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RNA and leChRO-seq scatterplots
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RNA and leChRO-seq scatterplots
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#Directory: /local/storage/projects/prophaseI/Limmaradnormalized/proseqvsrnaseq
#Date: 09/01/2021
#####################################################################################
#####Get RN FPKM of PRO-seq from DESeq2#####
#####################################################################################
library(DESeq2)
load(file = "../DESeq2.RData")
load("../bw.counts.RData")
combine.dat <- cbind(pol2Counts,
bodyCounts,
polyACounts)
colnames(combine.dat) <- c(paste0("pol2_", colnames(pol2Counts)),
paste0("body_", colnames(bodyCounts)),
paste0("polyA_", colnames(polyACounts)))
condition <- do.call(rbind, strsplit(colnames(combine.dat), "_R"))[, 1]
condition <- factor(condition)
# group <- levels(condition)
## condition1
sample.info <- data.frame(condition = condition,
row.names = colnames(combine.dat))
dds <- DESeqDataSetFromMatrix(countData = combine.dat,
colData = sample.info,
design = ~ condition )
#Size factors determined by bw library size/rad scaler;see excel file
sizeFactors(dds) <- c(6697004,7470531,14690503,3711683,3176013,18807759,14145068,33563464,6470470,6181809,5708847,13023631,6697004,7470531,14690503,3711683,3176013,18807759,14145068,33563464,6470470,6181809,5708847,13023631,6697004,7470531,14690503,3711683,3176013,18807759,14145068,33563464,6470470,6181809,5708847,13023631)
# dds <- DESeq(dds)
dds <- DESeq(dds, fitType = "local")
resultsNames(dds)
load(file = "../counts.normalized.RData")
counts.normalizedP <- counts.normalized[,21:24]
counts.normalizedPaverage <- as.data.frame(rowMeans(counts.normalizedP, na.rm=TRUE))
save(counts.normalizedPaverage, file = "./counts.normalizedPaverage.RData")
counts.normalizedLZ <- counts.normalized[,17:20]
counts.normalizedLZaverage <- as.data.frame(rowMeans(counts.normalizedLZ, na.rm=TRUE))
save(counts.normalizedLZaverage, file = "./counts.normalizedLZaverage.RData")
counts.normalizedD <- counts.normalized[,13:16]
counts.normalizedDaverage <- as.data.frame(rowMeans(counts.normalizedD, na.rm=TRUE))
save(counts.normalizedDaverage, file = "./counts.normalizedLZaverage.RData")
#####################################################################################
#####Get FPKM of RNA-seq from DESeq2#####
#####################################################################################
samples = as.data.frame(read.table("samples.txt", header = TRUE, stringsAsFactors=FALSE))
samples$Condition <- factor(samples$Condition)
samples
library(tximport)
library(readr)
library(GenomicFeatures)
library(biomaRt)
library(DESeq2)
library(ggplot2)
library(gplots)
library(ggrepel)
library(tidyr)
library(org.Mm.eg.db)
library(tibble)
gtf = "./gencode.vM25.annotation.gtf"
txdb=makeTxDbFromGFF(gtf,
format="gtf",
organism="Mus musculus")
k <- keys(txdb, keytype = "GENEID")
df <- AnnotationDbi::select(txdb, keys = k, keytype = "GENEID", columns = "TXNAME")
tx2gene <- df[, 2:1]
head(tx2gene)
setwd("./AllQuantFiles")
base_dir = getwd()
files <- file.path(base_dir, samples$Sample)
txi <- tximport(files,
type = "salmon",
tx2gene = tx2gene)
