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ase_BACKUP_274.R
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ase_BACKUP_274.R
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<<<<<<< HEAD
library("RUnit")
library("MBASED")
library("metap")
library("DESeq2")
library("apeglm")
library("EnhancedVolcano")
#=========================================================================================
# New UMD2a Data
#=========================================================================================
# DEG
rna_path = "C:/Users/miles/Downloads/brain/"
SRR904 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440904_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR905 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440905_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR906 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440906_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR907 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440907_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR908 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440908_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR909 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440909_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR904 = SRR904[which(! duplicated(SRR904[,1])),]
SRR905 = SRR905[which(! duplicated(SRR905[,1])),]
SRR906 = SRR906[which(! duplicated(SRR906[,1])),]
SRR907 = SRR907[which(! duplicated(SRR907[,1])),]
SRR908 = SRR908[which(! duplicated(SRR908[,1])),]
SRR909 = SRR909[which(! duplicated(SRR909[,1])),]
genes = SRR904[-c(1:5),1]
mat = as.matrix(cbind(SRR904[-c(1:5),2], SRR905[-c(1:5),2], SRR906[-c(1:5),2], SRR907[-c(1:5),2], SRR908[-c(1:5),2], SRR909[-c(1:5),2]),
dimnames=list(genes, c("4", "5", "6", "7", "8", "9")))
mycolData = data.frame(samples=c("4", "5", "6", "7", "8", "9"),
cond=c("pit", "pit", "castle", "castle", "iso", "iso"),
isBhve=c("bhve", "bhve", "bhve", "bhve", "ctrl", "ctrl"))
dds = DESeqDataSetFromMatrix(countData = mat,
colData = mycolData,
design = ~ cond)
dds <- DESeq(dds)
resultsNames(dds)
res <- results(dds, name="cond_pit_vs_castle")
res <- lfcShrink(dds, coef="cond_pit_vs_castle", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Pit v Castle Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/pit_v_castle_deg.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/pit_v_castle_deg_hgnc.txt", quote=F, col.names = F, row.names = F)
# BHVE v CTRL
dds = DESeqDataSetFromMatrix(countData = mat,
colData = mycolData,
design = ~ isBhve)
dds <- DESeq(dds)
resultsNames(dds)
res <- results(dds, name="isBhve_ctrl_vs_bhve")
res <- lfcShrink(dds, coef="isBhve_ctrl_vs_bhve", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Bhve v Ctrl Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/bhve_v_ctrl_deg.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/bhve_v_ctrl_deg_hgnc.txt", quote=F, col.names = F, row.names = F)
# Sim Pit v Castle
mat_pvc = as.matrix(cbind(SRR904[-c(1:5),2], SRR905[-c(1:5),2], SRR906[-c(1:5),2], SRR907[-c(1:5),2]),
dimnames=list(genes, c("4", "5", "6", "7")))
colData_pvc = data.frame(samples=c("4", "5", "6", "7"),
sim1=c("pit", "castle", "pit", "castle"),
sim2=c("castle", "pit", "castle", "pit"))
dds = DESeqDataSetFromMatrix(countData = mat_pvc,
colData = colData_pvc,
design = ~sim1)
dds <- DESeq(dds)
resultsNames(dds)
res <- results(dds, name="sim1_pit_vs_castle")
res <- lfcShrink(dds, coef="sim1_pit_vs_castle", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Simulated Pit v Castle 1 Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/sim1_pit_v_castle.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/sim1_pit_v_castle_hgnc.txt", quote=F, col.names = F, row.names = F)
dds <- DESeq(dds)
res <- results(dds, name="sim2_pit_vs_castle")
res <- lfcShrink(dds, coef="sim2_pit_vs_castle", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Simulated Pit v Castle 2 Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/sim2_pit_v_castle.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/sim2_pit_v_castle_hgnc.txt", quote=F, col.names = F, row.names = F)
# Pit v Iso
mat_pvi = as.matrix(cbind(SRR904[-c(1:5),2], SRR905[-c(1:5),2], SRR908[-c(1:5),2], SRR909[-c(1:5),2]),
dimnames=list(genes, c("4", "5", "8", "9")))
colData_pvi = data.frame(samples=c("4", "5", "8", "9"),
cond=c("pit", "pit", "iso", "iso"))
dds = DESeqDataSetFromMatrix(countData = mat_pvi,
colData = colData_pvi,
design = ~cond)
dds <- DESeq(dds)
res <- results(dds, name="cond_pit_vs_iso")
res <- lfcShrink(dds, coef="cond_pit_vs_iso", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Pit v Isolated Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/pit_v_iso.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/pit_v_iso_hgnc.txt", quote=F, col.names = F, row.names = F)
# Castle v Iso
mat_cvi = as.matrix(cbind(SRR906[-c(1:5),2], SRR907[-c(1:5),2], SRR908[-c(1:5),2], SRR909[-c(1:5),2]),
dimnames=list(genes, c("6", "7", "8", "9")))
colData_cvi = data.frame(samples=c("6", "7", "8", "9"),
cond=c("castle", "castle", "iso", "iso"))
dds = DESeqDataSetFromMatrix(countData = mat_cvi,
colData = colData_cvi,
design = ~cond)
dds <- DESeq(dds)
res <- results(dds, name="cond_iso_vs_castle")
res <- lfcShrink(dds, coef="cond_iso_vs_castle", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Castle v Isolated Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/castle_v_iso.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/castle_v_iso_hgnc.txt", quote=F, col.names = F, row.names = F)
# Dendrogram
mat = matrix(cbind(SRR904[-c(1:5),2], SRR905[-c(1:5),2], SRR906[-c(1:5),2], SRR907[-c(1:5),2], SRR908[-c(1:5),2], SRR909[-c(1:5),2]), ncol = 6, dimnames = list(genes, c("pit", "pit", "castle", "castle", "iso", "iso")))
pit_v_castle_genes = read.table("C:/Users/miles/Downloads/brain/results/pit_v_castle.txt", header=F)
pit_v_castle_genes = as.vector(pit_v_castle_genes$V1)
p = degDend(mat, pit_v_castle_genes, "C:/Users/miles/Downloads/brain/results/pit_v_castle_dend.png", include_samples = c("pit", "castle"))
