro4","Comparison")){ ## species
for(rowID in 1:nrow(Groupings)){
Group = Groupings[rowID,]$Group2
Type = "motif"
# for (Type in c("motif","variantAll.indel","variantAll")){
#for (Type in unique(linear.regression.output.kept$Type)){
if (grepl("_BB",Type)){
next
}
# 1. Annotation plot group
if (Groupings[rowID,]$Group1 == "All"){
Annotation_plot.group.sub = Annotation_plot.group
linear.regression.output.sub = linear.regression.output[linear.regression.output$Combination.type == paste(Group,Type) &
linear.regression.output$Family == Family,]
} else if (Groupings[rowID,]$Group1 == "group"){
Annotation_plot.group.sub = Annotation_plot.group[Annotation_plot.group$label.final.correctedName %in% c(list_MER11[list_MER11$FG == Group,]$subfamily,
list_MER11.macFas5.distance[list_MER11.macFas5.distance$FG == Group,]$subfamily),]
Annotation_plot.group.sub = Annotation_plot.group.sub[!is.na(Annotation_plot.group.sub$label.final.correctedName),]
linear.regression.output.sub = linear.regression.output[linear.regression.output$Combination.type == paste(paste("MER11_F",Group,sep=""),Type) &
linear.regression.output$Family == Family,]
}
# reverse order by the value
Annotation_plot.group.sub = Annotation_plot.group.sub[order(-Annotation_plot.group.sub$iPSC.alpha.Zscore),] # order by the iPSC.alpha
Annotation_plot.group.sub$uniqueID_new = factor(Annotation_plot.group.sub$uniqueID_new,levels=unique(Annotation_plot.group.sub$uniqueID_new))
# 2. linear regression output
# if (!grepl("logFC",Type)){
# linear.regression.output.sub.both = linear.regression.output[linear.regression.output$Combination.type %in% c(paste(paste("MER11_F",Group,sep=""),Type),paste(paste("MER11_F",Group,sep="")," ",Type,".logFC",sep="")) &
# linear.regression.output$Family == Family,]
# }
### select based on the species 2023/7/7
if (Species == "All") {
Annotation_plot.group.sub = Annotation_plot.group.sub[Annotation_plot.group.sub$species %in% c("hg19","panTro4","macFas5"),]
} else if (Species == "Comparison" & !(Groupings[rowID,]$Group1 %in% c("Combined","Combined2"))) {
Annotation_plot.group.sub = Annotation_plot.group.sub[Annotation_plot.group.sub$species %in% c("hg19","panTro4","macFas5"),]
} else {
Annotation_plot.group.sub = Annotation_plot.group.sub[Annotation_plot.group.sub$species %in% Species,]
}
if (Species == "Comparison"){
linear.regression.output.sub = linear.regression.output.sub[linear.regression.output.sub$Species %in% c("hg19","panTro4","macFas5"),]
linear.regression.output.sub$Combination = paste(linear.regression.output.sub$Species,linear.regression.output.sub$Combination)
linear.regression.output.sub$Combination.type = paste(linear.regression.output.sub$Species,linear.regression.output.sub$Combination.type)
# if (!grepl("logFC",Type)){
# linear.regression.output.sub.both = linear.regression.output.sub.both[linear.regression.output.sub.both$Species %in% c("hg19","panTro4","macFas5"),]
# linear.regression.output.sub.both$Combination = paste(linear.regression.output.sub.both$Species,linear.regression.output.sub.both$Combination)
# linear.regression.output.sub.both$Combination.type = paste(linear.regression.output.sub.both$Species,linear.regression.output.sub.both$Combination.type)
# }
} else {
linear.regression.output.sub = linear.regression.output.sub[linear.regression.output.sub$Species == Species,]
# if (!grepl("logFC",Type)){
# linear.regression.output.sub.both = linear.regression.output.sub.both[linear.regression.output.sub.both$Species == Species,]
# }
}
### skip
if (nrow(linear.regression.output.sub) == 0 | (Species == "Comparison" & Groupings[rowID,]$Group1 %in% c("Combined","Combined2"))){
next
}
### prepare data for the correlation between ipsc activity and log2FC
# if (!grepl("logFC",Type)){
# linear.regression.output.sub.both$uniqueID = paste(linear.regression.output.sub.both$Combination,linear.regression.output.sub.both$Family,linear.regression.output.sub.both$ID)
# linear.regression.output.sub.both1=linear.regression.output.sub.both[!grepl("logFC",linear.regression.output.sub.both$Type),]
# linear.regression.output.sub.both2=linear.regression.output.sub.both[grepl("logFC",linear.regression.output.sub.both$Type),]
# linear.regression.output.sub.both.final = merge(linear.regression.output.sub.both1,linear.regression.output.sub.both2,by="uniqueID",all=T)
# linear.regression.output.sub.both.final$Label.final = ifelse(linear.regression.output.sub.both.final$log10.P.x>=10 | linear.regression.output.sub.both.final$log10.P.y >=10,linear.regression.output.sub.both.final$ID.x,NA)
# }
linear.regression.output.sub = linear.regression.output.sub[order(linear.regression.output.sub$P),]
