Last updated: 2018-07-26

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    Rmd fa3ed4c kevinlkx 2018-07-26 compare centipede predictions for MEF2D in DiffAC regions 

library(ggplot2)
library(grid)
library(gridExtra)
suppressPackageStartupMessages(library(GenomicRanges))
library(limma)

Attaching package: 'limma'
The following object is masked from 'package:BiocGenerics':

    plotMA
library(edgeR)
library(VennDiagram)
Warning: package 'VennDiagram' was built under R version 3.4.4
Loading required package: futile.logger
message <- futile.logger::flog.threshold(futile.logger::ERROR, name = "VennDiagramLogger")

## venn diagram
plot_venn_overlaps <- function(overlaps.m, title = "", col_fill = NULL, category.names = NULL){
  grid.newpage()
  overlaps_venn.l <- lapply(as.data.frame(overlaps.m), function(x) which(x == 1))
  if(is.null(col_fill)){
    col_fill <-  1:length(overlaps_venn.l)
  }
  if(is.null(category.names)){
    category.names <- names(x)
  }
  
  venn.plot <- venn.diagram( 
    x = overlaps_venn.l,
    category.names = category.names, 
    filename = NULL,
    fill = col_fill,
    alpha=rep(0.5, length(overlaps_venn.l)), 
    cex = 1.5, 
    cat.fontface=4, 
    main=title) 
  grid.draw(venn.plot)
}

parameters

tf_name <- "MEF2D"
pwm_name <- "MEF2D_MA0773.1_1e-4"

thresh_PostPr_bound <- 0.99

flank <- 100

cat("PWM name: ", pwm_name, "\n")
PWM name:  MEF2D_MA0773.1_1e-4

load diff accessibility test results, comparing hypoxia vs. normoxia.

  • log fold change > 0 indicates differentially open in hypoxia.
  • log fold change < 0 indicates differentially open in normoxia.
diffAC_regions.df <- read.csv("~/Dropbox/research/ATAC_DNase/ATAC-seq_Olivia_Gray/results/DiffAC_regions/ordered_results_withcoords.csv")

cat(nrow(diffAC_regions.df), "regions in total \n")
138927 regions in total 
diffAC_regions.df <- diffAC_regions.df[, c("chr", "Start", "End","GeneID", "baseMean", "Strand",  "log2FoldChange", "lfcSE", "stat", "pvalue", "padj")]

diffAC_sig_regions.df <- diffAC_regions.df[diffAC_regions.df$padj < 0.1, ]
cat(nrow(diffAC_sig_regions.df), "significant regions \n")
2390 significant regions 
hist(diffAC_regions.df$log2FoldChange, xlab = "log2FoldChange", main = "Differentially open regions (FDR < 10%)")

diffAC_sigH_regions.df <- diffAC_sig_regions.df[diffAC_sig_regions.df$log2FoldChange > 0, ]
cat(nrow(diffAC_sigH_regions.df), "regions differentially open in hypoxia. \n")
201 regions differentially open in hypoxia. 
diffAC_sigN_regions.df <- diffAC_sig_regions.df[diffAC_sig_regions.df$log2FoldChange < 0, ]
cat(nrow(diffAC_sigN_regions.df), "regions differentially open in normoxia. \n")
2189 regions differentially open in normoxia. 
diffAC_sig_regions.gr <- makeGRangesFromDataFrame(diffAC_sig_regions.df, start.field = "Start", end.field = "End", keep.extra.columns = T)

diffAC_sigH_regions.gr <- makeGRangesFromDataFrame(diffAC_sigH_regions.df, start.field = "Start", end.field = "End", keep.extra.columns = T)

diffAC_sigN_regions.gr <- makeGRangesFromDataFrame(diffAC_sigN_regions.df, start.field = "Start", end.field = "End", keep.extra.columns = T)

load CENTIPEDE predictions

dir_predictions <- paste0("~/Dropbox/research/ATAC_DNase/ATAC-seq_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"), 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"), header = T, stringsAsFactors = F)
}

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)

if(any(site_predictions_N.l[[1]]$name != site_predictions_H.l[[1]]$name)){
  stop("sites not match!")
}

sites.df <- site_predictions_N.l[[1]][,1:7]

## get motif coordinates
if(sites.df[1, "end"] - sites.df[1, "start"] > flank){
  sites.df$start <- sites.df$start + flank
  sites.df$end <- sites.df$end - flank
}


sites.gr <- makeGRangesFromDataFrame(sites.df, start.field = "start", end.field = "end", keep.extra.columns = F)