p = degDend(mat, pit_v_castle_genes, "C:/Users/miles/Downloads/brain/results/pit_v_castle_all_dend.png")
pit_v_iso_genes = read.table("C:/Users/miles/Downloads/brain/results/pit_v_iso.txt", header=F)
pit_v_iso_genes = as.vector(pit_v_iso_genes$V1)
p = degDend(mat, pit_v_iso_genes, "C:/Users/miles/Downloads/brain/results/pit_v_iso_dend.png", include_samples = c("pit", "iso"))
castle_v_iso_genes = read.table("C:/Users/miles/Downloads/brain/results/castle_v_iso.txt", header=F)
castle_v_iso_genes = as.vector(castle_v_iso_genes$V1)
p = degDend(mat, castle_v_iso_genes, "C:/Users/miles/Downloads/brain/results/castle_v_iso_dend.png", include_samples = c("castle", "iso"))
# SNP-level data
SRR904 = read.table(paste(rna_path, "/data/ase/SRR5440904_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR905 = read.table(paste(rna_path, "/data/ase/SRR5440905_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR906 = read.table(paste(rna_path, "/data/ase/SRR5440906_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR907 = read.table(paste(rna_path, "/data/ase/SRR5440907_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR908 = read.table(paste(rna_path, "/data/ase/SRR5440908_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR909 = read.table(paste(rna_path, "/data/ase/SRR5440909_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR904$V6 = as.numeric(as.vector(SRR904$V6))
SRR905$V6 = as.numeric(as.vector(SRR905$V6))
SRR906$V6 = as.numeric(as.vector(SRR906$V6))
SRR907$V6 = as.numeric(as.vector(SRR907$V6))
SRR908$V6 = as.numeric(as.vector(SRR908$V6))
SRR909$V6 = as.numeric(as.vector(SRR909$V6))
SRR904$V7 = as.numeric(as.vector(SRR904$V7))
SRR905$V7 = as.numeric(as.vector(SRR905$V7))
SRR906$V7 = as.numeric(as.vector(SRR906$V7))
SRR907$V7 = as.numeric(as.vector(SRR907$V7))
SRR908$V7 = as.numeric(as.vector(SRR908$V7))
SRR909$V7 = as.numeric(as.vector(SRR909$V7))
SRR904$MC_COUNTS = SRR904$V6
SRR905$MC_COUNTS = SRR905$V6
SRR906$MC_COUNTS = SRR906$V6
SRR907$MC_COUNTS = SRR907$V6
SRR908$MC_COUNTS = SRR908$V6
SRR909$MC_COUNTS = SRR909$V6
SRR904$MC_COUNTS[which(SRR904$V14 == "False")] = SRR904$V7[which(SRR904$V14 == "False")]
SRR905$MC_COUNTS[which(SRR905$V14 == "False")] = SRR905$V7[which(SRR905$V14 == "False")]
SRR906$MC_COUNTS[which(SRR906$V14 == "False")] = SRR906$V7[which(SRR906$V14 == "False")]
SRR907$MC_COUNTS[which(SRR907$V14 == "False")] = SRR907$V7[which(SRR907$V14 == "False")]
SRR908$MC_COUNTS[which(SRR908$V14 == "False")] = SRR908$V7[which(SRR908$V14 == "False")]
SRR909$MC_COUNTS[which(SRR909$V14 == "False")] = SRR909$V7[which(SRR909$V14 == "False")]
SRR904$CV_COUNTS = SRR904$V7
SRR905$CV_COUNTS = SRR905$V7
SRR906$CV_COUNTS = SRR906$V7
SRR907$CV_COUNTS = SRR907$V7
SRR908$CV_COUNTS = SRR908$V7
SRR909$CV_COUNTS = SRR909$V7
SRR904$CV_COUNTS[which(SRR904$V14 == "False")] = SRR904$V6[which(SRR904$V14 == "False")]
SRR905$CV_COUNTS[which(SRR905$V14 == "False")] = SRR905$V6[which(SRR905$V14 == "False")]
SRR906$CV_COUNTS[which(SRR906$V14 == "False")] = SRR906$V6[which(SRR906$V14 == "False")]
SRR907$CV_COUNTS[which(SRR907$V14 == "False")] = SRR907$V6[which(SRR907$V14 == "False")]
SRR908$CV_COUNTS[which(SRR908$V14 == "False")] = SRR908$V6[which(SRR908$V14 == "False")]
SRR909$CV_COUNTS[which(SRR909$V14 == "False")] = SRR909$V6[which(SRR909$V14 == "False")]
SRR904$pos = paste0(SRR904$V1, ":", SRR904$V2, "-", SRR904$V2)
SRR905$pos = paste0(SRR905$V1, ":", SRR905$V2, "-", SRR905$V2)
SRR906$pos = paste0(SRR906$V1, ":", SRR906$V2, "-", SRR906$V2)
SRR907$pos = paste0(SRR907$V1, ":", SRR907$V2, "-", SRR907$V2)
SRR908$pos = paste0(SRR908$V1, ":", SRR908$V2, "-", SRR908$V2)
SRR909$pos = paste0(SRR909$V1, ":", SRR909$V2, "-", SRR909$V2)
pit = inner_join(SRR904, SRR905, by = "pos")
pit_mc = pit$MC_COUNTS.x + pit$MC_COUNTS.y
pit_cv = pit$CV_COUNTS.x + pit$CV_COUNTS.y
names(pit_mc) = pit$pos
names(pit_cv) = pit$pos
castle = inner_join(SRR906, SRR907, by = "pos")
castle_mc = castle$MC_COUNTS.x + castle$MC_COUNTS.y
castle_cv = castle$CV_COUNTS.x + castle$CV_COUNTS.y
names(castle_mc) = castle$pos
names(castle_cv) = castle$pos
iso = inner_join(SRR908, SRR909, by = "pos")
iso_mc = iso$MC_COUNTS.x + iso$MC_COUNTS.y
iso_cv = iso$CV_COUNTS.x + iso$CV_COUNTS.y
names(iso_mc) = iso$pos
names(iso_cv) = iso$pos
pit_v_castle_res = my_MBASED(pit_mc, pit_cv, castle_mc, castle_cv, "pit", "castle", pit$pos, n_boot, isSNP=T)
castle_v_pit_res = my_MBASED(castle_mc, castle_cv, pit_mc, pit_cv, "castle", "pit", pit$pos, n_boot, isSNP=T)
pit_v_castle_pos = pit_v_castle_res[[2]]
castle_v_pit_pos = castle_v_pit_res[[2]]
ovlp_pc_v_cp_pos = pit_v_castle_pos[which(pit_v_castle_pos %in% castle_v_pit_pos)]
pit_v_iso_res = my_MBASED(pit_mc, pit_cv, iso_mc, iso_cv, "pit", "iso", pit$pos, n_boot, isSNP=T)
iso_v_pit_res = my_MBASED(iso_mc, iso_cv, pit_mc, pit_cv, "iso", "pit", pit$pos, n_boot, isSNP=T)
pit_v_iso_pos = pit_v_iso_res[[2]]
iso_v_pit_pos = iso_v_pit_res[[2]]
ovlp_pi_v_ip_pos = pit_v_iso_pos[which(pit_v_iso_pos %in% iso_v_pit_pos)]
castle_v_iso_res = my_MBASED(castle_mc, castle_cv, iso_mc, iso_cv, "castle", "iso", castle$pos, n_boot, isSNP=T)
iso_v_castle_res = my_MBASED(iso_mc, iso_cv, castle_mc, castle_cv, "iso", "csatle", castle$pos, n_boot, isSNP=T)
castle_v_iso_pos = castle_v_iso_res[[2]]
iso_v_castle_pos = iso_v_castle_res[[2]]
ovlp_ci_v_ic_pos = castle_v_iso_pos[which(castle_v_iso_pos %in% iso_v_castle_pos)]
gtf = read.table("C:/Users/miles/Downloads/brain/brain_scripts/full_ens_w_ncbi_gene.gtf", sep="\t", header=F, stringsAsFactors = F)
gtf = gtf[which(gtf[,3] == "gene" & gtf[,1] != "NC_027944.1"),]
gtf_gene_name <- c()
for (i in 1:nrow(gtf)) {
start <- gregexpr(pattern ='gene_name', gtf$V9[i])[[1]]
stop <- gregexpr(pattern =';', substr(gtf$V9[i], start, nchar(gtf$V9[i])))[[1]][1]
gene_name <- substr(gtf$V9[i], start+10, start+stop-2)
if (start == -1) {
gene_name <- substr(gtf$V9[i], start+10, start+stop)
}
gtf_gene_name <- c(gtf_gene_name, gene_name)
}
gtf$gene_name <- gtf_gene_name
colnames(gtf) <- c("LG", "source", "type", "start", "stop", "idk", "idk1", "idk2", "info", "gene_name")