linear.regression.output.sub$log10.P = -log10(linear.regression.output.sub$P)
linear.regression.output.sub$is_sig_P10 = ifelse(linear.regression.output.sub$log10.P>=10,"*",NA)
linear.regression.output.sub = linear.regression.output.sub[order(-linear.regression.output.sub$log10.P),]
linear.regression.output.sub$ID = as.character(linear.regression.output.sub$ID)
#linear.regression.output.sub$ID = factor(linear.regression.output.sub$ID,levels = unique(linear.regression.output.sub$ID))
# if the motif allele is the majority
linear.regression.output.sub$BETA = ifelse(linear.regression.output.sub$REF == "B",-(linear.regression.output.sub$BETA),linear.regression.output.sub$BETA)
linear.regression.output.sub$BETA.group = ifelse(linear.regression.output.sub$BETA >0,"Positive","Negative")
# get JASPAR tree and order
JASPAR_tree.final.sub = JASPAR_tree.final[JASPAR_tree.final$motif.name %in% linear.regression.output.sub$ID,]
linear.regression.output.sub$Label = NA
linear.regression.output.sub$Label = ifelse(linear.regression.output.sub$log10.P>=5,as.character(linear.regression.output.sub$ID),linear.regression.output.sub$Label)
linear.regression.output.sub$ID = factor(linear.regression.output.sub$ID,levels = unique(JASPAR_tree.final.sub$motif.name))
# cluster and branch color
linear.regression.output.sub.color = linear.regression.output.sub[!duplicated(linear.regression.output.sub$cluster_color),]
linear.regression.output.sub$branch_color2 = linear.regression.output.sub$branch_color
## color lowly expressed TFs
linear.regression.output.sub$branch_color2 = ifelse(linear.regression.output.sub$X0h < 1,"low.exp",linear.regression.output.sub$branch_color2)
linear.regression.output.sub$branch_color = factor(linear.regression.output.sub$branch_color)
linear.regression.output.sub$branch_color2 = factor(linear.regression.output.sub$branch_color2)
linear.regression.output.sub$cluster_color = factor(linear.regression.output.sub$cluster_color)
linear.regression.output.sub$BETA.group = factor(linear.regression.output.sub$BETA.group)
# set the maximum expression and BETA color
linear.regression.output.sub$X0h = ifelse(linear.regression.output.sub$X0h>=100,100,linear.regression.output.sub$X0h) ### still set a maximum of 100
linear.regression.output.sub$X0h.modified = ifelse(linear.regression.output.sub$X0h.modified>=100,100,linear.regression.output.sub$X0h)
linear.regression.output.sub$X0h.modified = ifelse(linear.regression.output.sub$X0h.modified>1,linear.regression.output.sub$X0h.modified,NA)
linear.regression.output.sub$BETA.modified = ifelse(linear.regression.output.sub$branch_color2 == "low.exp",NA,linear.regression.output.sub$BETA)
# assign color
branch.color = levels(linear.regression.output.sub$branch_color)
names(branch.color) = levels(linear.regression.output.sub$branch_color)
branch.color2 = levels(linear.regression.output.sub$branch_color2)
names(branch.color2) = levels(linear.regression.output.sub$branch_color2)
branch.color2['low.exp'] = "white"
cluster.color = levels(linear.regression.output.sub$cluster_color)
names(cluster.color) = levels(linear.regression.output.sub$cluster_color)
# remove redundancy by branch and beta group
linear.regression.output.sub.uniq = linear.regression.output.sub[order(-linear.regression.output.sub$log10.P),]
linear.regression.output.sub.uniq = linear.regression.output.sub.uniq[!duplicated(linear.regression.output.sub.uniq[,c("branch_color","BETA.group","Combination.type")]),]
linear.regression.output.sub.uniq$ID = factor(linear.regression.output.sub.uniq$ID,levels = levels(linear.regression.output.sub.uniq$ID)[levels(linear.regression.output.sub.uniq$ID) %in% linear.regression.output.sub.uniq$ID])
# keep associated motifs log10 p value >=5
linear.regression.output.sub.top = linear.regression.output.sub[linear.regression.output.sub$log10.P>=5,]