identical(samples$sample,colnames(txi$counts))
ddsRNA <- DESeqDataSetFromTximport(txi, samples, ~Condition)
ddsRNA <- ddsRNA[ rowSums(counts(ddsRNA)) > 1, ]
ddsRNA <- DESeq(ddsRNA)
P.LZ <- results(ddsRNA, contrast=c("Condition", "Pachynema", "Leptonema/Zygonema"))
summary(P.LZ)
out of 25649 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 5120, 20%
LFC < 0 (down) : 4568, 18%
outliers [1] : 30, 0.12%
low counts [2] : 5968, 23%
(mean count < 3)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
pdf("P_LZRNAseq.pdf")
plotMA(P.LZ, alpha = 0.05, colNonSig = "gray67", colSig = "dark red")
dev.off()
D.LZ <- results(ddsRNA, contrast=c("Condition", "Diplonema", "Leptonema/Zygonema"))
summary(D.LZ)
out of 25649 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 6571, 26%
LFC < 0 (down) : 6454, 25%
outliers [1] : 30, 0.12%
low counts [2] : 4973, 19%
(mean count < 2)
pdf("D_LZRNAseq.pdf")
plotMA(D.LZ, alpha = 0.05, colNonSig = "gray67", colSig = "dark red")
dev.off()
counts.normalizedRNA <- as.data.frame(counts(ddsRNA, normalized = T))
counts.normalizedRNAnames <- as.data.frame(gsub("\\..*","",rownames(counts.normalizedRNA)))
counts.normalizedRNA2 <- as.data.frame(c(counts.normalizedRNAnames, counts.normalizedRNA))
rownames(counts.normalizedRNA2) <- counts.normalizedRNA2[,1]
counts.normalizedRNA2$symbol <- mapIds(org.Mm.eg.db,
keys = rownames(counts.normalizedRNA2),
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")
counts.normalizedP <- counts.normalizedRNA2[,8:10]
counts.normalizedPRNAaverage <- as.data.frame(rowMeans(counts.normalizedP, na.rm=TRUE))
counts.normalizedPRNAaverage$symbol <- mapIds(org.Mm.eg.db,
keys = rownames(counts.normalizedPRNAaverage),
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")
save(counts.normalizedPRNAaverage, file = "./counts.normalizedPRNAaverage.RData")
counts.normalizedD <- counts.normalizedRNA2[,2:4]
counts.normalizedDRNAaverage <- as.data.frame(rowMeans(counts.normalizedD, na.rm=TRUE))
counts.normalizedDRNAaverage$symbol <- mapIds(org.Mm.eg.db,
keys = rownames(counts.normalizedDRNAaverage),
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")
save(counts.normalizedDRNAaverage, file = "./counts.normalizedDRNAaverage.RData")
counts.normalizedLZ <- counts.normalizedRNA2[,5:7]
counts.normalizedLZRNAaverage <- as.data.frame(rowMeans(counts.normalizedLZ, na.rm=TRUE))
counts.normalizedLZRNAaverage$symbol <- mapIds(org.Mm.eg.db,
keys = rownames(counts.normalizedLZRNAaverage),
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")
save(counts.normalizedLZRNAaverage, file = "./counts.normalizedLZRNAaverage.RData")
#####################################################################################
#####Get P Scatterplot with normalized FPKM (no gene)#####
#####################################################################################
union.gene <- intersect(rownames(counts.normalizedPaverage),
(counts.normalizedPRNAaverage$symbol))
RNAseq.geneP <- counts.normalizedPRNAaverage[match(union.gene, (counts.normalizedPRNAaverage$symbol)), ]
PROseq.geneP <- counts.normalizedPaverage[match(union.gene, rownames(counts.normalizedPaverage)), ]
plot.dat <- data.frame("RNAseq" = RNAseq.geneP[,1],
"PROseq" = PROseq.geneP)
rownames(plot.dat) <- rownames(PROseq.geneP)
plot.dat$RNAseq <- log(plot.dat$RNAseq)