gtf = gtf[which(! startsWith(gtf$gene_name, "LOC")),]
pit_v_castle_genes = posToGene(pit_v_castle_pos, gtf)
###################
# Gene Level Data #
###################
rna_path = "C:/Users/miles/Downloads/brain/"
SRR904 = read.table(paste(rna_path, "/data/ase/SRR5440904_RG_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
SRR905 = read.table(paste(rna_path, "/data/ase/SRR5440905_RG_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
SRR906 = read.table(paste(rna_path, "/data/ase/SRR5440906_RG_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
SRR907 = read.table(paste(rna_path, "/data/ase/SRR5440907_RG_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
SRR908 = read.table(paste(rna_path, "/data/ase/SRR5440908_RG_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
SRR909 = read.table(paste(rna_path, "/data/ase/SRR5440909_RG_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
SRR904$GENE = str_replace(SRR904$GENE,"%", " (1 of many)")
SRR905$GENE = str_replace(SRR905$GENE,"%", " (1 of many)")
SRR906$GENE = str_replace(SRR906$GENE,"%", " (1 of many)")
SRR907$GENE = str_replace(SRR907$GENE,"%", " (1 of many)")
SRR908$GENE = str_replace(SRR908$GENE,"%", " (1 of many)")
SRR909$GENE = str_replace(SRR909$GENE,"%", " (1 of many)")
#NCBI
# SRR904 = read.table(paste(rna_path, "/data/ase/SRR5440904_RG_nc_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
# SRR905 = read.table(paste(rna_path, "/data/ase/SRR5440905_RG_nc_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
# SRR906 = read.table(paste(rna_path, "/data/ase/SRR5440906_RG_nc_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
# SRR907 = read.table(paste(rna_path, "/data/ase/SRR5440907_RG_nc_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
# SRR908 = read.table(paste(rna_path, "/data/ase/SRR5440908_RG_nc_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
# SRR909 = read.table(paste(rna_path, "/data/ase/SRR5440909_RG_nc_counts.tsv", sep=""), header = TRUE, stringsAsFactors = F)
gene_names = SRR904$GENE
# gene_names = str_replace(gene_names,"%", " (1 of many)")
n_boot = 100
# Prepare the Data
pit_mc = SRR904$MC_COUNTS + SRR905$MC_COUNTS
pit_cv = SRR904$CV_COUNTS + SRR905$CV_COUNTS
names(pit_mc) = gene_names
names(pit_cv) = gene_names
castle_mc = SRR906$MC_COUNTS + SRR907$MC_COUNTS
castle_cv = SRR906$CV_COUNTS + SRR907$CV_COUNTS
names(castle_mc) = gene_names
names(castle_cv) = gene_names
iso_mc = SRR908$MC_COUNTS + SRR909$MC_COUNTS
iso_cv = SRR908$CV_COUNTS + SRR909$CV_COUNTS
names(iso_mc) = gene_names
names(iso_cv) = gene_names
# Check for Skews in ASE ratios
hist(log2(SRR904$MC_COUNTS/SRR904$CV_COUNTS), breaks=50)
hist(log2(SRR905$MC_COUNTS/SRR905$CV_COUNTS), breaks=50)
hist(log2(SRR906$MC_COUNTS/SRR906$CV_COUNTS), breaks=50)
hist(log2(SRR907$MC_COUNTS/SRR907$CV_COUNTS), breaks=50)
hist(log2(SRR908$MC_COUNTS/SRR908$CV_COUNTS), breaks=50)
hist(log2(SRR909$MC_COUNTS/SRR909$CV_COUNTS), breaks=50)
pos = which(SRR904$MC_COUNTS > 0 & SRR904$CV_COUNTS > 0)
d = density(log2(SRR904$MC_COUNTS[pos]/SRR904$CV_COUNTS[pos]))
plot(d, main="Pit 1")
pos = which(SRR905$MC_COUNTS > 0 & SRR905$CV_COUNTS > 0)
d = density(log2(SRR905$MC_COUNTS[pos]/SRR905$CV_COUNTS[pos]))
plot(d, main="Pit 2")
pos = which(SRR906$MC_COUNTS > 0 & SRR906$CV_COUNTS > 0)
d = density(log2(SRR906$MC_COUNTS[pos]/SRR906$CV_COUNTS[pos]))
plot(d, main="Castle 1")
pos = which(SRR907$MC_COUNTS > 0 & SRR907$CV_COUNTS > 0)
d = density(log2(SRR907$MC_COUNTS[pos]/SRR907$CV_COUNTS[pos]))
plot(d, main="Castle 2")
pos = which(SRR908$MC_COUNTS > 0 & SRR908$CV_COUNTS > 0)
d = density(log2(SRR908$MC_COUNTS[pos]/SRR908$CV_COUNTS[pos]))
plot(d, main="Isolated 1")
pos = which(SRR909$MC_COUNTS > 0 & SRR909$CV_COUNTS > 0)
d = density(log2(SRR909$MC_COUNTS[pos]/SRR909$CV_COUNTS[pos]))
plot(d, main="Isolated 2")
# Find Discordant ASE
disc_ase = gene_names[which(sign(SRR904$dif) == sign(SRR905$dif) & sign(SRR904$dif) != 0 &
sign(SRR906$dif) == sign(SRR907$dif) & sign(SRR906$dif) != 0 &
sign(SRR906$dif) != sign(SRR904$dif))]
disc_ase_pc = disc_ase
disc_ase_pc_hgnc = sort(hgncMzebraInPlace(data.frame(disc_ase_pc), 1, rownames(tj)))
write.table(disc_ase_pc, "C:/Users/miles/Downloads/brain/results/ase/disc_ASE_pc.txt", quote = F, col.names = F, row.names = F)
write.table(disc_ase_pc_hgnc, "C:/Users/miles/Downloads/brain/results/ase/disc_ASE_pc_hgnc.txt", quote = F, col.names = F, row.names = F)
# Do 1-sampled ASE experiments
pos_all_ind = which(SRR904$MC_COUNTS + SRR904$CV_COUNTS > 0 &
SRR905$MC_COUNTS + SRR905$CV_COUNTS > 0 &
SRR906$MC_COUNTS + SRR906$CV_COUNTS > 0 &
SRR907$MC_COUNTS + SRR907$CV_COUNTS > 0 &
SRR908$MC_COUNTS + SRR908$CV_COUNTS > 0 &
SRR909$MC_COUNTS + SRR909$CV_COUNTS > 0)
SRR904_1 = my_MBASED_1(SRR904$MC_COUNTS, SRR904$CV_COUNTS, "SRR904 (Pit 1)", "", gene_names, n_boot)
SRR905_1 = my_MBASED_1(SRR905$MC_COUNTS, SRR905$CV_COUNTS, "SRR905 (Pit 2)", "", gene_names, n_boot)
SRR906_1 = my_MBASED_1(SRR906$MC_COUNTS, SRR906$CV_COUNTS, "SRR906 (Castle 1)", "", gene_names, n_boot)
SRR907_1 = my_MBASED_1(SRR907$MC_COUNTS, SRR907$CV_COUNTS, "SRR907 (Castle 2)", "", gene_names, n_boot)
SRR908_1 = my_MBASED_1(SRR908$MC_COUNTS, SRR908$CV_COUNTS, "SRR908 (Isolated 1)", "", gene_names, n_boot)
SRR909_1 = my_MBASED_1(SRR909$MC_COUNTS, SRR909$CV_COUNTS, "SRR909 (Isolated 2)", "", gene_names, n_boot)
SRR904$p = 1; SRR905$p = 1; SRR906$p = 1; SRR907$p = 1; SRR908$p = 1; SRR909$p = 1
SRR904$q = 1; SRR905$q = 1; SRR906$q = 1; SRR907$q = 1; SRR908$q = 1; SRR909$q = 1
SRR904$p[which(SRR904$GENE %in% rownames(assays(SRR904_1[[1]])$pValueASE))] = assays(SRR904_1[[1]])$pValueASE
SRR905$p[which(SRR905$GENE %in% rownames(assays(SRR905_1[[1]])$pValueASE))] = assays(SRR905_1[[1]])$pValueASE
SRR906$p[which(SRR906$GENE %in% rownames(assays(SRR906_1[[1]])$pValueASE))] = assays(SRR906_1[[1]])$pValueASE
SRR907$p[which(SRR907$GENE %in% rownames(assays(SRR907_1[[1]])$pValueASE))] = assays(SRR907_1[[1]])$pValueASE
SRR908$p[which(SRR908$GENE %in% rownames(assays(SRR908_1[[1]])$pValueASE))] = assays(SRR908_1[[1]])$pValueASE