linear.regression.output.sub.top = linear.regression.output.sub.top[order(-linear.regression.output.sub.top$log10.P),]
# exclude motifs from the same branch and the same association direction (updated 2023/7/11)
# linear.regression.output.sub.top.uniq = linear.regression.output.sub.top[!duplicated(linear.regression.output.sub.top$branch_color),]
linear.regression.output.sub.top.uniq = linear.regression.output.sub.top[!duplicated(linear.regression.output.sub.top[,c("branch_color","BETA.group")]),]
# 3. Motifs
linear.regression.output.sub.top.order1 = linear.regression.output.sub.top[linear.regression.output.sub.top$BETA>0,]
linear.regression.output.sub.top.order1 = linear.regression.output.sub.top.order1[order(-linear.regression.output.sub.top.order1$log10.P),]
linear.regression.output.sub.top.order2 = linear.regression.output.sub.top[linear.regression.output.sub.top$BETA<=0,]
linear.regression.output.sub.top.order2 = linear.regression.output.sub.top.order2[order(linear.regression.output.sub.top.order2$log10.P),]
linear.regression.output.sub.top.order = rbind(linear.regression.output.sub.top.order1,linear.regression.output.sub.top.order2)
rm(linear.regression.output.sub.top.order1,linear.regression.output.sub.top.order2)
# Fimo dataframe, focus on the JASPAR2022 database
Fimo.all.sum.sub = Fimo.all.sum[Fimo.all.sum$uniqueID.new %in% as.character(Annotation_plot.group.sub$uniqueID_new),]
Fimo.all.sum.sub = Fimo.all.sum.sub[Fimo.all.sum.sub$DB == "JASPAR2022",]
Fimo.all.sum.sub$motif_name = toupper(Fimo.all.sum.sub$motif_name)
Fimo.all.sum.sub$uniqueID2 = paste(Fimo.all.sum.sub$DB,Fimo.all.sum.sub$uniqueID.new,Fimo.all.sum.sub$motif_name)
Fimo.all.sum.sub = Fimo.all.sum.sub[order(-Fimo.all.sum.sub$n),]
Fimo.all.sum.sub = Fimo.all.sum.sub[!duplicated(Fimo.all.sum.sub$uniqueID2),]
Fimo.all.sum.sub = Fimo.all.sum.sub[Fimo.all.sum.sub$motif_name %in% as.character(linear.regression.output.sub.top$ID),]
if (nrow(Fimo.all.sum.sub) == 0) {
Fimo.all.sum.sub = Fimo.all.sum[Fimo.all.sum$motif_name %in% unique(as.character(linear.regression.output.sub[1:topN,]$ID)),]
Group2 = paste(Group,"-log10(padj) <5")
linear.regression.output.sub.top = linear.regression.output.sub[1:topN,]
} else {
Group2 = paste(Group,"-log10(padj) >=5")
Fimo.all.sum.sub$motif_name = factor(Fimo.all.sum.sub$motif_name,levels=rev(unique(linear.regression.output.sub.top.order$ID)))
}
Fimo.all.sum.sub$uniqueID.new = factor(Fimo.all.sum.sub$uniqueID.new,levels=unique(as.character(Annotation_plot.group.sub$uniqueID_new)))
Fimo.all.sum.sub$n.group = ifelse(Fimo.all.sum.sub$n >1,"Multiple","Single")
### 4. motif association plot
#for (Branch in c("top","all")){
for (Branch in c("all")){
if (Branch == "top"){
linear.regression.output.plot = linear.regression.output.sub.uniq
} else {
linear.regression.output.plot = linear.regression.output.sub
}
p3<-ggplot(linear.regression.output.plot, aes(ID,log10.P)) +
geom_point(aes(shape = BETA.group,size=BETA,fill = (X0h)))+
scale_shape_manual(values=shape_genotype)+
scale_fill_gradient2(low="#377eb8",mid = "white", high = "#e41a1c",midpoint = 1,na.value = "grey50")+
geom_hline(yintercept = c(5,10), linetype="dotted",color = "black", linewidth=.5)+
geom_text_repel(aes(label = Label,color = Combination),max.overlaps = 100) +
scale_x_discrete(drop = FALSE)+
ggtitle(paste(Family,Group))+
xlab("")+
ylab("-log10(P value)")+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
#panel.background = element_rect(fill = "#f0f0f0"),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.text.y=element_text(colour="black",size=rel(0.7)),
axis.text.x=element_blank(),
axis.title=element_text(colour="black",size=rel(1)),
legend.position="right",
legend.background = element_blank(),
legend.text=element_text(size=rel(1)))
# assign low expressed color to grey
p3_beta<-ggplot(linear.regression.output.plot, aes(ID,log10.P)) +
geom_point(aes(shape = BETA.group,fill=BETA,size = (X0h)))+
scale_shape_manual(values=shape_genotype)+
scale_size(limits = c(0,100))+
scale_fill_gradient2(low="#377eb8",mid = "white", high = "#e41a1c",midpoint = 0,na.value = "grey50")+
geom_hline(yintercept = c(5,10), linetype="dotted",color = "black", linewidth=.5)+
geom_text_repel(aes(label = Label,color = Combination),max.overlaps = 100) +
