Last updated: 2018-06-20

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    Rmd a1c2b7c kevinlkx 2018-06-20 compare centipede predictions for CTCF 

library(ggplot2)
library(grid)
library(gridExtra)
library(limma)
library(edgeR)

select TF

tf_name <- "CTCF"
pwm_name <- "CTCF_MA0139.1_1e-4"

thresh_PostPr_bound <- 0.99
cat(pwm_name, "\n")
CTCF_MA0139.1_1e-4

load CENTIPEDE predictions

dir_predictions <- paste0("~/Dropbox/research//ATAC-seq/for_Olivia_Gray/results/centipede_predictions/", pwm_name)

## condition: N
bam_namelist_N <- c("N1_nomito_rdup.bam", "N2_nomito_rdup.bam", "N3_nomito_rdup.bam")

site_predictions_N.l <- vector("list", 3)
names(site_predictions_N.l) <- bam_namelist_N

for(i in 1:length(bam_namelist_N)){
  bam_basename <- tools::file_path_sans_ext(basename(bam_namelist_N[[i]]))
  site_predictions_N.l[[i]] <- read.table(paste0(dir_predictions, "/", pwm_name, "_", bam_basename, "_predictions.txt.gz"), header = T, stringsAsFactors = F)
}

CentPostPr_N.df <- data.frame(N1 = site_predictions_N.l[[1]]$CentPostPr, 
                              N2 = site_predictions_N.l[[2]]$CentPostPr, 
                              N3 = site_predictions_N.l[[3]]$CentPostPr)

CentLogRatios_N.df <- data.frame(N1 = site_predictions_N.l[[1]]$CentLogRatios, 
                                 N2 = site_predictions_N.l[[2]]$CentLogRatios, 
                                 N3 = site_predictions_N.l[[3]]$CentLogRatios)


## condition: H
bam_namelist_H <- c("H1_nomito_rdup.bam", "H2_nomito_rdup.bam", "H3_nomito_rdup.bam")

site_predictions_H.l <- vector("list", 3)
names(site_predictions_H.l) <- bam_namelist_H

for(i in 1:length(bam_namelist_H)){
  bam_basename <- tools::file_path_sans_ext(basename(bam_namelist_H[[i]]))
  site_predictions_H.l[[i]] <- read.table(paste0(dir_predictions, "/", pwm_name, "_", bam_basename, "_predictions.txt.gz"), header = T, stringsAsFactors = F)
}

name_sites <- site_predictions_H.l[[1]]$name

CentPostPr_H.df <- data.frame(H1 = site_predictions_H.l[[1]]$CentPostPr, 
                              H2 = site_predictions_H.l[[2]]$CentPostPr, 
                              H3 = site_predictions_H.l[[3]]$CentPostPr)

CentLogRatios_H.df <- data.frame(H1 = site_predictions_H.l[[1]]$CentLogRatios, 
                                 H2 = site_predictions_H.l[[2]]$CentLogRatios, 
                                 H3 = site_predictions_H.l[[3]]$CentLogRatios)

CentPostPr.df <- cbind(CentPostPr_N.df, CentPostPr_H.df)
CentLogRatios.df <- cbind(CentLogRatios_N.df, CentLogRatios_H.df)

binarize to bound and unbound

cat("Number of bound sites: \n")
Number of bound sites: 
colSums(CentPostPr.df > thresh_PostPr_bound)
   N1    N2    N3    H1    H2    H3 
37688 33636 30943 18018 17260 24842 
idx_bound <- which(rowSums(CentPostPr.df > thresh_PostPr_bound) >= 2)
cat(length(idx_bound), "sites are bound in at least two samples \n")
34309 sites are bound in at least two samples 
cat(length(idx_bound), "(",round(length(idx_bound)/nrow(CentPostPr.df) *100, 2), "% ) sites are bound in at least two samples \n")
34309 ( 8.74 % ) sites are bound in at least two samples

Plot average binding and average logRatios

all sites

par(pty="s")
plot(rowMeans(CentPostPr_N.df), rowMeans(CentPostPr_H.df), 
     xlab = "N average P(Bound)", ylab = "H average P(Bound)", main = tf_name,
     pch = ".", col = rgb(0,0,1,0.7))
abline(a=0,b=1)

par(mfrow = c(1,2))
par(pty="s")
plot(rowMeans(CentLogRatios_N.df), rowMeans(CentLogRatios_H.df), 
     xlab = "N average logRatios", ylab = "H average logRatios", main = tf_name, 
     pch = ".", col = rgb(0,0,1,0.7))
abline(a=0,b=1,col = "darkgray")

plot(x = (rowMeans(CentLogRatios_H.df)+rowMeans(CentLogRatios_N.df))/2, 
     y = rowMeans(CentLogRatios_H.df) - rowMeans(CentLogRatios_N.df),
     xlab = "average logRatios", ylab = "Difference in logRatios (H - N)", main = tf_name,
     pch = ".", col = rgb(0,0,1,0.7))
abline(v=0, h=0, col = "darkgray")

bound sites

par(pty="s")
plot(rowMeans(CentPostPr_N.df[idx_bound,]), rowMeans(CentPostPr_H.df[idx_bound,]), 
     xlab = "N average P(Bound)", ylab = "H average P(Bound)", main = paste(tf_name, "bound sites"),
     pch = ".", col = rgb(0,0,1,0.7))
abline(a=0,b=1)