SRR909$p[which(SRR909$GENE %in% rownames(assays(SRR909_1[[1]])$pValueASE))] = assays(SRR909_1[[1]])$pValueASE
SRR904$q[which(SRR904$GENE %in% rownames(assays(SRR904_1[[1]])$pValueASE))] = p.adjust(assays(SRR904_1[[1]])$pValueASE, method="bonferroni")
SRR905$q[which(SRR905$GENE %in% rownames(assays(SRR905_1[[1]])$pValueASE))] = p.adjust(assays(SRR905_1[[1]])$pValueASE, method="bonferroni")
SRR906$q[which(SRR906$GENE %in% rownames(assays(SRR906_1[[1]])$pValueASE))] = p.adjust(assays(SRR906_1[[1]])$pValueASE, method="bonferroni")
SRR907$q[which(SRR907$GENE %in% rownames(assays(SRR907_1[[1]])$pValueASE))] = p.adjust(assays(SRR907_1[[1]])$pValueASE, method="bonferroni")
SRR908$q[which(SRR908$GENE %in% rownames(assays(SRR908_1[[1]])$pValueASE))] = p.adjust(assays(SRR908_1[[1]])$pValueASE, method="bonferroni")
SRR909$q[which(SRR909$GENE %in% rownames(assays(SRR909_1[[1]])$pValueASE))] = p.adjust(assays(SRR909_1[[1]])$pValueASE, method="bonferroni")
SRR904$sig = SRR904$q < 0.05
SRR905$sig = SRR905$q < 0.05
SRR906$sig = SRR906$q < 0.05
SRR907$sig = SRR907$q < 0.05
SRR908$sig = SRR908$q < 0.05
SRR909$sig = SRR909$q < 0.05
SRR904$dif = SRR904$MC_COUNTS - SRR904$CV_COUNTS
SRR905$dif = SRR905$MC_COUNTS - SRR905$CV_COUNTS
SRR906$dif = SRR906$MC_COUNTS - SRR906$CV_COUNTS
SRR907$dif = SRR907$MC_COUNTS - SRR907$CV_COUNTS
SRR908$dif = SRR908$MC_COUNTS - SRR908$CV_COUNTS
SRR909$dif = SRR909$MC_COUNTS - SRR909$CV_COUNTS
SRR904$ase = log2(SRR904$CV_COUNTS / SRR904$MC_COUNTS)
SRR905$ase = log2(SRR905$CV_COUNTS / SRR905$MC_COUNTS)
SRR906$ase = log2(SRR906$CV_COUNTS / SRR906$MC_COUNTS)
SRR907$ase = log2(SRR907$CV_COUNTS / SRR907$MC_COUNTS)
SRR908$ase = log2(SRR908$CV_COUNTS / SRR908$MC_COUNTS)
SRR909$ase = log2(SRR909$CV_COUNTS / SRR909$MC_COUNTS)
df = data.frame(cbind(SRR904$GENE, SRR904$ase, SRR905$ase, SRR906$ase, SRR907$ase, SRR908$ase, SRR909$ase))
df = data.frame(cbind(SRR904, SRR905, SRR906, SRR907, SRR908, SRR909))
df$Digging_1 = as.numeric(as.vector(df$Digging_1))
df$Digging_2 = as.numeric(as.vector(df$Digging_2))
df$Building_1 = as.numeric(as.vector(df$Building_1))
df$Building_2 = as.numeric(as.vector(df$Building_2))
df$Control_1 = as.numeric(as.vector(df$Control_1))
df$Control_2 = as.numeric(as.vector(df$Control_2))
my_ryan = df[which(df[,1] %in% ryan$X),]
my_ryan = my_ryan[match(ryan$X, my_ryan[,1]),]
colnames(my_ryan) = c("GENE", "Digging_1", "Digging_2", "Building_1", "Building_2", "Control_1", "Control_2")
my_ryan$Digging_Mean_ASE = (as.numeric(as.vector(my_ryan$Digging_1)) + as.numeric(as.vector(my_ryan$Digging_2)))/2
my_ryan$Building_Mean_ASE = (as.numeric(as.vector(my_ryan$Building_1)) + as.numeric(as.vector(my_ryan$Building_2)))/2
my_ryan$Control_Mean_ASE = (as.numeric(as.vector(my_ryan$Control_1)) + as.numeric(as.vector(my_ryan$Control_2)))/2
length(which( is.na(my_ryan[,2]) & is.na(my_ryan[,3]) & is.na(my_ryan[,4]) & is.na(my_ryan[,5]) & is.na(my_ryan[,6]) & is.na(my_ryan[,7]) ))
length(which(sign(my_ryan$Digging_Mean_ASE) == sign(ryan$Digging_Mean_ASE) & sign(my_ryan$Building_Mean_ASE) == sign(ryan$Building_Mean_ASE) & sign(my_ryan$Control_Mean_ASE) == sign(ryan$Iso_Mean_ASE) ))
all_sig_same_dir = SRR904$GENE[which(SRR904$sig & SRR905$sig & SRR906$sig & SRR907$sig & SRR908$sig & SRR909$sig &
sign(SRR905$dif) == sign(SRR904$dif) &
sign(SRR906$dif) == sign(SRR904$dif) &
sign(SRR907$dif) == sign(SRR904$dif) &
sign(SRR908$dif) == sign(SRR904$dif) &
sign(SRR909$dif) == sign(SRR904$dif) )]
all_same_dir = SRR904$GENE[which(sign(SRR905$dif) == sign(SRR904$dif) & SRR905$dif != 0 & SRR904$dif != 0 &
sign(SRR906$dif) == sign(SRR904$dif) & SRR906$dif != 0 & SRR904$dif != 0 &
sign(SRR907$dif) == sign(SRR904$dif) & SRR907$dif != 0 & SRR904$dif != 0 &
sign(SRR908$dif) == sign(SRR904$dif) & SRR908$dif != 0 & SRR904$dif != 0 &
sign(SRR909$dif) == sign(SRR904$dif) & SRR909$dif != 0 & SRR904$dif != 0 )]
# Zack's Method: Combine p-values
# all_p = sapply(1:length(gene_names), function(x) sumlog(c(SRR904$p[x], SRR905$p[x], SRR906$p[x], SRR907$p[x], SRR908$p[x], SRR909$p[x]))$p)
all_p = sapply(1:length(gene_names), function(x) sumz(c(SRR904$p[x], SRR905$p[x], SRR906$p[x], SRR907$p[x], SRR908$p[x], SRR909$p[x]))$p)
all_p[which(SRR904$p == 0 &
SRR905$p == 0 &
SRR906$p == 0 &
SRR907$p == 0 &
SRR908$p == 0 &
SRR909$p == 0 )] = 0
all_q = p.adjust(all_p, method = "BH")
agg = gene_names[which(all_q < 0.05 &
sign(SRR905$dif) == sign(SRR904$dif) &
sign(SRR906$dif) == sign(SRR904$dif) &
sign(SRR907$dif) == sign(SRR904$dif) &
sign(SRR908$dif) == sign(SRR904$dif) &
sign(SRR909$dif) == sign(SRR904$dif) )]
write.table(all_sig_same_dir, "C:/Users/miles/Downloads/brain/results/ase_all_sig_same_dir_RG.txt", quote = F, col.names = F, row.names = F)
write.table(all_same_dir, "C:/Users/miles/Downloads/brain/results/ase_all_same_dir_RG.txt", quote = F, col.names = F, row.names = F)
write.table(agg, "C:/Users/miles/Downloads/brain/results/ase_agg_sig_same_dir_RG.txt", quote = F, col.names = F, row.names = F)
all_sig_same_dir_hgnc = hgncMzebraInPlace(data.frame(all_sig_same_dir), 1, gene_names)
all_same_dir_hgnc = hgncMzebraInPlace(data.frame(all_same_dir), 1, gene_names)
agg_hgnc = hgncMzebraInPlace(data.frame(agg), 1, gene_names)
write.table(all_sig_same_dir_hgnc, "C:/Users/miles/Downloads/brain/results/ase_all_sig_same_dir_hgnc_RG.txt", quote = F, col.names = F, row.names = F)
write.table(all_same_dir_hgnc, "C:/Users/miles/Downloads/brain/results/ase_all_same_dir_hgnc_RG.txt", quote = F, col.names = F, row.names = F)
write.table(agg_hgnc, "C:/Users/miles/Downloads/brain/results/ase_agg_sig_same_dir_hgnc_RG.txt", quote = F, col.names = F, row.names = F)
# Do 2-sampled ASE experiments
# Pit v Castle
pit_v_castle_res = my_MBASED(pit_mc, pit_cv, castle_mc, castle_cv, "pit", "castle", gene_names, n_boot)
pit_v_castle_genes = pit_v_castle_res[[2]]
castle_v_pit_res = my_MBASED(castle_mc, castle_cv, pit_mc, pit_cv, "castle", "pit", gene_names, n_boot)
castle_v_pit_genes = castle_v_pit_res[[2]]
ovlp_pc_v_cp = pit_v_castle_genes[which(pit_v_castle_genes %in% castle_v_pit_genes)]