#scale_color_manual(values=color_genotype)+
scale_x_discrete(drop = FALSE)+
ggtitle(paste(Family,Group))+
xlab("")+
ylab("-log10(P value)")+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
#panel.background = element_rect(fill = "#f0f0f0"),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.text.y=element_text(colour="black",size=rel(0.7)),
axis.text.x=element_blank(),
axis.title=element_text(colour="black",size=rel(1)),
legend.position="right",
legend.background = element_blank(),
legend.text=element_text(size=rel(1)))
p3_branch<-ggplot(linear.regression.output.plot, aes(x=ID,y=1)) +
geom_tile(aes(fill = branch_color),col = NA,size=0.1) +
scale_fill_manual(values =branch.color)+
scale_x_discrete(drop = FALSE)+
xlab("x")+
ylab("brahch")+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
#axis.line = element_line(colour = "black"),
axis.line.x = element_blank(),
axis.line.y = element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_text(colour="black",size=rel(1),angle = 90,vjust=0.5, hjust=0.9),
axis.title=element_text(colour="black",size=rel(1)),
legend.position="right",
legend.background = element_blank(),
legend.text=element_text(size=rel(1)))
p3_cluster<-ggplot(linear.regression.output.plot, aes(x=ID,y=1)) +
geom_tile(aes(fill = cluster_color),col = NA,size=0.1) +
scale_fill_manual(values =cluster.color)+
scale_x_discrete(drop = FALSE)+
xlab("x")+
ylab("cluster")+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
#axis.line = element_line(colour = "black"),
axis.line.x = element_blank(),
axis.line.y = element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_text(colour="black",size=rel(1),angle = 90,vjust=0.5, hjust=0.9),
axis.title=element_text(colour="black",size=rel(1)),
legend.position="right",
legend.background = element_blank(),
legend.text=element_text(size=rel(1)))
gC <- ggplotGrob(p3)
gC_beta <- ggplotGrob(p3_beta)
gC_branch <- ggplotGrob(p3_branch)
gC_cluster <- ggplotGrob(p3_cluster)
# between species
if (Species == "Comparison" & Groupings[rowID,]$Group2 == "G4"){
linear.regression.output.plot.h = linear.regression.output.plot[linear.regression.output.plot$Species %in% c("hg19"),]
linear.regression.output.plot.h$BETA.group = as.character(linear.regression.output.plot.h$BETA.group)
linear.regression.output.plot.c = linear.regression.output.plot[linear.regression.output.plot$Species %in% c("panTro4"),]
linear.regression.output.plot.c$BETA.group = as.character(linear.regression.output.plot.c$BETA.group)
linear.regression.output.plot.m = linear.regression.output.plot[linear.regression.output.plot$Species %in% c("macFas5"),]
linear.regression.output.plot.m$BETA.group = as.character(linear.regression.output.plot.m$BETA.group)
linear.regression.output.plot.comparison = merge(linear.regression.output.plot.h[,c("ID","log10.P","BETA","BETA.group")],linear.regression.output.plot.c[,c("ID","log10.P","BETA","BETA.group")],by="ID",all=T)
linear.regression.output.plot.comparison = merge(linear.regression.output.plot.comparison,linear.regression.output.plot.m[,c("ID","log10.P","BETA","BETA.group")],by="ID",all=T)
linear.regression.output.plot.comparison$log10.P = ifelse(is.na(linear.regression.output.plot.comparison$log10.P),0,linear.regression.output.plot.comparison$log10.P)
linear.regression.output.plot.comparison$log10.P.x = ifelse(is.na(linear.regression.output.plot.comparison$log10.P.x),0,linear.regression.output.plot.comparison$log10.P.x)
linear.regression.output.plot.comparison$log10.P.y = ifelse(is.na(linear.regression.output.plot.comparison$log10.P.y),0,linear.regression.output.plot.comparison$log10.P.y)
linear.regression.output.plot.comparison$label.final = ifelse(linear.regression.output.plot.comparison$log10.P>=5 | linear.regression.output.plot.comparison$log10.P.x >=5 |
linear.regression.output.plot.comparison$log10.P.y >=5,as.character(linear.regression.output.plot.comparison$ID),NA)
linear.regression.output.plot.comparison$BETA.final.m = ifelse(linear.regression.output.plot.comparison$log10.P.x >= linear.regression.output.plot.comparison$log10.P,
linear.regression.output.plot.comparison$BETA.x,
linear.regression.output.plot.comparison$BETA)
