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--- | ||
title: R code to generate Figure 2 | ||
author: Slim Fourati | ||
date: May 10, 2016 | ||
output: github_documents | ||
--- | ||
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loading require packages | ||
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```r | ||
suppressPackageStartupMessages(library(package = "knitr")) | ||
suppressPackageStartupMessages(library(package = "RCurl")) | ||
suppressPackageStartupMessages(library(package = "Biobase")) | ||
suppressPackageStartupMessages(library(package = "ggplot2")) | ||
suppressPackageStartupMessages(library(package = "cluster")) | ||
suppressPackageStartupMessages(library(package = "pheatmap")) | ||
suppressPackageStartupMessages(library(package = "readr")) | ||
suppressPackageStartupMessages(library(package = "dplyr")) | ||
suppressPackageStartupMessages(library(package = "tidyr")) | ||
suppressPackageStartupMessages(library(package = "tibble")) | ||
``` | ||
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set default options/variables | ||
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```r | ||
workDir <- dirname(getwd()) | ||
opts_chunk$set(tidy = FALSE, fig.path = "../figure/") | ||
options(stringsAsFactors = FALSE, width = 80) | ||
``` | ||
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read SLEA ExpressionSet | ||
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```r | ||
gsSetRawFile <- file.path(workDir, | ||
"output/rv144pilot.gsSetVehSubstracted.RData") | ||
load(file = gsSetRawFile) | ||
``` | ||
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plot barplot showing normalized enrichment score of enriched genesets | ||
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```r | ||
plotDF <- fData(gsSetRaw) %>% | ||
filter(`FDR q-val` <= 0.05) %>% | ||
arrange(NES, `FDR q-val`) %>% | ||
mutate(NAME = factor(NAME, levels = NAME), | ||
`FDR q-val` = pmax(`FDR q-val`, 1e-04), | ||
`FDR q-val` = -log10(`FDR q-val`) * sign(NES)) %>% | ||
select(NAME, NES, `FDR q-val`) | ||
plotBar <- ggplot(data = plotDF, | ||
mapping = aes(x = NAME, y = NES, fill = `FDR q-val`)) + | ||
geom_bar(stat = "identity") + | ||
scale_y_continuous(expand = c(0.01, 0)) + | ||
scale_fill_gradient2(name = "-log10(FDR q-val)", | ||
low = "blue", mid = "white", high = "red") + | ||
labs(x = NULL, y = "GSEA: NES") + | ||
geom_hline(yintercept = 0) + | ||
coord_flip() + | ||
theme_bw() + | ||
theme(axis.line.x = element_line(color = "black"), | ||
axis.line.y = element_blank(), | ||
axis.text.y = element_text(hjust = 1, vjust = 0.5), | ||
axis.ticks.y = element_blank(), | ||
panel.border = element_blank(), | ||
panel.grid = element_blank()) | ||
print(plotBar) | ||
``` | ||
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![plot of chunk fig2a](../figure/fig2a-1.png) | ||
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determine the number of set of correlated genesets | ||
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```r | ||
flag <- pData(gsSetRaw) %>% | ||
filter(`stimulation time` %in% 15) %>% | ||
select(donor, visit, `Sample name`) %>% | ||
spread(visit, `Sample name`) %>% | ||
filter(complete.cases(.)) %>% | ||
gather(visit, `Sample name`, -donor) | ||
mat <- exprs(gsSetRaw)[fData(gsSetRaw)$"FDR q-val" <= 0.05, | ||
flag$"Sample name"] | ||
hclustFun <- function(X, k) { | ||
distMat <- as.dist((1-cor(t(X)))/2) | ||
hc <- hclust(distMat) | ||
group <- cutree(hc, k) | ||
return(value = list(cluster = group)) | ||
} | ||
set.seed(seed = 111) | ||
fit <- clusGap(mat, hclustFun, K.max = 10) | ||
plot(fit) | ||
``` | ||
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![plot of chunk figs1](../figure/figs1-1.png) | ||
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```r | ||
print(fit, method = "globalSEmax") | ||
``` | ||
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``` | ||
