Downstream analysis
Julien Wollbrett
Swiss Institut of Bioinformatics (SIB), Université de Lausannebgee[at]sib.swiss
2020-04-22
Last updated: 2020-04-28
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Knit directory: BgeeCall_practical/
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Introduction
Once BgeeCall generated gene experssion calls it is possible to use them to do downstream analysis. This section contains R code examples of potential analysis to run on present genes.
PCA
# read file with gene as rows and libraries as columns
file <- "PATH_TO_PRESENT_TPMS_FILE"
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
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")
#PCA plot for the 2 first dimensions
#plotMDS(present_TPMs,xlab = "Dimension 1")
plot(mds$points[,1], -mds$points[,2], type="n", xlim=c(-2.5e+05,2.5e+05), xlab="Dimension 1", ylab="Dimension 2",
main="PCA plot of the 2 principal dimensions", )
text(mds$points[,1], -mds$points[,2], rownames(mds$points), cex=0.8)
Diffrential expression
library(edgeR)Loading required package: limma# read file with gene as rows and libraries counts as columns
file_counts <- "PATH_TO_PRESENT_COUNTS_FILE"
present_counts <- read.table(file = file_counts, header = TRUE, sep = "\t")
#read file with library annotations
file_annotations <- "PATH_TO_LIBRARY_ANNOTATIONS_FILE"
library_annotations <- read.table(file = file_annotations, header = TRUE, sep = "\t")# create list of grouping parameters (here sex)
group <- NULL
for(i in seq(colnames(present_counts))){
group[i] <- as.character(library_annotations[library_annotations$Library.ID ==colnames(present_counts)[i],]$Sex)
}
# create DGEList object
dge <- DGEList(present_counts, group=group)
#calculate normalization factor between libraries
dge <- calcNormFactors(dge)
#estimate common and tag wise dispersion
dge <- estimateCommonDisp(dge)
dge <- estimateTagwiseDisp(dge)
#perform an exact test for the difference in expression male-female
dgeTest <- exactTest(dge)
# retrieve all gene differentially expressed with a p-value lower than 0.05
topTags <- topTags(dgeTest, n=nrow(dgeTest$table), p.value = 0.05)
#filter for FDR < 0.05
results <- topTags$table[topTags$table$FDR<0.05,]
write.table(x = results, file = "dif_expressed_genes.tsv", row.names = TRUE, quote = FALSE, sep="\t")
# generate an MA plot with 1% differentially expressed genes
plotSmear(dgeTest, de.tags = rownames(topTags$table)[which(topTags$table$FDR<0.01)],)
# generate a volcano plot
volcanoData <- cbind(topTags$table$logFC, -log10(topTags$table$PValue))
colnames(volcanoData) <- c("logFC", "negLogPval")
plot(volcanoData, pch=19)
GO analysis
library(biomaRt)
## topGO function of edgeR require entrez IDs. We will use biomaRt to map ensembl Ids to entrez Ids
#query biomaRt
mart <- useDataset("dmelanogaster_gene_ensembl", useMart("ensembl"))
entrez_mapping <- getBM(attributes=c("ensembl_gene_id", "entrezgene_id"), mart=mart, useCache = FALSE)
#update row names of the DGEExact object from edgeR
table <- merge(dgeTest$table, entrez_mapping, by.x="row.names", by.y="ensembl_gene_id", all.x = TRUE)