linear.regression.output.plot.comparison$BETA.final.mg = ifelse(linear.regression.output.plot.comparison$log10.P.x >= linear.regression.output.plot.comparison$log10.P,
linear.regression.output.plot.comparison$BETA.group.x,
linear.regression.output.plot.comparison$BETA.group)
linear.regression.output.plot.comparison$BETA.final.c = ifelse(linear.regression.output.plot.comparison$log10.P.x >= linear.regression.output.plot.comparison$log10.P.y,
linear.regression.output.plot.comparison$BETA.x,
linear.regression.output.plot.comparison$BETA.y)
linear.regression.output.plot.comparison$BETA.final.cg = ifelse(linear.regression.output.plot.comparison$log10.P.x >= linear.regression.output.plot.comparison$log10.P.y,
linear.regression.output.plot.comparison$BETA.group.x,
linear.regression.output.plot.comparison$BETA.group.y)
linear.regression.output.plot.comparison = merge(linear.regression.output.plot.comparison,linear.regression.output.plot[!duplicated(linear.regression.output.plot$ID),c("ID","X0h")],by="ID",all.x=T)
pS1 <- ggplot(linear.regression.output.plot.comparison, aes(log10.P.x,log10.P.y)) +
geom_point(aes(shape = BETA.final.cg,fill=(X0h)))+
scale_shape_manual(values=shape_genotype)+
scale_size(limits = c(0,100))+
scale_fill_gradient2(low="#377eb8",mid = "white", high = "#e41a1c",na.value = "grey50")+
geom_hline(yintercept = c(5,10), linetype="dotted",color = "white", linewidth=.5)+
geom_vline(xintercept = c(5,10), linetype="dotted",color = "white", linewidth=.5)+
geom_text_repel(aes(label = label.final),max.overlaps = 100) +
ggtitle("Association between motifs and activity change")+
xlab("human (-log10 (pvalue))")+
ylab("chimpanzee (-log10 (pvalue))")+
ggtitle("human vs chimpanzee")+
theme(plot.title = element_text(hjust = 0.5, size = rel(1)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "#f0f0f0"),
#panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.text.y=element_text(colour="black",size=rel(1)),
axis.text.x=element_text(colour="black",size=rel(1)),
axis.title=element_text(colour="black",size=rel(1)),
legend.position="right",
legend.background = element_blank(),
legend.text=element_text(size=rel(1)))
pS2 <- ggplot(linear.regression.output.plot.comparison, aes(log10.P.x,log10.P)) +
geom_point(aes(shape = BETA.final.mg,fill=(X0h)))+
scale_shape_manual(values=shape_genotype)+
scale_size(limits = c(0,100))+
scale_fill_gradient2(low="#377eb8",mid = "white", high = "#e41a1c",na.value = "grey50")+
geom_hline(yintercept = c(5,10), linetype="dotted",color = "white", linewidth=.5)+
geom_vline(xintercept = c(5,10), linetype="dotted",color = "white", linewidth=.5)+
geom_text_repel(aes(label = label.final),max.overlaps = 100) +
ggtitle("Association between motifs and activity change")+
xlab("human (-log10 (pvalue))")+
ylab("macaque (-log10 (pvalue))")+
ggtitle("human vs macaque")+
theme(plot.title = element_text(hjust = 0.5, size = rel(1)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "#f0f0f0"),
#panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.text.y=element_text(colour="black",size=rel(1)),
axis.text.x=element_text(colour="black",size=rel(1)),
axis.title=element_text(colour="black",size=rel(1)),
legend.position="right",
legend.background = element_blank(),
legend.text=element_text(size=rel(1)))
gS1 <- ggplotGrob(pS1)
gS2 <- ggplotGrob(pS2)
gSC = rbind(gS1,gS2,size = "first")
pdf(paste("Figure_5C_",Family,"_",Species,"_",Group,"_",Type,"_",Date,"-species_comparison.pdf",sep=""), # create PNG for the heat map
width = 6, # 5 x 300 pixels
height = 10,
pointsize = 10) # smaller font size
grid.draw(gSC)
dev.off()
}
g2 = rbind(gC,gC_beta,gC_branch,gC_cluster, size = "first")
if (Group == "All" & Species == "All") {
Width = 24
pdf(paste("Figure_5A_S6_",Family,"_",Species,"_",Group,"_",Type,"_",Branch,"_",Date,"-motif.pdf",sep=""), # create PNG for the heat map
width = Width, # 5 x 300 pixels
height = 16,
pointsize = 10) # smaller font size
grid.draw(g2)
dev.off()
}
}
motifOrder.logP_5 = unique(as.character(linear.regression.output.sub.top$ID))
motifOrder.logP_5.uniq = motifOrder.logP_5[motifOrder.logP_5 %in% linear.regression.output.sub.top.uniq$ID]
motifOrder.logP_10 = unique(as.character(linear.regression.output.sub.top[linear.regression.output.sub.top$log10.P>=10,]$ID))