plot.dat$PROseq <- log(plot.dat$PROseq)
plot.dat1 <- plot.dat[is.finite(rowSums(plot.dat)),]
#
lm_eqn <- function(df){
m <- lm(RNAseq ~ PROseq, df);
f <- summary(m)$fstatistic;
p <- pf(f[1],f[2],f[3],lower.tail=F);
attributes(p) <- NULL;
eq <- substitute(italic(r)^2~"="~r2*","~~italic(P)~p,
list(r2 = format(summary(m)$r.squared, digits = 3),
p = ifelse(p < 1e-10, "< 1e-10", format(p, digits = 3))
))
as.character(as.expression(eq));
}
library(ggplot2)
library(cowplot)
library(easyGgplot2)
library(RColorBrewer)
df <- data.frame(RNAseq = plot.dat1$RNAseq, PROseq = plot.dat1$PROseq,
d = densCols(plot.dat1$RNAseq, plot.dat1$PROseq,
colramp = colorRampPalette(rev(brewer.pal(11,'Spectral')))))
pdf("./RNA.PRO.PachyFPKMALLGENEJULY.pdf")
ggplot(df) +
geom_point(aes(RNAseq, PROseq, col = d), size = 2, alpha = 1) +
scale_color_identity() +
xlim(-3, 12) + ylim(-18, -5) +
xlab("RNAseq FPKM") +
ylab("PROseq FPKM") +
ggtitle("P") +
theme(plot.title = element_text(size = 17, face = "bold"),
axis.text=element_text(size=17),
axis.title=element_text(size=17,face="bold")) +
geom_smooth(aes(RNAseq, PROseq), method = "lm", level=0.5, size = 0.3, linetype="dashed")
dev.off()
max((range(plot.dat1$PROseq)))
min((range(plot.dat1$PROseq)))
max((range(plot.dat1$RNAseq)))
min((range(plot.dat1$RNAseq)))
rnaseq.lm = lm(RNAseq ~ PROseq, data=plot.dat1)
summary(rnaseq.lm)$r.squared
cor.test(plot.dat1$PROseq, plot.dat1$RNAseq, method = 'pearson')
Pearson's product-moment correlation
data: plot.dat1$PROseq and plot.dat1$RNAseq
t = 165.33, df = 13290, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.8146436 0.8257686
sample estimates:
cor
0.8202837
#####################################################################################
#####Get LZ Scatterplot with normalized FPKM (no gene)#####
#####################################################################################
union.gene <- intersect(rownames(counts.normalizedLZaverage),
(counts.normalizedLZRNAaverage$symbol))
RNAseq.geneLZ <- counts.normalizedLZRNAaverage[match(union.gene, (counts.normalizedLZRNAaverage$symbol)), ]
PROseq.geneLZ <- counts.normalizedLZaverage[match(union.gene, rownames(counts.normalizedLZaverage)), ]
plot.dat <- data.frame("RNAseq" = RNAseq.geneLZ[,1],
"PROseq" = PROseq.geneLZ)
rownames(plot.dat) <- rownames(PROseq.geneLZ)
plot.dat$RNAseq <- log(plot.dat$RNAseq)
plot.dat$PROseq <- log(plot.dat$PROseq)
plot.dat1 <- plot.dat[is.finite(rowSums(plot.dat)),]
#
lm_eqn <- function(df){
m <- lm(RNAseq ~ PROseq, df);
f <- summary(m)$fstatistic;
p <- pf(f[1],f[2],f[3],lower.tail=F);
attributes(p) <- NULL;
eq <- substitute(italic(r)^2~"="~r2*","~~italic(P)~p,
list(r2 = format(summary(m)$r.squared, digits = 3),
p = ifelse(p < 1e-10, "< 1e-10", format(p, digits = 3))
))
as.character(as.expression(eq));
}
library(ggplot2)
library(cowplot)
library(easyGgplot2)
library(RColorBrewer)
df <- data.frame(RNAseq = plot.dat1$RNAseq, PROseq = plot.dat1$PROseq,
d = densCols(plot.dat1$RNAseq, plot.dat1$PROseq,
colramp = colorRampPalette(rev(brewer.pal(11,'Spectral')))))
pdf("./RNA.PRO.LEPTOZYGOFPKMALLGENEJULY.pdf")
ggplot(df) +
geom_point(aes(RNAseq, PROseq, col = d), size = 2, alpha = 1) +
scale_color_identity() +
xlim(-3, 12) + ylim(-19, -7) +
xlab("RNAseq FPKM") +