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

sites_CentPostPr.df <- cbind(sites.df, CentPostPr_N.df, CentPostPr_H.df)
sites_CentLogRatios.df <- cbind(sites.df, CentLogRatios_N.df, CentLogRatios_H.df)

intersect CENTIPEDE sites with diffAC regions

overlaps_diffAC.df <- as.data.frame(findOverlaps(query = sites.gr, subject = diffAC_sig_regions.gr, type = "within", ignore.strand = T))
idx_sites_diffAC <- unique(overlaps_diffAC.df$queryHits)
cat(length(idx_sites_diffAC), "candidate motif sites differentially open in hypoxia or normoxia. \n")
123 candidate motif sites differentially open in hypoxia or normoxia. 
overlaps_sigH.df <- as.data.frame(findOverlaps(query = sites.gr, subject = diffAC_sigH_regions.gr, type = "within", ignore.strand = T))
idx_sites_sigH <- unique(overlaps_sigH.df$queryHits)

cat(length(idx_sites_sigH), "candidate motif sites differentially open in hypoxia. \n")
23 candidate motif sites differentially open in hypoxia. 
overlaps_sigN.df <- as.data.frame(findOverlaps(query = sites.gr, subject = diffAC_sigN_regions.gr, type = "within", ignore.strand = T))
idx_sites_sigN <- unique(overlaps_sigN.df$queryHits)

cat(length(idx_sites_sigN), "candidate motif sites differentially open in normoxia. \n")
100 candidate motif sites differentially open in normoxia.

number of reads mapped

dir_bam <- "~/Dropbox/research/ATAC_DNase/ATAC-seq_Olivia_Gray/ATAC-seq_BAMfiles/"

bam_basename_list <- c("N1_nomito_rdup", "N2_nomito_rdup", "N3_nomito_rdup", 
                  "H1_nomito_rdup", "H2_nomito_rdup", "H3_nomito_rdup")

reads_mapped <- rep(NA, length = length(bam_basename_list))
names(reads_mapped) <- bam_basename_list

for(bam_basename in bam_basename_list){
  readstats_name <-  paste0(dir_bam, "/", bam_basename, ".idxstats.txt")
  readstats.df <- read.table(readstats_name, header = F)
  reads_mapped[bam_basename] <- sum(readstats.df[,3])
}

cat("Number of reads mapped (million): \n")
Number of reads mapped (million): 
print(reads_mapped/1e6)
N1_nomito_rdup N2_nomito_rdup N3_nomito_rdup H1_nomito_rdup H2_nomito_rdup 
      44.52970       45.87505       43.06390       30.81426       38.17168 
H3_nomito_rdup 
      37.04096

binarize to bound and unbound

cat("Number of bound sites that are differentially open in hypoxia: \n")
Number of bound sites that are differentially open in hypoxia: 
colSums(CentPostPr.df[idx_sites_sigH, ] > thresh_PostPr_bound)
N1 N2 N3 H1 H2 H3 
19 19 16 23 20 23 
cat("Number of bound sites that are differentially open in normoxia: \n")
Number of bound sites that are differentially open in normoxia: 
colSums(CentPostPr.df[idx_sites_sigN, ] > thresh_PostPr_bound)
 N1  N2  N3  H1  H2  H3 
100  99  99  80  71  90

Average binding probablity and average logRatios

all motif sites

# binding probablity
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, col = "darkgray")

# logRatios
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")

sites that are differentially open in hypoxia

cat(length(idx_sites_sigH), "candidate motif sites differentially open in hypoxia. \n")
23 candidate motif sites differentially open in hypoxia. 
# binding probablity
par(pty="s")
plot(rowMeans(CentPostPr_N.df[idx_sites_sigH,]), rowMeans(CentPostPr_H.df[idx_sites_sigH,]), 
     xlab = "N average P(Bound)", ylab = "H average P(Bound)", main = paste(tf_name, "bound sites"),
     pch = 20, col = rgb(0,0,1,0.7))
abline(a=0, b=1, col = "darkgray")