motifOrder.logP_10.uniq = motifOrder.logP_10[motifOrder.logP_10 %in% as.character(linear.regression.output.sub.top.uniq$ID)]
###########################
### 5. correlation analysis
if (Groupings[rowID,]$Group1 %in% c("group","All") & !grepl("logFC",Type) & length(unique(as.character(linear.regression.output.sub.top$ID)))>1){
Candidate_motifs = unique(as.character(linear.regression.output.sub.top$ID))
Candidate_instances = unique(as.character(Annotation_plot.group.sub$uniqueID_new))
Fimo.all.sum.sub.convert.df = data.frame(matrix(ncol=length(Candidate_motifs),nrow=length(Candidate_instances)))
colnames(Fimo.all.sum.sub.convert.df) = Candidate_motifs
rownames(Fimo.all.sum.sub.convert.df) = Candidate_instances
for (rowID.tmp in 1:nrow(Fimo.all.sum.sub.convert.df)){
for (colID.tmp in 1:ncol(Fimo.all.sum.sub.convert.df)){
Fimo.all.sum.sub.tmp = Fimo.all.sum.sub[Fimo.all.sum.sub$uniqueID.new == rownames(Fimo.all.sum.sub.convert.df)[rowID.tmp] & Fimo.all.sum.sub$motif_name == colnames(Fimo.all.sum.sub.convert.df)[colID.tmp],]
if (nrow(Fimo.all.sum.sub.tmp)>0)
Fimo.all.sum.sub.convert.df[rowID.tmp,colID.tmp] = 1
#Fimo.all.sum.sub.convert.df[rowID.tmp,colID.tmp] = Fimo.all.sum.sub.tmp$n
}
}
rm(rowID.tmp,colID.tmp)
rm(Fimo.all.sum.sub.tmp)
Fimo.all.sum.sub.convert.df[is.na(Fimo.all.sum.sub.convert.df)] <-0
Fimo.all.sum.sub2.convert.df = Fimo.all.sum.sub.convert.df[,colnames(Fimo.all.sum.sub.convert.df) %in% motifOrder.logP_10]
summary.tmp = data.frame(apply(Fimo.all.sum.sub.convert.df, 2, function(x) length(unique(x))))
colnames(summary.tmp) = "uniq.count"
summary.tmp$motif = rownames(summary.tmp)
if (nrow(summary.tmp[summary.tmp$uniq.count>1,]) == 0) {
next
}
####################### upset plot
Fimo.all.sum.sub.convert.df.upset = Fimo.all.sum.sub.convert.df
Fimo.all.sum.sub.convert.df.upset$uniqueID_new = rownames(Fimo.all.sum.sub.convert.df.upset)
Fimo.all.sum.sub.convert.df.upset = merge(Fimo.all.sum.sub.convert.df.upset,Annotation_plot.group.sub[,c("uniqueID_new","iPSC.alpha.Zscore.isActive.group","iPSC.alpha.Zscore")],by.x="uniqueID_new",all.x=T)
Fimo.all.sum.sub.convert.df.upset$iPSC.alpha.Zscore.isActive.group = factor(Fimo.all.sum.sub.convert.df.upset$iPSC.alpha.Zscore.isActive.group,levels=rev(names(iPSC.alpha.Zscore.isActive.group.color)))
Fimo.all.sum.sub.convert.df.upset$Group = Fimo.all.sum.sub.convert.df.upset$iPSC.alpha.Zscore.isActive.group
Fimo.all.sum.sub.convert.df.upset$Group2 = ifelse(Fimo.all.sum.sub.convert.df.upset$Group == "No activity","None","Active")
Fimo.all.sum.sub.convert.df.upset$Group2 = factor(Fimo.all.sum.sub.convert.df.upset$Group2,levels=c("None","Active"))
color_isActive2 = c("Active"= "red","None"="white")
# if (length(motifOrder.logP_5.uniq) > 1 & Species == "All" & Group == "G4" & Family == "MER11_combined_F2"){
# pdf(paste("Step9_3_",Family,"_",Species,"_",Group,"_",Type,"_motif_",Date,"-upset2.pdf",sep=""),
# width = 6, # 5 x 300 pixels
# height = 12,
# pointsize = 10) # smaller font size
# print(upset(
# Fimo.all.sum.sub.convert.df.upset,
# motifOrder.logP_5.uniq,min_size=10,width_ratio=0.1, sort_intersections_by=c('degree', 'cardinality'),
# base_annotations=list(
# 'Intersection size'=intersection_size(
# counts=TRUE,
# text=list(check_overlap=TRUE),
# mapping=aes(fill=Group)
# ) +
# scale_fill_manual(values=iPSC.alpha.Zscore.isActive.group.color) +
# ggtitle("log10 P >=5 active") +
# theme(plot.title = element_text(hjust = 0.5, size = rel(1)),
# panel.background = element_blank()) +
# ylab('Group')),
# set_sizes=(
# upset_set_size(
# geom=geom_bar(
# aes(fill=Group, x=group),
# width=0.8),
# position='right') +
# scale_fill_manual(values=iPSC.alpha.Zscore.isActive.group.color)
# ),
# # moves legends over the set sizes
# guides='over',
# annotations = list(
# 'Length'=list(
# aes=aes(x=intersection, y=iPSC.alpha.Zscore),
# geom=geom_boxplot(na.rm=TRUE)
# ),
# 'Rating'=list(
# aes=aes(x=intersection, y=iPSC.alpha.Zscore),
# geom=list(
# geom_jitter(aes(), na.rm=TRUE),
# geom_violin(alpha=0.5, na.rm=TRUE))
# ),
# 'MPRA Rating'=list(
# aes=aes(x=intersection, fill=Group2),
# geom=list(
# geom_bar(stat='count', position='fill', na.rm=TRUE),
# geom_text(
# aes(
# label=!!aes_percentage(relative_to='intersection'),
# group=Group2
# ),
# color="black",
