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<h1 class="title toc-ignore">Compare CENTIPEDE predictions for HIF1A</h1>
<h4 class="author"><em>Kaixuan Luo</em></h4>
<h4 class="date"><em>6/18/2018</em></h4>
</div>
<p><strong>Last updated:</strong> 2018-06-20</p>
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compare centipede predictions for HIF1A
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<p></details></p>
<hr />
<pre class="r"><code>library(ggplot2)
library(grid)
library(gridExtra)
library(limma)
library(edgeR)</code></pre>
<div id="select-tf" class="section level2">
<h2>select TF</h2>
<pre class="r"><code>tf_name <- "HIF1A"
pwm_name <- "HIF1A::ARNT_MA0259.1_1e-4"
thresh_PostPr_bound <- 0.99
cat(pwm_name, "\n")</code></pre>
<pre><code>HIF1A::ARNT_MA0259.1_1e-4 </code></pre>
</div>
<div id="load-centipede-predictions" class="section level2">
<h2>load CENTIPEDE predictions</h2>
<pre class="r"><code>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)</code></pre>
</div>
<div id="binarize-to-bound-and-unbound" class="section level2">
<h2>binarize to bound and unbound</h2>
<pre class="r"><code>cat("Number of bound sites: \n")</code></pre>
<pre><code>Number of bound sites: </code></pre>
<pre class="r"><code>colSums(CentPostPr.df > thresh_PostPr_bound)</code></pre>
<pre><code> N1 N2 N3 H1 H2 H3
4139 3834 3539 2334 2213 2788 </code></pre>
<pre class="r"><code>idx_bound <- which(rowSums(CentPostPr.df > thresh_PostPr_bound) >= 2)
cat(length(idx_bound), "sites are bound in at least two samples \n")</code></pre>
<pre><code>3882 sites are bound in at least two samples </code></pre>
<pre class="r"><code>cat(length(idx_bound), "(",round(length(idx_bound)/nrow(CentPostPr.df) *100, 2), "% ) sites are bound in at least two samples \n")</code></pre>
<pre><code>3882 ( 6.85 % ) sites are bound in at least two samples </code></pre>
</div>
<div id="plot-average-binding-and-average-logratios" class="section level2">
<h2>Plot average binding and average logRatios</h2>
<div id="all-sites" class="section level3">
<h3>all sites</h3>
<pre class="r"><code>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)</code></pre>
<p><img src="figure/compare_centipede_predictions_HIF1A.Rmd/unnamed-chunk-5-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>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")</code></pre>
<p><img src="figure/compare_centipede_predictions_HIF1A.Rmd/unnamed-chunk-5-2.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="bound-sites" class="section level3">
<h3>bound sites</h3>
<pre class="r"><code>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)</code></pre>
<p><img src="figure/compare_centipede_predictions_HIF1A.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>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")</code></pre>
<p><img src="figure/compare_centipede_predictions_HIF1A.Rmd/unnamed-chunk-6-2.png" width="672" style="display: block; margin: auto;" /></p>
</div>
</div>
<div id="pca" class="section level2">
<h2>PCA</h2>
<div id="all-sites-1" class="section level3">
<h3>all sites</h3>
<pre class="r"><code>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</code></pre>
<p><img src="figure/compare_centipede_predictions_HIF1A.Rmd/unnamed-chunk-7-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="bound-sites-1" class="section level3">
<h3>bound sites</h3>
<pre class="r"><code>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$group <- rep(c("N","H"), each = 3)
p <- ggplot(pca_logRatios.df, aes(x=PC1,y=PC2,color=group,label=row.names(pca_logRatios.df)))
p <- p + geom_point() + geom_text(size = 3, show.legend = F, vjust = 2, nudge_y = 0.5) +
labs(title = tf_name, x = percentage[1], y = percentage[2])
p</code></pre>
<p><img src="figure/compare_centipede_predictions_HIF1A.Rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
</div>
<div id="differential-logratios-for-bound-sites-using-limma" class="section level2">
<h2>Differential logRatios for bound sites using limma</h2>
<pre class="r"><code>targets <- data.frame(bam = c(bam_namelist_N, bam_namelist_H),
label = colnames(CentLogRatios.df),
condition = rep(c("N", "H"), each = 3))
print(targets)</code></pre>
<pre><code> bam label condition
1 N1_nomito_rdup.bam N1 N
2 N2_nomito_rdup.bam N2 N
3 N3_nomito_rdup.bam N3 N
4 H1_nomito_rdup.bam H1 H
5 H2_nomito_rdup.bam H2 H
6 H3_nomito_rdup.bam H3 H</code></pre>
<pre class="r"><code>condition <- factor(targets$condition, levels = c("N", "H"))
design <- model.matrix(~0+condition)
colnames(design) <- levels(condition)
print(design)</code></pre>
<pre><code> 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")$condition
[1] "contr.treatment"</code></pre>
<pre class="r"><code>CentLogRatios_Bound.df <- CentLogRatios.df[idx_bound, ]
fit <- lmFit(CentLogRatios_Bound.df, design)
contrasts <- makeContrasts(H-N, levels=design)
fit2 <- contrasts.fit(fit, contrasts)
fit2 <- eBayes(fit2, trend=TRUE)
num_diffbind <- summary(decideTests(fit2))
percent_diffbind <- round(num_diffbind / sum(num_diffbind) * 100, 2)
cat(percent_diffbind[1], "% down in H vs. N,", percent_diffbind[3], "% up in H vs. N \n")</code></pre>
<pre><code>63.34 % down in H vs. N, 0.52 % up in H vs. N </code></pre>
<pre class="r"><code># volcanoplot(fit2, main="H vs. N", xlab = "Difference in logRatios (H - N)")
plot(x = fit2$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)</code></pre>
<p><img src="figure/compare_centipede_predictions_HIF1A.Rmd/unnamed-chunk-9-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>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 </code></pre>
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