## Clustering Gap statistic ["clusGap"] from call: | ||
## clusGap(x = mat, FUNcluster = hclustFun, K.max = 10) | ||
## B=100 simulated reference sets, k = 1..10; spaceH0="scaledPCA" | ||
## --> Number of clusters (method 'globalSEmax', SE.factor=1): 4 | ||
## logW E.logW gap SE.sim | ||
## [1,] 4.332752 4.286456 -0.04629572 0.09489605 | ||
## [2,] 4.113765 3.979655 -0.13411004 0.12651150 | ||
## [3,] 3.903299 3.727155 -0.17614382 0.11567892 | ||
## [4,] 3.208124 3.504154 0.29603045 0.11518557 | ||
## [5,] 3.065703 3.267838 0.20213479 0.11898030 | ||
## [6,] 2.843997 3.035234 0.19123737 0.14227075 | ||
## [7,] 2.606959 2.743604 0.13664472 0.16986437 | ||
## [8,] 2.306303 2.389217 0.08291446 0.20388936 | ||
## [9,] 2.034552 1.882376 -0.15217670 0.24243110 | ||
## [10,] 1.294605 1.106186 -0.18841879 0.33161430 | ||
``` | ||
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```r | ||
Kmax <- maxSE(fit$Tab[, "gap"], fit$Tab[, "SE.sim"], method = "globalSEmax") | ||
``` | ||
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correlation heatmap | ||
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```r | ||
corMat <- cor(t(mat)) | ||
distMat <- as.dist(1 - corMat) | ||
pheatmap(mat = corMat, | ||
color = colorRampPalette(c("white", | ||
"yellow", | ||
"orange", | ||
"red"))(100), | ||
breaks = c(min(corMat), | ||
seq(from = 0, | ||
to = 1, | ||
length.out = 100)), | ||
cellwidth = 8, | ||
cellheight = 8, | ||
clustering_distance_rows = distMat, | ||
clustering_distance_cols = distMat, | ||
treeheight_row = 0) | ||
``` | ||
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![plot of chunk fig2b](../figure/fig2b-1.png) | ||
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heatmap geneset by pathway | ||
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```r | ||
colOrder <- apply(mat, 1, rank) %>% | ||
rowMeans() %>% | ||
data.frame(meanRank = .) %>% | ||
rownames_to_column() %>% | ||
merge(pData(gsSetRaw), by.y = "Sample name", by.x = "rowname") %>% | ||
arrange(treatment, desc(visit), meanRank) | ||
pheatmap(mat = mat[, colOrder$rowname], | ||
color = colorRampPalette(c("blue", | ||
"white", | ||
"red"))(100), | ||
breaks = c(-max(abs(mat)), | ||
seq(from = -1 * min(abs(range(mat))), | ||
to = min(abs(range(mat))), | ||
length.out = 99), | ||
max(abs(mat))), | ||
cellwidth = 3, | ||
cellheight = 8, | ||
fontsize = 8, | ||
cluster_cols = FALSE, | ||
clustering_distance_rows = "correlation", | ||
annotation_col = pData(gsSetRaw)[, c("visit", "treatment")], | ||
annotation_color = list(treatment = c(VACCINE = "green", | ||
PLACEBO = "blue"), | ||
visit = c(pre = "purple", | ||
post = "orange")), | ||
show_colnames = FALSE) | ||
``` | ||
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![plot of chunk fig2c](../figure/fig2c-1.png) | ||
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print session info | ||
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```r | ||
sessionInfo() | ||
``` | ||
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``` | ||
## R version 3.5.1 (2018-07-02) | ||
## Platform: x86_64-apple-darwin17.6.0 (64-bit) | ||
## Running under: macOS High Sierra 10.13.6 | ||
## | ||
## Matrix products: default | ||
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib | ||
## LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.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 stats graphics grDevices utils datasets methods | ||
## [8] base | ||
## | ||
## other attached packages: | ||
## [1] bindrcpp_0.2.2 tibble_1.4.2 tidyr_0.8.1 | ||
## [4] dplyr_0.7.6 readr_1.1.1 pheatmap_1.0.10 | ||
## [7] cluster_2.0.7-1 ggplot2_3.0.0 Biobase_2.40.0 | ||
## [10] BiocGenerics_0.26.0 RCurl_1.95-4.11 bitops_1.0-6 | ||
## [13] knitr_1.20 | ||
## | ||
## loaded via a namespace (and not attached): | ||
## [1] Rcpp_0.12.18 highr_0.7 pillar_1.3.0 compiler_3.5.1 | ||
## [5] RColorBrewer_1.1-2 plyr_1.8.4 bindr_0.1.1 tools_3.5.1 | ||
## [9] digest_0.6.15 evaluate_0.11 gtable_0.2.0 pkgconfig_2.0.1 | ||
## [13] rlang_0.2.1 withr_2.1.2 stringr_1.3.1 hms_0.4.2 | ||
## [17] grid_3.5.1 tidyselect_0.2.4 glue_1.3.0 R6_2.2.2 | ||
## [21] purrr_0.2.5 magrittr_1.5 scales_1.0.0 assertthat_0.2.0 | ||
## [25] colorspace_1.3-2 labeling_0.3 stringi_1.2.4 lazyeval_0.2.1 | ||
## [29] munsell_0.5.0 crayon_1.3.4 | ||
``` |
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