# stat='count',
# position=position_fill(vjust = .5)
# ),
# scale_y_continuous(labels=scales::percent_format())
# )
# )
# )
# )
# )
# dev.off()
# }
# if (length(motifOrder.logP_10) > 1 & Species == "All" & Group == "G4" & Family == "MER11_combined_F2"){
# Width = 6
# Height = 12
# pdf(paste("Step9_3_",Family,"_",Species,"_",Group,"_",Type,"_motif_",Date,"-upset3.pdf",sep=""),
# width = Width, # 5 x 300 pixels
# height = Height,
# pointsize = 10) # smaller font size
# print(upset(
# Fimo.all.sum.sub.convert.df.upset,
# motifOrder.logP_10,min_size=10,width_ratio=0.1,sort_intersections_by=c('degree', 'cardinality'),
# base_annotations=list(
# 'Intersection size'=intersection_size(
# counts=TRUE,
# text=list(check_overlap=TRUE),
# mapping=aes(fill=Group)
# ) +
# scale_fill_manual(values=iPSC.alpha.Zscore.isActive.group.color) +
# ggtitle("log10 P >=10 all") +
# theme(plot.title = element_text(hjust = 0.5, size = rel(1)),
# panel.background = element_blank()) +
# ylab('Group')),
# set_sizes=(
# upset_set_size(
# geom=geom_bar(
# aes(fill=Group, x=group),
# width=0.8),
# position='right') +
# scale_fill_manual(values=iPSC.alpha.Zscore.isActive.group.color)
# ),
# # moves legends over the set sizes
# guides='over',
# annotations = list(
# 'Length'=list(
# aes=aes(x=intersection, y=iPSC.alpha.Zscore),
# geom=geom_boxplot(na.rm=TRUE)
# ),
# 'Rating'=list(
# aes=aes(x=intersection, y=iPSC.alpha.Zscore),
# geom=list(
# geom_jitter(aes(), na.rm=TRUE),
# geom_violin(alpha=0.5, na.rm=TRUE))
# ),
# 'MPAA Rating'=list(
# aes=aes(x=intersection, fill=Group2),
# geom=list(
# geom_bar(stat='count', position='fill', na.rm=TRUE),
# geom_text(
# aes(
# label=!!aes_percentage(relative_to='intersection'),
# group=Group2
# ),
# color="black",
# stat='count',
# position=position_fill(vjust = .5)
# ),
# scale_y_continuous(labels=scales::percent_format())
# )
# )
# )
# )
# )
# dev.off()
# if (Family == "MER11_combined_F2" & Type == "motif" & Species == "All"){
# if (Group == "MER11A") {
# Height = 24
# }
# Width = 6
# pdf(paste("Step9_3_",Family,"_",Species,"_",Group,"_",Type,"_motif_",Date,"-upset3_2.pdf",sep=""),
# width = Width, # 5 x 300 pixels
# height = Height,
# pointsize = 10) # smaller font size
# print(upset(
# Fimo.all.sum.sub.convert.df.upset,
# motifOrder.logP_10,min_size=10,width_ratio=0.1,sort_intersections_by=c('degree', 'cardinality'),
# base_annotations=list(
# 'Intersection size'=intersection_size(
# counts=TRUE,
# text=list(check_overlap=TRUE),
# mapping=aes(fill=Group)
# ) +
# scale_fill_manual(values=iPSC.alpha.Zscore.isActive.group.color) +
# ggtitle("log10 P >=10 all") +
# theme(plot.title = element_text(hjust = 0.5, size = rel(1)),
# panel.background = element_blank()) +
# ylab('Group')),
# set_sizes=(
# upset_set_size(
# geom=geom_bar(
# aes(fill=Group, x=group),
# width=0.8),
# position='right') +
# scale_fill_manual(values=iPSC.alpha.Zscore.isActive.group.color)
# ),
# # moves legends over the set sizes
# guides='over',
# annotations = list(
# 'Length'=list(
# aes=aes(x=intersection, y=iPSC.alpha.Zscore),
# geom=geom_boxplot(na.rm=TRUE)
# ),
# 'Rating'=list(
# aes=aes(x=intersection, y=iPSC.alpha.Zscore),
# geom=list(
# geom_jitter(aes(), na.rm=TRUE),
# geom_violin(alpha=0.5, na.rm=TRUE))
# ),
# 'MPRA Rating'=list(
# aes=aes(x=intersection, fill=Group2),
# geom=list(
# geom_bar(stat='count', position='fill', na.rm=TRUE),
# geom_text(
# aes(
# label=!!aes_percentage(relative_to='intersection'),
# group=Group2
# ),
# color="black",
# stat='count',
# position=position_fill(vjust = .5)
# ),
# scale_y_continuous(labels=scales::percent_format())
# )
# )
# )
# )
# )
# dev.off()
# }
if (length(motifOrder.logP_10.uniq) > 1 & Species == "All" & Group == "G4" & Family == "MER11_combined_F2"){
Width = 6
Height = 12
if (Family == "MER11_combined_F2" & Type == "motif"){
Height = 16
}
pdf(paste("Figure_5B-",Family,"_",Species,"_",Group,"_",Type,"_motif_",Date,"-upset4.pdf",sep=""),
width = Width, # 5 x 300 pixels
height = 12,
pointsize = 10) # smaller font size
print(upset(
Fimo.all.sum.sub.convert.df.upset,
motifOrder.logP_10.uniq,min_size=10,width_ratio=0.1,sort_intersections_by=c('degree', 'cardinality'),
base_annotations=list(
'Intersection size'=intersection_size(