# Pit v Isolated
pit_v_iso_res = my_MBASED(pit_mc, pit_cv, iso_mc, iso_cv, "pit", "iso", gene_names, n_boot)
pit_v_iso_genes = pit_v_iso_res[[2]]
iso_v_pit_res = my_MBASED(iso_mc, iso_cv, pit_mc, pit_cv, "iso", "pit", gene_names, n_boot)
iso_v_pit_genes = iso_v_pit_res[[2]]
ovlp_pi_v_ip = pit_v_iso_genes[which(pit_v_iso_genes %in% iso_v_pit_genes)]
# Castle v Isolated
castle_v_iso_res = my_MBASED(castle_mc, castle_cv, iso_mc, iso_cv, "castle", "iso", gene_names, n_boot)
castle_v_iso_genes = castle_v_iso_res[[2]]
iso_v_castle_res = my_MBASED(iso_mc, iso_cv, castle_mc, castle_cv, "iso", "castle", gene_names, n_boot)
iso_v_castle_genes = iso_v_castle_res[[2]]
ovlp_ci_v_ic = castle_v_iso_genes[which(castle_v_iso_genes %in% iso_v_castle_genes)]
res = data.frame(test=c("pit_v_castle", "castle_v_pit", "pvc_cvp_ovlp", "pit_v_iso", "iso_v_pit", "pvi_ivp_ovlp", "castle_v_iso", "iso_v_castle", "cvi_ivc"),
num_genes=c(length(pit_v_castle_genes), length(castle_v_pit_genes), length(ovlp_pc_v_cp),
length(pit_v_iso_genes), length(iso_v_pit_genes), length(ovlp_pi_v_ip),
length(castle_v_iso_genes), length(iso_v_castle_genes), length(ovlp_ci_v_ic)))
write.table(pit_v_castle_genes, "C:/Users/miles/Downloads/brain/results/ase_pit_v_castle_RG.txt", quote = F, col.names = F, row.names = F)
write.table(castle_v_pit_genes, "C:/Users/miles/Downloads/brain/results/ase_castle_v_pit_RG.txt", quote = F, col.names = F, row.names = F)
write.table(pit_v_iso_genes, "C:/Users/miles/Downloads/brain/results/ase_pit_v_iso_RG.txt", quote = F, col.names = F, row.names = F)
write.table(iso_v_pit_genes, "C:/Users/miles/Downloads/brain/results/ase_iso_v_pit_RG.txt", quote = F, col.names = F, row.names = F)
write.table(castle_v_iso_genes, "C:/Users/miles/Downloads/brain/results/ase_castle_v_iso_RG.txt", quote = F, col.names = F, row.names = F)
write.table(iso_v_castle_genes, "C:/Users/miles/Downloads/brain/results/ase_iso_v_castle_RG.txt", quote = F, col.names = F, row.names = F)
write.table(ovlp_pc_v_cp, "C:/Users/miles/Downloads/brain/results/ase_ovlp_pc_v_cp_RG.txt", quote = F, col.names = F, row.names = F)
write.table(ovlp_pi_v_ip, "C:/Users/miles/Downloads/brain/results/ase_ovlp_pi_v_ip_RG.txt", quote = F, col.names = F, row.names = F)
write.table(ovlp_ci_v_ic, "C:/Users/miles/Downloads/brain/results/ase_ovlp_ci_v_ic_RG.txt", quote = F, col.names = F, row.names = F)
pit_v_castle_genes_hgnc = hgncMzebraInPlace(data.frame(pit_v_castle_genes), 1, gene_names)
castle_v_pit_genes_hgnc = hgncMzebraInPlace(data.frame(castle_v_pit_genes), 1, gene_names)
pit_v_iso_genes_hgnc = hgncMzebraInPlace(data.frame(pit_v_iso_genes), 1, gene_names)
iso_v_pit_genes_hgnc = hgncMzebraInPlace(data.frame(iso_v_pit_genes), 1, gene_names)
castle_v_iso_genes_hgnc = hgncMzebraInPlace(data.frame(pit_v_iso_genes), 1, gene_names)
iso_v_castle_genes_hgnc = hgncMzebraInPlace(data.frame(iso_v_castle_genes), 1, gene_names)
ovlp_pc_v_cp_hgnc = hgncMzebraInPlace(data.frame(ovlp_pc_v_cp), 1, gene_names)
ovlp_pi_v_ip_hgnc = hgncMzebraInPlace(data.frame(ovlp_pi_v_ip), 1, gene_names)
ovlp_ci_v_ic_hgnc = hgncMzebraInPlace(data.frame(ovlp_ci_v_ic), 1, gene_names)
write.table(pit_v_castle_genes_hgnc, "C:/Users/miles/Downloads/brain/results/ase_pit_v_castle_hgnc.txt", quote = F, col.names = F, row.names = F)
write.table(castle_v_pit_genes_hgnc, "C:/Users/miles/Downloads/brain/results/ase_castle_v_pit_hgnc.txt", quote = F, col.names = F, row.names = F)
write.table(pit_v_iso_genes_hgnc, "C:/Users/miles/Downloads/brain/results/ase_pit_v_iso_hgnc.txt", quote = F, col.names = F, row.names = F)
write.table(iso_v_pit_genes_hgnc, "C:/Users/miles/Downloads/brain/results/ase_iso_v_pit_hgnc.txt", quote = F, col.names = F, row.names = F)
write.table(castle_v_iso_genes_hgnc, "C:/Users/miles/Downloads/brain/results/ase_castle_v_iso_hgnc.txt", quote = F, col.names = F, row.names = F)
write.table(iso_v_castle_genes_hgnc, "C:/Users/miles/Downloads/brain/results/ase_iso_v_castle_hgnc.txt", quote = F, col.names = F, row.names = F)
write.table(ovlp_pc_v_cp_hgnc, "C:/Users/miles/Downloads/brain/results/ase_ovlp_pc_v_cp_hgnc.txt", quote = F, col.names = F, row.names = F)
write.table(ovlp_pi_v_ip_hgnc, "C:/Users/miles/Downloads/brain/results/ase_ovlp_pi_v_ip_hgnc.txt", quote = F, col.names = F, row.names = F)
write.table(ovlp_ci_v_ic_hgnc, "C:/Users/miles/Downloads/brain/results/ase_ovlp_ci_v_ic_hgnc.txt", quote = F, col.names = F, row.names = F)
my_MBASED_1 = function(s1_mc, s1_cv, s1_name, gene_ind, gene_names, n_boot, myIsPhased=T, verbose=T) {
# Purpose: Run a one sampled MBASED Experiment
# s1_mc: sample 1 mc counts
# s1_cv: sample 1 cv counts
# gene_ind: index of genes to run on (aka subset of gene indexes), set to "" to find pos genes in this sample
# gene_names: genes the counts are for (aka all genes)
# n_boot: number of bootstraps in runMBASED
# pos_ind = 1:length(gene_names)
pos_ind = gene_ind
if (pos_ind == "") {
pos_ind = which( s1_mc + s1_cv > 0)
}
pos_gene = gene_names[pos_ind]
this_s1_mc = s1_mc[pos_ind]
this_s1_cv = s1_cv[pos_ind]
if (verbose) {
print(paste("Genes Used", length(pos_gene)))
}
# Create the SummarizedExperiment and run MBASED
my_granges = GRanges(seqnames = rep("chr1:1-2", length(pos_gene)), aseID=pos_gene)
# lociAllele1Counts
s1_exp = SummarizedExperiment(assays=list(
lociAllele1Counts = matrix( c(this_s1_mc), ncol=1, dimnames = list(pos_gene, s1_name)),
lociAllele2Counts = matrix( c(this_s1_cv), ncol=1, dimnames = list(pos_gene, s1_name))
), rowRanges = my_granges)
s1 = runMBASED(ASESummarizedExperiment=s1_exp, isPhased = myIsPhased, numSim = n_boot)
# Analyze MBASED Data
# hist(assays(s1)$majorAlleleFrequencyDifference, main=paste(s1_name, "MAF"), xlab = "Major Allele Frequency")
# hist(assays(s1)$pValueASE, main=paste(s1_name, "p-value"), xlab = "p-value")
qvalue = p.adjust(assays(s1)$pValueASE, method="bonferroni")
s1_genes = pos_gene[which(qvalue < 0.05)]
return(list(s1, s1_genes))
}
my_MBASED = function(s1_mc, s1_cv, s2_mc, s2_cv, s1_name, s2_name, gene_names, n_boot, myIsPhased=T, verbose=T, isSNP=F) {