ylab("PROseq FPKM") +
ggtitle("LZ") +
theme(plot.title = element_text(size = 17, face = "bold"),
axis.text=element_text(size=17),
axis.title=element_text(size=17,face="bold")) +
geom_smooth(aes(RNAseq, PROseq), method = "lm", level=0.5, size = 0.3, linetype="dashed")
dev.off()
max((range(plot.dat1$PROseq)))
min((range(plot.dat1$PROseq)))
max((range(plot.dat1$RNAseq)))
min((range(plot.dat1$RNAseq)))
rnaseq.lm = lm(RNAseq ~ PROseq, data=plot.dat1)
summary(rnaseq.lm)$r.squared
cor.test(plot.dat1$PROseq, plot.dat1$RNAseq, method = 'pearson')
Pearson's product-moment correlation
data: plot.dat1$PROseq and plot.dat1$RNAseq
t = 143.32, df = 13373, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.7714829 0.7848508
sample estimates:
cor
0.778255
#####################################################################################
#####Get D Scatterplot with normalized FPKM (no gene)#####
#####################################################################################
union.gene <- intersect(rownames(counts.normalizedDaverage),
(counts.normalizedDRNAaverage$symbol))
RNAseq.geneD <- counts.normalizedDRNAaverage[match(union.gene, (counts.normalizedDRNAaverage$symbol)), ]
PROseq.geneD <- counts.normalizedDaverage[match(union.gene, rownames(counts.normalizedDaverage)), ]
plot.dat <- data.frame("RNAseq" = RNAseq.geneD[,1],
"PROseq" = PROseq.geneD)
rownames(plot.dat) <- rownames(PROseq.geneD)
plot.dat$RNAseq <- log(plot.dat$RNAseq)
plot.dat$PROseq <- log(plot.dat$PROseq)
plot.dat1 <- plot.dat[is.finite(rowSums(plot.dat)),]
#
lm_eqn <- function(df){
m <- lm(RNAseq ~ PROseq, df);
f <- summary(m)$fstatistic;
p <- pf(f[1],f[2],f[3],lower.tail=F);
attributes(p) <- NULL;
eq <- substitute(italic(r)^2~"="~r2*","~~italic(P)~p,
list(r2 = format(summary(m)$r.squared, digits = 3),
p = ifelse(p < 1e-10, "< 1e-10", format(p, digits = 3))
))
as.character(as.expression(eq));
}
library(ggplot2)
library(cowplot)
library(easyGgplot2)
library(RColorBrewer)
df <- data.frame(RNAseq = plot.dat1$RNAseq, PROseq = plot.dat1$PROseq,
d = densCols(plot.dat1$RNAseq, plot.dat1$PROseq,
colramp = colorRampPalette(rev(brewer.pal(11,'Spectral')))))
pdf("./RNA.PRO.DIPLOFPKMALLGENEJULY.pdf")
ggplot(df) +
geom_point(aes(RNAseq, PROseq, col = d), size = 2, alpha = 1) +
scale_color_identity() +
xlim(-3, 12) + ylim(-18, -5) +
xlab("RNAseq FPKM") +
ylab("PROseq FPKM") +
ggtitle("D") +
theme(plot.title = element_text(size = 17, face = "bold"),
axis.text=element_text(size=17),
axis.title=element_text(size=17,face="bold")) +
geom_smooth(aes(RNAseq, PROseq), method = "lm", level=0.5, size = 0.3, linetype="dashed")
dev.off()
max((range(plot.dat1$PROseq)))
min((range(plot.dat1$PROseq)))
max((range(plot.dat1$RNAseq)))
min((range(plot.dat1$RNAseq)))
rnaseq.lm = lm(RNAseq ~ PROseq, data=plot.dat1)
summary(rnaseq.lm)$r.squared
cor.test(plot.dat1$PROseq, plot.dat1$RNAseq, method = 'pearson')
Pearson's product-moment correlation
data: plot.dat1$PROseq and plot.dat1$RNAseq
t = 149.94, df = 12689, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.7931418 0.8056984
sample estimates:
cor
0.7995074
#####################################################################################
#####LZP log2fc scatterplots#####
#####################################################################################