counts=TRUE,
text=list(check_overlap=TRUE),
mapping=aes(fill=Group)
) +
scale_fill_manual(values=iPSC.alpha.Zscore.isActive.group.color) +
ggtitle("log10 P >=10 active") +
theme(plot.title = element_text(hjust = 0.5, size = rel(1)),
panel.background = element_blank()) +
ylab('Group')),
set_sizes=(
upset_set_size(
geom=geom_bar(
aes(fill=Group, x=group),
width=0.8),
position='right') +
scale_fill_manual(values=iPSC.alpha.Zscore.isActive.group.color)
),
# moves legends over the set sizes
guides='over',
annotations = list(
'Length'=list(
aes=aes(x=intersection, y=iPSC.alpha.Zscore),
geom=geom_boxplot(na.rm=TRUE)
),
'Rating'=list(
aes=aes(x=intersection, y=iPSC.alpha.Zscore),
geom=list(
geom_jitter(aes(), na.rm=TRUE),
geom_violin(alpha=0.5, na.rm=TRUE))
),
'MPRA Rating'=list(
aes=aes(x=intersection, fill=Group2),
geom=list(
geom_bar(stat='count', position='fill', na.rm=TRUE),
geom_text(
aes(
label=!!aes_percentage(relative_to='intersection'),
group=Group2
),
color="black",
stat='count',
position=position_fill(vjust = .5)
),
scale_y_continuous(labels=scales::percent_format())
)
)
)
)
)
dev.off()
}
# }
# log10 P >= 5
res.logP_5 <- cor(as.matrix(Fimo.all.sum.sub.convert.df),use = "complete.obs")
# res.logP_5.uniq <- cor(as.matrix(Fimo.all.sum.sub.convert.df[,colnames(Fimo.all.sum.sub.convert.df) %in% as.character(linear.regression.output.sub.top.uniq$ID)]),use = "complete.obs")
# kept_motifs_MER11_F2.tmp = data.frame("Frame"=rep(Family,nrow(res.logP_5)),
# "Family"=rep(Group,nrow(res.logP_5)),
# "Type"=rep(Type,nrow(res.logP_5)),
# "Motif"=c(colnames(res.logP_5)),
# "Species"=rep(Species,nrow(res.logP_5)),
# "thredhold" = "logP_5",
# "is_top"=NA)
# if (nrow(res.logP_5.uniq)>=1){
# kept_motifs_MER11_F2.tmp$is_top = ifelse(kept_motifs_MER11_F2.tmp$Motif %in% colnames(res.logP_5.uniq),"yes",kept_motifs_MER11_F2.tmp$is_top)
# }
# kept_motifs_MER11_F2 = rbind(kept_motifs_MER11_F2,kept_motifs_MER11_F2.tmp)
#
#
pdf(paste("Figure_S7-",Family,"_",Species,"_",Group,"_",Type,"_motif_",Date,"-corrplot.logP_5.pdf",sep=""),
width = 6,
height = 12,
pointsize = 10) # smaller font size
p.corr1 = corrplot(res.logP_5,
addCoef.col = 'black',
#tl.srt = 45,
#p.mat = testRes$p,
p.mat = NULL,
#number.cex = 1.5,
method = 'square', ## or 'circle'
diag = TRUE,
order = 'hclust',
sig.level = 0.005,
#insig='blank',
type = 'upper',
bg="white",
hclust.method = "complete",
cl.pos = 'b', col = COL2('RdBu'))
dev.off()
# motifOrder.logP_5 = rownames(p.corr1$corr)
# if (length(as.character(linear.regression.output.sub.top.uniq$ID))>1){
# pdf(paste("Step9_3_",Family,"_",Species,"_",Group,"_",Type,"_motif_",Date,"-corrplot.logP_5.uniq.pdf",sep=""),
# width = 12,
# height = 12,
# pointsize = 10) # smaller font size
# p.corr1.uniq = corrplot(res.logP_5.uniq,
# addCoef.col = 'black',
# #tl.srt = 45,
# #p.mat = testRes$p,
# p.mat = NULL,
# #number.cex = 1.5,
# method = 'square', ## or 'circle'
# diag = TRUE,
# order = 'hclust',
# sig.level = 0.005,
# #insig='blank',
# type = 'upper',
# bg="white",
# hclust.method = "complete",
# cl.pos = 'b', col = COL2('RdBu'))
# dev.off()
# }
# # log10 P >= 10
# if (length(motifOrder.logP_10) > 1) {
# summary.tmp = data.frame(apply(Fimo.all.sum.sub2.convert.df, 2, function(x) length(unique(x))))
# colnames(summary.tmp) = "uniq.count"
# summary.tmp$motif = rownames(summary.tmp)
# if (nrow(summary.tmp[summary.tmp$uniq.count>1,]) == 0){
# next
# }
# res.logP_10 <- cor(as.matrix(Fimo.all.sum.sub2.convert.df),use = "complete.obs")
# kept_motifs_MER11_F2.tmp = data.frame("Frame"=rep(Family,nrow(res.logP_10)),
# "Family"=rep(Group,nrow(res.logP_10)),
# "Type"=rep(Type,nrow(res.logP_10)),
# "Motif"=c(colnames(res.logP_10)),
# "Species"=rep(Species,nrow(res.logP_10)),
# "thredhold" = "logP_10",
# "is_top"=NA)
# pdf(paste("Step9_3_",Family,"_",Species,"_",Group,"_",Type,"_motif_",Date,"-corrplot.logP_10.pdf",sep=""),
# width = 12,
# height = 12,
# pointsize = 10) # smaller font size
# p.corr2 = corrplot(res.logP_10,
# addCoef.col = 'black',
# #tl.srt = 45,
# #p.mat = testRes$p,
# p.mat = NULL,
# #number.cex = 1.5,
# method = 'square', ## or 'circle'
# diag = TRUE,
# order = 'hclust',
# sig.level = 0.005,
# #insig='blank',
# type = 'upper',
# bg="white",
# hclust.method = "complete",