#looks like mapping from biomaRt has some problems of redundancy. Need hack to remove redundancy (not good practice)
table <- na.omit(table[!duplicated(table[,c('entrezgene_id')]),])
rownames(table) <- table$entrezgene_id
dgeTest$table <- table[,c('logFC','logCPM','PValue')]
#run the GO analysis
go <- goana(dgeTest, species = "Dm")
topGO(go) Term Ont N Up Down P.Up
GO:0022626 cytosolic ribosome CC 91 0 86 1.0000000
GO:0002181 cytoplasmic translation BP 117 0 94 1.0000000
GO:0042254 ribosome biogenesis BP 191 3 123 1.0000000
GO:0022613 ribonucleoprotein complex biogenesis BP 268 6 138 1.0000000
GO:0044445 cytosolic part CC 143 6 93 0.9999738
GO:0022625 cytosolic large ribosomal subunit CC 52 0 51 1.0000000
GO:0006364 rRNA processing BP 124 3 80 0.9999985
GO:0005840 ribosome CC 174 4 96 1.0000000
GO:0016072 rRNA metabolic process BP 142 4 85 0.9999990
GO:0044391 ribosomal subunit CC 165 3 92 1.0000000
GO:0006412 translation BP 367 9 151 1.0000000
GO:0043604 amide biosynthetic process BP 425 16 166 1.0000000
GO:0043043 peptide biosynthetic process BP 401 12 159 1.0000000
GO:1990904 ribonucleoprotein complex CC 556 26 197 1.0000000
GO:0005730 nucleolus CC 185 6 95 0.9999999
GO:0043603 cellular amide metabolic process BP 526 30 186 1.0000000
GO:0003735 structural constituent of ribosome MF 158 4 86 0.9999999
GO:0006518 peptide metabolic process BP 464 23 168 1.0000000
GO:0042273 ribosomal large subunit biogenesis BP 51 0 43 1.0000000
GO:0022627 cytosolic small ribosomal subunit CC 38 0 36 1.0000000
P.Down
GO:0022626 1.966044e-62
GO:0002181 9.423063e-54
GO:0042254 4.884838e-52
GO:0022613 3.330418e-42
GO:0044445 5.653007e-40
GO:0022625 9.007390e-40
GO:0006364 4.019224e-34
GO:0005840 1.061884e-32
GO:0016072 1.223935e-32
GO:0044391 7.403915e-32
GO:0006412 1.165862e-31
GO:0043604 1.531185e-31
GO:0043043 4.264480e-31
GO:1990904 1.069313e-30
GO:0005730 4.486529e-29
GO:0043603 7.974862e-29
GO:0003735 8.356819e-29
GO:0006518 2.293887e-27
GO:0042273 9.169436e-27
GO:0022627 1.126329e-26
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] biomaRt_2.42.0 edgeR_3.28.1 limma_3.42.2 workflowr_1.6.1
loaded via a namespace (and not attached):
[1] progress_1.2.2 locfit_1.5-9.4 tidyselect_1.0.0
[4] xfun_0.12 purrr_0.3.3 lattice_0.20-41
[7] vctrs_0.2.3 htmltools_0.4.0 stats4_3.6.3
[10] BiocFileCache_1.10.2 yaml_2.2.1 blob_1.2.1
[13] XML_3.99-0.3 rlang_0.4.5 later_1.0.0
[16] pillar_1.4.3 glue_1.3.1 DBI_1.1.0
[19] rappdirs_0.3.1 BiocGenerics_0.32.0 bit64_0.9-7
[22] dbplyr_1.4.2 stringr_1.4.0 memoise_1.1.0
[25] evaluate_0.14 Biobase_2.46.0 knitr_1.28
[28] IRanges_2.20.2 httpuv_1.5.2 curl_4.3
[31] parallel_3.6.3 AnnotationDbi_1.48.0 Rcpp_1.0.3
[34] openssl_1.4.1 promises_1.1.0 backports_1.1.5
[37] S4Vectors_0.24.3 fs_1.3.2 bit_1.1-15.2
[40] hms_0.5.3 askpass_1.1 digest_0.6.25
[43] stringi_1.4.6 dplyr_0.8.4 grid_3.6.3
[46] rprojroot_1.3-2 tools_3.6.3 magrittr_1.5
[49] RSQLite_2.2.0 tibble_2.1.3 GO.db_3.10.0
[52] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
[55] prettyunits_1.1.1 org.Dm.eg.db_3.10.0 assertthat_0.2.1
[58] rmarkdown_2.1 httr_1.4.1 R6_2.4.1
[61] git2r_0.26.1 compiler_3.6.3