par(mfrow = c(1,2))
par(pty="s")
plot(rowMeans(CentLogRatios_N.df[idx_bound,]), rowMeans(CentLogRatios_H.df[idx_bound,]), 
     xlab = "N average logRatios", ylab = "H average logRatios", main = tf_name, 
     pch = ".", col = rgb(0,0,1,0.7))
abline(a=0,b=1,col = "darkgray")

plot(x = (rowMeans(CentLogRatios_H.df[idx_bound,])+rowMeans(CentLogRatios_N.df[idx_bound,]))/2, 
     y = rowMeans(CentLogRatios_H.df[idx_bound,]) - rowMeans(CentLogRatios_N.df[idx_bound,]),
     xlab = "average logRatios", ylab = "Difference in logRatios (H - N)", main = tf_name,
     pch = ".", col = rgb(0,0,1,0.7))
abline(v=0, h=0, col = "darkgray")

PCA

all sites

pca_logRatios <- prcomp(t(CentLogRatios.df))
percentage <- round(pca_logRatios$sdev / sum(pca_logRatios$sdev) * 100, 2)
percentage <- paste0( colnames(pca_logRatios$x), " (", paste( as.character(percentage), "%)") )

pca_logRatios.df <- as.data.frame(pca_logRatios$x)
pca_logRatios.df$group <- rep(c("N","H"), each = 3)
p <- ggplot(pca_logRatios.df, aes(x=PC1,y=PC2,color=group,label=row.names(pca_logRatios.df)))
p <- p + geom_point() + geom_text(size = 3, show.legend = F, vjust = 2, nudge_y = 0.5) + 
  labs(title = tf_name, x = percentage[1], y = percentage[2])
p

bound sites

pca_logRatios <- prcomp(t(CentLogRatios.df[idx_bound, ]))
percentage <- round(pca_logRatios$sdev / sum(pca_logRatios$sdev) * 100, 2)
percentage <- paste0( colnames(pca_logRatios$x), " (", paste( as.character(percentage), "%)") )

pca_logRatios.df <- as.data.frame(pca_logRatios$x)
pca_logRatios.df$group <- rep(c("N","H"), each = 3)
p <- ggplot(pca_logRatios.df, aes(x=PC1,y=PC2,color=group,label=row.names(pca_logRatios.df)))
p <- p + geom_point() + geom_text(size = 3, show.legend = F, vjust = 2, nudge_y = 0.5) + 
  labs(title = tf_name, x = percentage[1], y = percentage[2])
p

Differential logRatios for bound sites using limma

targets <- data.frame(bam = c(bam_namelist_N, bam_namelist_H), 
                      label = colnames(CentLogRatios.df), 
                      condition = rep(c("N", "H"), each = 3))

print(targets)
                 bam label condition
1 N1_nomito_rdup.bam    N1         N
2 N2_nomito_rdup.bam    N2         N
3 N3_nomito_rdup.bam    N3         N
4 H1_nomito_rdup.bam    H1         H
5 H2_nomito_rdup.bam    H2         H
6 H3_nomito_rdup.bam    H3         H
condition <- factor(targets$condition, levels = c("N", "H"))
design <- model.matrix(~0+condition)
colnames(design) <- levels(condition)
print(design)
  N H
1 1 0
2 1 0
3 1 0
4 0 1
5 0 1
6 0 1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$condition
[1] "contr.treatment"
CentLogRatios_Bound.df <- CentLogRatios.df[idx_bound, ]

fit <- lmFit(CentLogRatios_Bound.df, design)
contrasts <- makeContrasts(H-N, levels=design)
fit2 <- contrasts.fit(fit, contrasts)
fit2 <- eBayes(fit2, trend=TRUE)
num_diffbind <- summary(decideTests(fit2))
percent_diffbind <- round(num_diffbind / sum(num_diffbind) * 100, 2)
cat(percent_diffbind[1], "% down in H vs. N,", percent_diffbind[3], "% up in H vs. N \n")
63.53 % down in H vs. N, 0.01 % up in H vs. N 
# volcanoplot(fit2, main="H vs. N", xlab = "Difference in logRatios (H - N)")

plot(x = fit2$coef, y = -log10(fit2$p.value),
     xlab = "Difference in logRatios (H - N)", ylab = "-log10(P-value)", main= paste(tf_name, "H vs. N"),
     pch = 16, cex = 0.35)

Session information

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.4

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] edgeR_3.20.9  limma_3.34.9  gridExtra_2.3 ggplot2_2.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.16      knitr_1.20        whisker_0.3-2    
 [4] magrittr_1.5      workflowr_1.0.1   splines_3.4.3    
 [7] munsell_0.4.3     lattice_0.20-35   colorspace_1.3-2 
[10] rlang_0.2.0       stringr_1.3.0     plyr_1.8.4       
[13] tools_3.4.3       gtable_0.2.0      R.oo_1.22.0      
[16] git2r_0.21.0      htmltools_0.3.6   yaml_2.1.18      
[19] lazyeval_0.2.1    rprojroot_1.3-2   digest_0.6.15    
[22] tibble_1.4.2      R.utils_2.6.0     evaluate_0.10.1  
[25] rmarkdown_1.9     labeling_0.3      stringi_1.1.7    
[28] pillar_1.2.2      compiler_3.4.3    scales_0.5.0     
[31] backports_1.1.2   R.methodsS3_1.7.1 locfit_1.5-9.1

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