# logRatios
par(mfrow = c(1,2))
par(pty="s")
plot(rowMeans(CentLogRatios_N.df[idx_sites_sigH,]), rowMeans(CentLogRatios_H.df[idx_sites_sigH,]), 
     xlab = "N average logRatios", ylab = "H average logRatios", main = tf_name, 
     pch = 20, col = rgb(0,0,1,0.7))
abline(a=0,b=1,col = "darkgray")

plot(x = (rowMeans(CentLogRatios_H.df[idx_sites_sigH,])+rowMeans(CentLogRatios_N.df[idx_sites_sigH,]))/2, 
     y = rowMeans(CentLogRatios_H.df[idx_sites_sigH,]) - rowMeans(CentLogRatios_N.df[idx_sites_sigH,]),
     xlab = "average logRatios", ylab = "Difference in logRatios (H - N)", main = tf_name,
     pch = 20, col = rgb(0,0,1,0.7))
abline(v=0, h=0, col = "darkgray")

sites that are differentially open in normoxia

cat(length(idx_sites_sigN), "candidate motif sites differentially open in normoxia \n")
100 candidate motif sites differentially open in normoxia 
# binding probablity
par(pty="s")
plot(rowMeans(CentPostPr_N.df[idx_sites_sigN,]), rowMeans(CentPostPr_H.df[idx_sites_sigN,]), 
     xlab = "N average P(Bound)", ylab = "H average P(Bound)", main = paste(tf_name, "bound sites"),
     pch = 20, col = rgb(0,0,1,0.7))
abline(a=0, b=1, col = "darkgray")

# logRatios
par(mfrow = c(1,2))
par(pty="s")
plot(rowMeans(CentLogRatios_N.df[idx_sites_sigN,]), rowMeans(CentLogRatios_H.df[idx_sites_sigN,]), 
     xlab = "N average logRatios", ylab = "H average logRatios", main = tf_name, 
     pch = 20, col = rgb(0,0,1,0.7))
abline(a=0,b=1,col = "darkgray")

plot(x = (rowMeans(CentLogRatios_H.df[idx_sites_sigN,])+rowMeans(CentLogRatios_N.df[idx_sites_sigN,]))/2, 
     y = rowMeans(CentLogRatios_H.df[idx_sites_sigN,]) - rowMeans(CentLogRatios_N.df[idx_sites_sigN,]),
     xlab = "average logRatios", ylab = "Difference in logRatios (H - N)", main = tf_name,
     pch = 20, col = rgb(0,0,1,0.7))
abline(v=0, h=0, col = "darkgray")

Compare logRatios for differentially accessible 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_diffAC.df <- CentLogRatios.df[idx_sites_diffAC, ]

fit <- lmFit(CentLogRatios_diffAC.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(num_diffbind[1], "sites differentially open in normoxia (", percent_diffbind[1], "%) \n", 
    num_diffbind[3], "sites differentially open in hypoxia (", percent_diffbind[3], "%) \n",
    num_diffbind[2], "sites not significantly different (", percent_diffbind[2], "%) \n")
0 sites differentially open in normoxia ( 0 %) 
 0 sites differentially open in hypoxia ( 0 %) 
 123 sites not significantly different ( 100 %) 
# 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 10.13.6

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] parallel  stats4    grid      stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] VennDiagram_1.6.20   futile.logger_1.4.3  edgeR_3.20.9        
 [4] limma_3.34.9         GenomicRanges_1.30.3 GenomeInfoDb_1.14.0 
 [7] IRanges_2.12.0       S4Vectors_0.16.0     BiocGenerics_0.24.0 
[10] gridExtra_2.3        ggplot2_2.2.1       

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.16           formatR_1.5            XVector_0.18.0        
 [4] compiler_3.4.3         pillar_1.2.2           git2r_0.21.0          
 [7] plyr_1.8.4             workflowr_1.1.1        futile.options_1.0.1  
[10] zlibbioc_1.24.0        R.methodsS3_1.7.1      R.utils_2.6.0         
[13] bitops_1.0-6           tools_3.4.3            digest_0.6.15         
[16] lattice_0.20-35        evaluate_0.10.1        tibble_1.4.2          
[19] gtable_0.2.0           rlang_0.2.0            yaml_2.1.18           
[22] GenomeInfoDbData_1.0.0 stringr_1.3.0          knitr_1.20            
[25] locfit_1.5-9.1         rprojroot_1.3-2        rmarkdown_1.9         
[28] lambda.r_1.2.2         magrittr_1.5           whisker_0.3-2         
[31] splines_3.4.3          backports_1.1.2        scales_0.5.0          
[34] htmltools_0.3.6        colorspace_1.3-2       stringi_1.1.7         
[37] RCurl_1.95-4.10        lazyeval_0.2.1         munsell_0.4.3         
[40] R.oo_1.22.0

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