# Purpose: Run a two sampled MBASED Experiment
# s1_mc: sample 1 mc counts
# s1_cv: sample 1 cv counts
# s2_mc: sample 2 mc counts
# s2_cv: sample 2 cv counts
# s1_name: name of sample 1 (for example "pit")
# s2_name: name of sample 2 (for example "castle")
# gene_names: genes the counts are for
# n_boot: number of bootstraps in runMBASED
# First find non-zero loci bc according to the documentation:
# "All supplied loci must have total read count (across both alleles) greater than 0
# (in each of the two samples, in the case of two-sample analysis)."
if (isSNP) {
this_s1_mc = s1_mc[which(names(s1_mc) %in% names(s2_mc))]
this_s1_cv = s1_cv[which(names(s1_cv) %in% names(s2_cv))]
this_s2_mc = s2_mc[which(names(s2_mc) %in% names(s1_mc))]
this_s2_cv = s2_cv[which(names(s2_cv) %in% names(s1_cv))]
print(paste("SNPs lost from s1:", length(s1_mc) - length(this_s1_mc)))
print(paste("SNPs lost from s2:", length(s2_mc) - length(this_s2_mc)))
pos_gene = names(s1_mc)[which(names(s1_mc) %in% names(s2_mc))]
} else {
pos_ind = which( s1_mc + s1_cv > 0 & s2_mc + s2_cv > 0 )
pos_gene = gene_names[pos_ind]
this_s1_mc = s1_mc[pos_ind]
this_s1_cv = s1_cv[pos_ind]
this_s2_mc = s2_mc[pos_ind]
this_s2_cv = s2_cv[pos_ind]
if (verbose) {
print(paste("Genes Used", length(pos_gene)))
}
}
# Create the SummarizedExperiment and run MBASED
my_granges = GRanges(seqnames = rep("chr1:1-2", length(pos_gene)), aseID=pos_gene)
s1_v_s2_exp = SummarizedExperiment(assays=list(
lociAllele1Counts = matrix( c(this_s1_mc, this_s2_mc), ncol=2, dimnames = list(pos_gene, c(s1_name, s2_name))),
lociAllele2Counts = matrix( c(this_s1_cv, this_s2_cv), ncol=2, dimnames = list(pos_gene, c(s1_name, s2_name)))
), rowRanges = my_granges)
s1_v_s2 = runMBASED(ASESummarizedExperiment=s1_v_s2_exp, isPhased = myIsPhased, numSim = n_boot)
# Analyze MBASED Data
hist(assays(s1_v_s2)$majorAlleleFrequencyDifference, main=paste(s1_name, "v", s2_name, "MAF"), xlab = "Major Allele Frequency")
hist(assays(s1_v_s2)$pValueASE, main=paste(s1_name, "v", s2_name, "p-value"), xlab = "p-value")
qvalue = p.adjust(assays(s1_v_s2)$pValueASE, method="bonferroni")
s1_v_s2_genes = pos_gene[which(qvalue < 0.05)]
return(list(s1_v_s2, s1_v_s2_genes))
}
posToGene = function(all_pos, gtf) {
found_gene = c()
for (pos in all_pos) {
stop_1 = gregexpr(pattern = ':', pos)[[1]]
stop_2 = gregexpr(pattern = '-', pos)[[1]]
lg = substr(pos, 1, stop_1-1)
base = substr(pos, stop_1+1, stop_2-1)
this_found = gtf$gene_name[which(gtf$LG == lg & gtf$start+25000 <= base & gtf$stop+25000 >= base)]
found_gene = c(found_gene, this_found)
}
return(found_gene)
}
shuffleAlleles = function(s1_mc, s1_cv, s2_mc, s2_cv) {
all_mc = data.frame(s1_mc, s2_mc)
ind1 = sample(c(1,2), length(s1_mc), replace = T)
ind2 = ind1
ind2 = factor(ind1, levels = c("1", "2"))
ind2 = plyr::revalue(ind2, replace = c("1" = "2", "2" = "1"))
new_s1_mc = all_mc[as.matrix(data.frame(1:nrow(all_mc), as.numeric(as.vector(ind1))))]
new_s2_mc = all_mc[as.matrix(data.frame(1:nrow(all_mc), as.numeric(as.vector(ind2))))]
all_cv = data.frame(s1_cv, s2_cv)
ind1 = sample(c(1,2), length(s1_cv), replace = T)
ind2 = ind1
ind2 = factor(ind1, levels = c("1", "2"))
ind2 = plyr::revalue(ind2, replace = c("1" = "2", "2" = "1"))
new_s1_cv = all_cv[as.matrix(data.frame(1:nrow(all_cv), as.numeric(as.vector(ind1))))]
new_s2_cv = all_cv[as.matrix(data.frame(1:nrow(all_cv), as.numeric(as.vector(ind2))))]
res = data.frame(new_s1_mc, new_s1_cv, new_s2_mc, new_s2_cv)
return(res)
}
#===============#
# Bootstrapping #
#===============#
real_pc = length(pit_v_castle_genes)
real_cp = length(castle_v_pit_genes)
real_ovlp_pc_v_cp = length(ovlp_pc_v_cp)
boot_res = data.frame()
for (n in 1:n_boot) {
if(n == n_boot) {
cat(paste(n, "\n"))
} else if (n %% (n_boot/10) == 0 || n == 1) {
cat(n)
} else {
cat(".")
}
tryCatch({
# Pit v Castle
shuf_res = shuffleAlleles(pit_mc, pit_cv, castle_mc, castle_cv)
pit_v_castle_res = my_MBASED(shuf_res$new_s1_mc, shuf_res$new_s1_cv, shuf_res$new_s2_mc, shuf_res$new_s2_cv, "pit", "castle", gene_names, n_boot, verbose=F)
pit_v_castle_genes = pit_v_castle_res[[2]]
castle_v_pit_res = my_MBASED(shuf_res$new_s2_mc, shuf_res$new_s2_cv, shuf_res$new_s1_mc, shuf_res$new_s1_cv, "castle", "pit", gene_names, n_boot, verbose=F)
castle_v_pit_genes = castle_v_pit_res[[2]]
ovlp_pc_v_cp = pit_v_castle_genes[which(pit_v_castle_genes %in% castle_v_pit_genes)]
# # Pit v Isolated
# pit_v_iso_res = my_MBASED(pit_mc, pit_cv, iso_mc, iso_cv, "pit", "iso", gene_names, n_boot, verbose=F)
# pit_v_iso_genes = pit_v_iso_res[[2]]
# iso_v_pit_res = my_MBASED(iso_mc, iso_cv, pit_mc, pit_cv, "iso", "pit", gene_names, n_boot, verbose=F)
# iso_v_pit_genes = iso_v_pit_res[[2]]
# ovlp_pi_v_ip = pit_v_iso_genes[which(pit_v_iso_genes %in% iso_v_pit_genes)]
#
# # Castle v Isolated
# castle_v_iso_res = my_MBASED(castle_mc, castle_cv, iso_mc, iso_cv, "castle", "iso", gene_names, n_boot, verbose=F)
# castle_v_iso_genes = castle_v_iso_res[[2]]
# iso_v_castle_res = my_MBASED(iso_mc, iso_cv, castle_mc, castle_cv, "iso", "castle", gene_names, n_boot, verbose=F)
# iso_v_castle_genes = iso_v_castle_res[[2]]
# ovlp_ci_v_ic = castle_v_iso_genes[which(castle_v_iso_genes %in% iso_v_castle_genes)]
# boot_res = rbind(boot_res, t(c(n, ovlp_pc_v_cp, ovlp_pi_v_ip, ovlp_ci_v_ic)))
boot_res = rbind(boot_res, t(c(n, length(ovlp_pc_v_cp))))
}, error = function(e) {
print(paste("Error on boostrap", n))
})
}
# colnames(boot_res) = c("run", "overlap_in_pvc_and_cvp", "overlap_in_pvi_and_ivp", "overlap_in_cvi_and_ivc")
colnames(boot_res) = c("run", "overlap_in_pvc_and_cvp")
boot_res$above = boot_res$overlap_in_pvc_and_cvp > real_ovlp_pc_v_cp
ggplot(boot_res, aes(overlap_in_pvc_and_cvp, alpha=.7, fill=above)) + geom_histogram(alpha=0.5, color = "purple") + geom_vline(aes(xintercept = real_ovlp_pc_v_cp)) + geom_text(aes(x=real_ovlp_pc_v_cp, label="Real Value"), y = Inf, hjust=0, vjust=1, color = "black") + xlab("# of Gene in Overlap Between Pit v Castle and Castle v Pit") + ggtitle("Comparison Between Bootstrap Values and Real Value") + guides(color=F, alpha=F, fill=F)
print(paste("p-value =", length(boot_res$above[which(boot_res$above)]) / length(boot_res$above)))
#=========================================================================================
# Old UMD1 Data
#=========================================================================================
rna_path <- "C:/Users/miles/Downloads/brain/"
data <- read.table(paste(rna_path, "/data/disc_ase.txt", sep=""), header = TRUE)
disc_genes <- c()
for (gene in unique(data$gene)) {
this_rows <- data[which(data$gene == gene),]
