Last updated: 2020-04-24
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Knit directory: BgeeCall_practical/
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This section present R code of downstream analysis that can be done using genes with presence of expression.
# read file with gene as rows and libraries as columns
file <- "/cloud/project/input_files/downstream_analysis/present_TPMs.tsv"
present_TPMs <- read.table(file = file, header = TRUE, sep = "\t")
# transpose data.frame to have genes as columns
data_for_PCA <- t(present_TPMs)
# calculate matrix of dissimilarities
# k = the maximum dimension of the space which the data are to be represented in; must be in {1, 2, …, n-1}
# eig = indicates whether eigenvalues should be returned
mds <- cmdscale(dist(data_for_PCA), k=3, eig=TRUE)
proportion_eig <- mds$eig * 100 / sum(mds$eig)
# plot proportion of variance explained by each dimension
png(file="PCA_prop_explained_variance.png")
barplot(proportion_eig, las=1, ylim=c(0,100), xlab="Dimensions", ylab="Proportion of explained variance (%)",
y.axis=NULL, col="darkgrey", main = "Proportion of variance explained by each dimension")
dev.off()
# calculate matrix of dissimilarities
mds <- cmdscale(dist(data_for_PCA))
#PCA plot for the 2 first dimensions
png(file="PCA_dim_1vs2.png")
plot(mds[,1], -mds[,2], type="n", xlim=c(-1.5e+05,1.5e+05), xlab="Dimension 1", ylab="Dimension 2",
main="PCA plot of the 2 principal dimensions")
text(mds[,1], -mds[,2], rownames(mds), cex=0.8)
dev.off()# load required libraries
library(edgeR)Loading required package: limma# read file with gene as rows and libraries counts as columns
file_counts <- "../../temp/test_bgeeCall/output/7227/present_counts.tsv"
present_counts <- read.table(file = file_counts, header = TRUE, sep = "\t")
genes <- rownames(present_counts)
#read file with library annotations
file_annotations <- "../../temp/test_bgeeCall/output/7227/library_annotations.tsv"
library_annotations <- read.table(file = file_annotations, header = TRUE, sep = "\t")
# create list of grouping parameters (here sex)
group <- NULL
for(i in seq(colnames(present_counts))){
group[i] <- as.character(library_annotations[library_annotations$Library.ID ==colnames(present_counts)[i],]$Sex)
}
# use group as names of the present_count data.frame
colnames(present_counts) <- group
# create the design matrix for limma
design_matrix <- model.matrix(~0+group)
colnames(design_matrix)<- gsub("group","",colnames(design_matrix))
# calculate the normalization factors between libraries
normalization_factors <- calcNormFactors(present_counts)
# use voom to calculate normalised read counts
voom_output <- voom(present_counts,design_matrix,lib.size=colSums(present_counts)*normalization_factors)
# extract the normalised read counts
normalised_counts <- voom_output$E
# fit linear model for each gene given a series of libraries
fit <- lmFit(voom_output, design_matrix)
# construct the contrast matrix corresponding to specified contrasts of a set of parameters
cont.matrix <- makeContrasts(male-female,levels=design_matrix)
# compute estimated coefficients and standard errors for a given set of contrasts
fit <- contrasts.fit(fit, cont.matrix)
# compute moderated t-statistics of differential expression by empirical Bayes moderation of the standard errors
fit <- eBayes(fit)
options(digits=3)
# use the topTable function to order genes and filter by p-value and log fold change
results <- topTable(fit, coef="male - female", number = nrow(present_counts), p.value = 0.05, lfc = 2)
#write differentialy expressed genes in a file
#write.table(x = results, file = "/cloud/project/output_files/dif_expressed_genes.tsv", row.names = TRUE, quote = FALSE, sep="\t")
write.table(x = results, file = "dif_expressed_genes.tsv", row.names = TRUE, quote = FALSE, sep="\t")
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] edgeR_3.28.1 limma_3.42.2 workflowr_1.6.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 knitr_1.28 whisker_0.4 magrittr_1.5
[5] lattice_0.20-41 R6_2.4.1 rlang_0.4.5 stringr_1.4.0
[9] tools_3.6.3 grid_3.6.3 xfun_0.12 git2r_0.26.1
[13] htmltools_0.4.0 yaml_2.2.1 digest_0.6.25 rprojroot_1.3-2
[17] later_1.0.0 promises_1.1.0 fs_1.3.2 glue_1.3.1
[21] evaluate_0.14 rmarkdown_2.1 stringi_1.4.6 compiler_3.6.3
[25] backports_1.1.5 locfit_1.5-9.4 httpuv_1.5.2