#PRO
body.P.LZ.rslt <- (results(dds, contrast=c("condition", "body_P", "body_LZ")))
body.P.LZ.rslt$padj <- ifelse(is.na(body.P.LZ.rslt$padj), 1, body.P.LZ.rslt$padj)
hits1 <- body.P.LZ.rslt[body.P.LZ.rslt$padj < 0.05, ] %>% rownames()
save(hits1, file= "./hits1.RData")
##RNA (DESeq2)
samples
resP_LZ <- results( ddsRNA, contrast = c("Condition", "Pachynema", "Leptonema/Zygonema"))
head(resP_LZ)
LZ.P.DESeq <- resP_LZ
LZ.P.DESeq$padj <- ifelse(is.na(LZ.P.DESeq$padj), 1, LZ.P.DESeq$padj)
hits2 <- LZ.P.DESeq[LZ.P.DESeq$padj < 0.05, ] %>% rownames()
hits2 <- gsub("\\..*","",hits2)
hits3 <- AnnotationDbi::select(org.Mm.eg.db, keys=hits2, columns="SYMBOL",
keytype="ENSEMBL")
hits4 <- hits3[,2]
save(hits4, file= "./hits4.RData")
union.gene <- (union(hits1, hits4))
save(union.gene, file="union.gene.RData")
LZ.P.DESeqnames <- as.data.frame(gsub("\\..*","",rownames(LZ.P.DESeq)))
LZ.P.DESeq <- as.data.frame(c(LZ.P.DESeqnames, LZ.P.DESeq))
rownames(LZ.P.DESeq) <- LZ.P.DESeq[,1]
LZ.P.DESeq$symbol <- mapIds(org.Mm.eg.db,
keys = rownames(LZ.P.DESeq),
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")
union.gene <- intersect(intersect(rownames(body.P.LZ.rslt),
(LZ.P.DESeq$symbol)),
union.gene)
RNAseq.DEgene <- LZ.P.DESeq[match(union.gene, (LZ.P.DESeq$symbol)), ]
PROseq.DEgene <- body.P.LZ.rslt[match(union.gene, rownames(body.P.LZ.rslt)), ]
plot.dat <- data.frame("RNAseq" = RNAseq.DEgene$log2FoldChange,
"PROseq" = PROseq.DEgene$log2FoldChange)
rownames(plot.dat) <- rownames(PROseq.DEgene)
plot.dat1 <- na.omit(plot.dat)
plot.dat1 <- plot.dat1[plot.dat1$RNAseq > -24,]
#
lm_eqn <- function(df){
m <- lm(RNAseq ~ PROseq, df);
f <- summary(m)$fstatistic;
p <- pf(f[1],f[2],f[3],lower.tail=F);
attributes(p) <- NULL;
eq <- substitute(italic(r)^2~"="~r2*","~~italic(P)~p,
list(r2 = format(summary(m)$r.squared, digits = 3),
p = ifelse(p < 1e-10, "< 1e-10", format(p, digits = 3))
))
as.character(as.expression(eq));
}
library(ggplot2)
library(cowplot)
library(easyGgplot2)
library(RColorBrewer)
df <- data.frame(RNAseq = plot.dat1$RNAseq, PROseq = plot.dat1$PROseq,
d = densCols(plot.dat1$RNAseq, plot.dat1$PROseq,
colramp = colorRampPalette(rev(brewer.pal(11,'Spectral')))))
pdf("./RNA.PROLZPl2fcJULY.pdf")
ggplot(df) +
geom_point(aes(RNAseq, PROseq, col = d), size = 2, alpha = 1) +
scale_color_identity() +
xlim(-6,8) + ylim(-3, 7) +
xlab("RNAseq FPKM") +
ylab("PROseq FPKM") +
ggtitle("P Vs. LZ") +
theme(plot.title = element_text(size = 17, face = "bold"),
axis.text=element_text(size=17),
axis.title=element_text(size=17,face="bold")) +
geom_smooth(aes(RNAseq, PROseq), method = "lm", level=0.5, size = 0.3, linetype="dashed")
dev.off()
max((range(plot.dat1$PROseq)))
min((range(plot.dat1$PROseq)))
max((range(plot.dat1$RNAseq)))
min((range(plot.dat1$RNAseq)))
rnaseq.lm = lm(RNAseq ~ PROseq, data=plot.dat1)
summary(rnaseq.lm)$r.squared
cor.test(plot.dat1$PROseq, plot.dat1$RNAseq, method = 'pearson')
Pearson's product-moment correlation
data: plot.dat1$PROseq and plot.dat1$RNAseq
t = 56.469, df = 7579, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.5281484 0.5598425
sample estimates:
cor
0.5441896
n <- length(plot.dat1$RNAseq)
sort(plot.dat1$RNAseq,partial=n-1)[n-1]
#####################################################################################