if (gene == "atp1b4") {
print(this_rows)
}
if (this_rows$rep_1_ase_ratio[1] > 0 && this_rows$rep_2_ase_ratio[1] > 0 && nrow(this_rows) >= 2) { # both pos
for (i in 2:nrow(this_rows)) {
if (this_rows$rep_1_ase_ratio[i] < 0 && this_rows$rep_2_ase_ratio[i] < 0) {
disc_genes <- c(disc_genes, gene)
}
}
} else if (this_rows$rep_1_ase_ratio[1] < 0 && this_rows$rep_2_ase_ratio[1] < 0 && nrow(this_rows) >= 2) { # both neg
for (i in 2:nrow(this_rows)) {
if (this_rows$rep_1_ase_ratio[i] > 0 && this_rows$rep_2_ase_ratio[i] > 0) {
disc_genes <- c(disc_genes, gene)
}
}
}
}
mc_up <- c()
for (gene in unique(data$gene)) {
this_rows <- data[which(data$gene == gene),]
build_rows <- this_rows[which(this_rows$condition == "building"),]
iso_rows <- this_rows[which(this_rows$condition == "isolated"),]
dig_rows <- this_rows[which(this_rows$condition == "digging"),]
min_build <- min(build_rows$rep_1_ase_ratio, build_rows$rep_2_ase_ratio)
if (nrow(iso_rows) > 0 && nrow(dig_rows) > 0) { # only both up is considered mc_up
if (iso_rows$rep_1_ase_ratio[i] < min_build && iso_rows$rep_2_ase_ratio[i] < min_build && dig_rows$rep_1_ase_ratio[i] < min_build && dig_rows$rep_2_ase_ratio[i] < min_build) {
mc_up <- c(mc_up, gene)
}
} else { # either one up, is considered mc_up
if (nrow(iso_rows) > 0 && iso_rows$rep_1_ase_ratio[i] < min_build && iso_rows$rep_2_ase_ratio[i] < min_build) {
mc_up <- c(mc_up, gene)
}
if (nrow(dig_rows) > 0 && dig_rows$rep_1_ase_ratio[i] < min_build && dig_rows$rep_2_ase_ratio[i] < min_build) {
mc_up <- c(mc_up, gene)
}
}
}
df <- data.frame(gene <- mc_up, bio <- rep("MC_UP", length(mc_up)))
write.table(df, paste(rna_path, "/data/mc_up.txt", sep=""), sep="\t", quote = FALSE, col.names = FALSE, row.names = FALSE)
data = read.csv("C:/Users/miles/Downloads/cichlid_ase_common_genes_all_conditions_filtered_030920.csv", header = T)
test = as.vector(data[which( sign(data$Digging_Mean_ASE) != sign(data$Building_Mean_ASE) & sign(data$Building_1) == -1 ),1])
test = as.vector(data[which( sign(data$Digging_Mean_ASE) == sign(data$Building_Mean_ASE) & sign(data$Building_Mean_ASE) != sign(data$Iso_Mean_ASE) ),1])
test = as.vector(data[which( sign(data$Digging_Mean_ASE) == sign(data$Building_Mean_ASE) & sign(data$Building_Mean_ASE) == sign(data$Iso_Mean_ASE) & sign(data$Building_Mean_ASE) == -1 ),1])
ase_down_all = data.frame(umd1To2a(test), "ASE_DOWN_ALL")
write.table(ase_down_all, "C:/Users/miles/Downloads/brain/data/markers/ase_down_all_111820.txt", sep="\t", col.names = F, row.names = F, quote = F)
disc_ase_pc_castle_up_in_build = data.frame(umd1To2a(test), "DISC_ASE")
write.table(disc_ase_pc_pit_up_in_dig, "C:/Users/miles/Downloads/brain/data/markers/disc_ase_dig_v_build_pit_up_in_dig_111820.txt", sep="\t", col.names = F, row.names = F, quote = F)
=======
library("RUnit")
library("MBASED")
library("metap")
library("DESeq2")
library("apeglm")
library("EnhancedVolcano")
#=========================================================================================
# New UMD2a Data
#=========================================================================================
# DEG
rna_path = "C:/Users/miles/Downloads/brain/"
SRR904 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440904_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR905 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440905_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR906 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440906_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR907 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440907_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR908 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440908_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR909 = read.table(paste(rna_path, "/data/pit_castle_deg/SRR5440909_counter_per_gene.tsv", sep=""), sep="\t", header = F, stringsAsFactors = F)
SRR904 = SRR904[which(! duplicated(SRR904[,1])),]
SRR905 = SRR905[which(! duplicated(SRR905[,1])),]
SRR906 = SRR906[which(! duplicated(SRR906[,1])),]
SRR907 = SRR907[which(! duplicated(SRR907[,1])),]
SRR908 = SRR908[which(! duplicated(SRR908[,1])),]
SRR909 = SRR909[which(! duplicated(SRR909[,1])),]
genes = SRR904[-c(1:5),1]
mat = as.matrix(cbind(SRR904[-c(1:5),2], SRR905[-c(1:5),2], SRR906[-c(1:5),2], SRR907[-c(1:5),2], SRR908[-c(1:5),2], SRR909[-c(1:5),2]),
dimnames=list(genes, c("4", "5", "6", "7", "8", "9")))
mycolData = data.frame(samples=c("4", "5", "6", "7", "8", "9"),
cond=c("pit", "pit", "castle", "castle", "iso", "iso"),
isBhve=c("bhve", "bhve", "bhve", "bhve", "ctrl", "ctrl"))
dds = DESeqDataSetFromMatrix(countData = mat,
colData = mycolData,
design = ~ cond)
dds <- DESeq(dds)
resultsNames(dds)
res <- results(dds, name="cond_pit_vs_castle")
res <- lfcShrink(dds, coef="cond_pit_vs_castle", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Pit v Castle Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/pit_v_castle_deg.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/pit_v_castle_deg_hgnc.txt", quote=F, col.names = F, row.names = F)
# BHVE v CTRL
dds = DESeqDataSetFromMatrix(countData = mat,
colData = mycolData,
design = ~ isBhve)
dds <- DESeq(dds)
resultsNames(dds)
res <- results(dds, name="isBhve_ctrl_vs_bhve")
res <- lfcShrink(dds, coef="isBhve_ctrl_vs_bhve", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Bhve v Ctrl Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/bhve_v_ctrl_deg.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/bhve_v_ctrl_deg_hgnc.txt", quote=F, col.names = F, row.names = F)
# Sim Pit v Castle
mat_pvc = as.matrix(cbind(SRR904[-c(1:5),2], SRR905[-c(1:5),2], SRR906[-c(1:5),2], SRR907[-c(1:5),2]),
dimnames=list(genes, c("4", "5", "6", "7")))
colData_pvc = data.frame(samples=c("4", "5", "6", "7"),
sim1=c("pit", "castle", "pit", "castle"),
sim2=c("castle", "pit", "castle", "pit"))
dds = DESeqDataSetFromMatrix(countData = mat_pvc,
colData = colData_pvc,
design = ~sim1)
dds <- DESeq(dds)
resultsNames(dds)
res <- results(dds, name="sim1_pit_vs_castle")
res <- lfcShrink(dds, coef="sim1_pit_vs_castle", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Simulated Pit v Castle 1 Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/sim1_pit_v_castle.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/sim1_pit_v_castle_hgnc.txt", quote=F, col.names = F, row.names = F)
dds <- DESeq(dds)
res <- results(dds, name="sim2_pit_vs_castle")
res <- lfcShrink(dds, coef="sim2_pit_vs_castle", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Simulated Pit v Castle 2 Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/sim2_pit_v_castle.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/sim2_pit_v_castle_hgnc.txt", quote=F, col.names = F, row.names = F)
# Pit v Iso
mat_pvi = as.matrix(cbind(SRR904[-c(1:5),2], SRR905[-c(1:5),2], SRR908[-c(1:5),2], SRR909[-c(1:5),2]),
dimnames=list(genes, c("4", "5", "8", "9")))
colData_pvi = data.frame(samples=c("4", "5", "8", "9"),
cond=c("pit", "pit", "iso", "iso"))
dds = DESeqDataSetFromMatrix(countData = mat_pvi,
colData = colData_pvi,
design = ~cond)
dds <- DESeq(dds)
res <- results(dds, name="cond_pit_vs_iso")
res <- lfcShrink(dds, coef="cond_pit_vs_iso", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Pit v Isolated Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/pit_v_iso.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/pit_v_iso_hgnc.txt", quote=F, col.names = F, row.names = F)
# Castle v Iso
mat_cvi = as.matrix(cbind(SRR906[-c(1:5),2], SRR907[-c(1:5),2], SRR908[-c(1:5),2], SRR909[-c(1:5),2]),
dimnames=list(genes, c("6", "7", "8", "9")))
colData_cvi = data.frame(samples=c("6", "7", "8", "9"),
cond=c("castle", "castle", "iso", "iso"))
dds = DESeqDataSetFromMatrix(countData = mat_cvi,
colData = colData_cvi,
design = ~cond)
dds <- DESeq(dds)
res <- results(dds, name="cond_iso_vs_castle")
res <- lfcShrink(dds, coef="cond_iso_vs_castle", type="apeglm")
sig_ind = which(res$padj < 0.05 & res$log2FoldChange > 1)
sig_genes = genes[sig_ind]
res_df = data.frame(gene=genes, logFC=res$log2FoldChange, padj=res$padj)
rownames(res_df) = res_df$gene
EnhancedVolcano(res_df, lab=rownames(res_df), x="logFC", y="padj") + labs(subtitle="Castle v Isolated Volcano Plot") + theme(plot.title = element_blank(), plot.caption = element_blank())
sig_genes_hgnc = hgncMzebraInPlace(data.frame(sig_genes), 1, gene_names)
write.table(sig_genes, "C:/Users/miles/Downloads/brain/results/castle_v_iso.txt", quote=F, col.names = F, row.names = F)
write.table(sig_genes_hgnc, "C:/Users/miles/Downloads/brain/results/castle_v_iso_hgnc.txt", quote=F, col.names = F, row.names = F)
# Dendrogram
mat = matrix(cbind(SRR904[-c(1:5),2], SRR905[-c(1:5),2], SRR906[-c(1:5),2], SRR907[-c(1:5),2], SRR908[-c(1:5),2], SRR909[-c(1:5),2]), ncol = 6, dimnames = list(genes, c("pit", "pit", "castle", "castle", "iso", "iso")))
pit_v_castle_genes = read.table("C:/Users/miles/Downloads/brain/results/pit_v_castle.txt", header=F)
pit_v_castle_genes = as.vector(pit_v_castle_genes$V1)
p = degDend(mat, pit_v_castle_genes, "C:/Users/miles/Downloads/brain/results/pit_v_castle_dend.png", include_samples = c("pit", "castle"))
p = degDend(mat, pit_v_castle_genes, "C:/Users/miles/Downloads/brain/results/pit_v_castle_all_dend.png")
pit_v_iso_genes = read.table("C:/Users/miles/Downloads/brain/results/pit_v_iso.txt", header=F)
pit_v_iso_genes = as.vector(pit_v_iso_genes$V1)
p = degDend(mat, pit_v_iso_genes, "C:/Users/miles/Downloads/brain/results/pit_v_iso_dend.png", include_samples = c("pit", "iso"))
castle_v_iso_genes = read.table("C:/Users/miles/Downloads/brain/results/castle_v_iso.txt", header=F)
castle_v_iso_genes = as.vector(castle_v_iso_genes$V1)
p = degDend(mat, castle_v_iso_genes, "C:/Users/miles/Downloads/brain/results/castle_v_iso_dend.png", include_samples = c("castle", "iso"))
# SNP-level data
SRR904 = read.table(paste(rna_path, "/data/ase/SRR5440904_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR905 = read.table(paste(rna_path, "/data/ase/SRR5440905_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR906 = read.table(paste(rna_path, "/data/ase/SRR5440906_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR907 = read.table(paste(rna_path, "/data/ase/SRR5440907_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR908 = read.table(paste(rna_path, "/data/ase/SRR5440908_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR909 = read.table(paste(rna_path, "/data/ase/SRR5440909_informative.vcf", sep=""), header = F, stringsAsFactors = F)
SRR904$V6 = as.numeric(as.vector(SRR904$V6))
SRR905$V6 = as.numeric(as.vector(SRR905$V6))
SRR906$V6 = as.numeric(as.vector(SRR906$V6))
SRR907$V6 = as.numeric(as.vector(SRR907$V6))
SRR908$V6 = as.numeric(as.vector(SRR908$V6))
SRR909$V6 = as.numeric(as.vector(SRR909$V6))
SRR904$V7 = as.numeric(as.vector(SRR904$V7))
SRR905$V7 = as.numeric(as.vector(SRR905$V7))
SRR906$V7 = as.numeric(as.vector(SRR906$V7))
SRR907$V7 = as.numeric(as.vector(SRR907$V7))
SRR908$V7 = as.numeric(as.vector(SRR908$V7))
SRR909$V7 = as.numeric(as.vector(SRR909$V7))
SRR904$MC_COUNTS = SRR904$V6
SRR905$MC_COUNTS = SRR905$V6
SRR906$MC_COUNTS = SRR906$V6
SRR907$MC_COUNTS = SRR907$V6
SRR908$MC_COUNTS = SRR908$V6
SRR909$MC_COUNTS = SRR909$V6
SRR904$MC_COUNTS[which(SRR904$V14 == "False")] = SRR904$V7[which(SRR904$V14 == "False")]
SRR905$MC_COUNTS[which(SRR905$V14 == "False")] = SRR905$V7[which(SRR905$V14 == "False")]
SRR906$MC_COUNTS[which(SRR906$V14 == "False")] = SRR906$V7[which(SRR906$V14 == "False")]
SRR907$MC_COUNTS[which(SRR907$V14 == "False")] = SRR907$V7[which(SRR907$V14 == "False")]
SRR908$MC_COUNTS[which(SRR908$V14 == "False")] = SRR908$V7[which(SRR908$V14 == "False")]
SRR909$MC_COUNTS[which(SRR909$V14 == "False")] = SRR909$V7[which(SRR909$V14 == "False")]
SRR904$CV_COUNTS = SRR904$V7
SRR905$CV_COUNTS = SRR905$V7
SRR906$CV_COUNTS = SRR906$V7
SRR907$CV_COUNTS = SRR907$V7
SRR908$CV_COUNTS = SRR908$V7
SRR909$CV_COUNTS = SRR909$V7
SRR904$CV_COUNTS[which(SRR904$V14 == "False")] = SRR904$V6[which(SRR904$V14 == "False")]
SRR905$CV_COUNTS[which(SRR905$V14 == "False")] = SRR905$V6[which(SRR905$V14 == "False")]
SRR906$CV_COUNTS[which(SRR906$V14 == "False")] = SRR906$V6[which(SRR906$V14 == "False")]
SRR907$CV_COUNTS[which(SRR907$V14 == "False")] = SRR907$V6[which(SRR907$V14 == "False")]
SRR908$CV_COUNTS[which(SRR908$V14 == "False")] = SRR908$V6[which(SRR908$V14 == "False")]
SRR909$CV_COUNTS[which(SRR909$V14 == "False")] = SRR909$V6[which(SRR909$V14 == "False")]
SRR904$pos = paste0(SRR904$V1, ":", SRR904$V2, "-", SRR904$V2)
SRR905$pos = paste0(SRR905$V1, ":", SRR905$V2, "-", SRR905$V2)
SRR906$pos = paste0(SRR906$V1, ":", SRR906$V2, "-", SRR906$V2)
SRR907$pos = paste0(SRR907$V1, ":", SRR907$V2, "-", SRR907$V2)
SRR908$pos = paste0(SRR908$V1, ":", SRR908$V2, "-", SRR908$V2)
SRR909$pos = paste0(SRR909$V1, ":", SRR909$V2, "-", SRR909$V2)
pit = inner_join(SRR904, SRR905, by = "pos")
pit_mc = pit$MC_COUNTS.x + pit$MC_COUNTS.y
pit_cv = pit$CV_COUNTS.x + pit$CV_COUNTS.y
names(pit_mc) = pit$pos
names(pit_cv) = pit$pos
castle = inner_join(SRR906, SRR907, by = "pos")
castle_mc = castle$MC_COUNTS.x + castle$MC_COUNTS.y
castle_cv = castle$CV_COUNTS.x + castle$CV_COUNTS.y