#####DLZ log2fc scatterplots#####
#####################################################################################
#PRO
body.D.P.rslt <- (results(dds, contrast=c("condition", "body_D", "body_LZ")))
body.D.P.rslt$padj <- ifelse(is.na(body.D.P.rslt$padj), 1, body.D.P.rslt$padj)
hits1 <- body.D.P.rslt[body.D.P.rslt$padj < 0.05, ] %>% rownames()
save(hits1, file= "./hits1.RData")
##RNA (DESeq2)
samples
resD_P <- results( ddsRNA, contrast = c("Condition", "Diplonema", "Leptonema/Zygonema"))
head(resD_P)
D.P.DESeq <- resD_P
D.P.DESeq$padj <- ifelse(is.na(D.P.DESeq$padj), 1, D.P.DESeq$padj)
hits2 <- D.P.DESeq[D.P.DESeq$padj < 0.05, ] %>% rownames()
hits2 <- gsub("\\..*","",hits2)
hits3 <- AnnotationDbi::select(org.Mm.eg.db, keys=hits2, columns="SYMBOL",
keytype="ENSEMBL")
hits4 <- hits3[,2]
save(hits4, file= "./hits4.RData")
union.gene <- (union(hits1, hits4))
save(union.gene, file="union.gene.RData")
D.P.DESeqnames <- as.data.frame(gsub("\\..*","",rownames(D.P.DESeq)))
D.P.DESeq <- as.data.frame(c(D.P.DESeqnames, D.P.DESeq))
rownames(D.P.DESeq) <- D.P.DESeq[,1]
D.P.DESeq$symbol <- mapIds(org.Mm.eg.db,
keys = rownames(D.P.DESeq),
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")
union.gene <- intersect(intersect(rownames(body.D.P.rslt),
(D.P.DESeq$symbol)),
union.gene)
RNAseq.DEgene <- D.P.DESeq[match(union.gene, (D.P.DESeq$symbol)), ]
PROseq.DEgene <- body.D.P.rslt[match(union.gene, rownames(body.D.P.rslt)), ]
plot.dat <- data.frame("RNAseq" = RNAseq.DEgene$log2FoldChange,
"PROseq" = PROseq.DEgene$log2FoldChange)
rownames(plot.dat) <- rownames(PROseq.DEgene)
plot.dat1 <- na.omit(plot.dat)
#
lm_eqn <- function(df){
m <- lm(RNAseq ~ PROseq, df);
f <- summary(m)$fstatistic;
p <- pf(f[1],f[2],f[3],lower.tail=F);
attributes(p) <- NULL;
eq <- substitute(italic(r)^2~"="~r2*","~~italic(P)~p,
list(r2 = format(summary(m)$r.squared, digits = 3),
p = ifelse(p < 1e-10, "< 1e-10", format(p, digits = 3))
))
as.character(as.expression(eq));
}
library(ggplot2)
library(cowplot)
library(easyGgplot2)
library(RColorBrewer)
df <- data.frame(RNAseq = plot.dat1$RNAseq, PROseq = plot.dat1$PROseq,
d = densCols(plot.dat1$RNAseq, plot.dat1$PROseq,
colramp = colorRampPalette(rev(brewer.pal(11,'Spectral')))))
pdf("./RNA.PRODLZl2fcJULY.pdf")
ggplot(df) +
geom_point(aes(RNAseq, PROseq, col = d), size = 2, alpha = 1) +
scale_color_identity() +
xlim(-7, 10) + ylim(-5, 7) +
xlab("RNAseq FPKM") +
ylab("PROseq FPKM") +
ggtitle("D Vs. LZ") +
theme(plot.title = element_text(size = 17, face = "bold"),
axis.text=element_text(size=17),
axis.title=element_text(size=17,face="bold")) +
geom_smooth(aes(RNAseq, PROseq), method = "lm", level=0.5, size = 0.3, linetype="dashed")
dev.off()
max((range(plot.dat1$PROseq)))
min((range(plot.dat1$PROseq)))
max((range(plot.dat1$RNAseq)))
min((range(plot.dat1$RNAseq)))
rnaseq.lm = lm(RNAseq ~ PROseq, data=plot.dat1)
summary(rnaseq.lm)$r.squared
cor.test(plot.dat1$PROseq, plot.dat1$RNAseq, method = 'pearson')
Pearson's product-moment correlation
data: plot.dat1$PROseq and plot.dat1$RNAseq
t = 74.525, df = 8410, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.6176170 0.6433628
sample estimates:
cor
0.6306634
n <- length(plot.dat1$RNAseq)
sort(plot.dat1$RNAseq,partial=n-1)[n-1]