In this vignette, we will analyze a gene expression dataset with samples from multiple tissues. We will: download a public dataset identify the genes expressed in two tissues run enrichment analysis, cognizant of each tissues’ expression profile visualize network-based relationships between the tissues’ expression profiles
We will use data from BgeeDB normal-tissue expression. In research, we will typically want to compare normal to one or more treatment or disease groups. Thus, consider this as an illustrative example.
library(RITANdata)
library(RITAN)
library(BgeeDB)
bgee <- Bgee$new(species = "Homo_sapiens", dataType = "rna_seq", release = "13.2")
data <- getData(bgee)
e <- formatData(bgee, data[[1]], callType = "present", stats = "rpkm")
# str(sampleNames(e))
# str(featureNames(e))
# str(phenoData(e))
# table(phenoData(e)@data$Anatomical.entity.name)
## -------------------- -
## Get expression in two tissues
tmp <- exprs(e)[ , phenoData(e)@data$Anatomical.entity.name == "heart" ]
i <- apply( tmp, 1, function(x){ any(is.na(x)) })
expr_heart <- tmp[ !i, ]
tmp <- exprs(e)[ , phenoData(e)@data$Anatomical.entity.name == "skeletal muscle tissue" ]
i <- apply( tmp, 1, function(x){ any(is.na(x)) })
expr_skele <- tmp[ !i, ]
library(venn)
venn::venn( list(Heart = rownames(expr_heart),
Skeletal = rownames(expr_skele) ),
cexil= 1, cexsn = 1, zcolor = "style" )
## -------------------- -
library(biomaRt)
ensembl <- useMart("ensembl", dataset = "hsapiens_gene_ensembl" )
map_heart <- getBM( attributes=c('ensembl_gene_id','ensembl_transcript_id','hgnc_symbol'),
filters = 'ensembl_gene_id', values = rownames(expr_heart), mart = ensembl )
map_skele <- getBM( attributes=c('ensembl_gene_id','ensembl_transcript_id','hgnc_symbol'),
filters = 'ensembl_gene_id', values = rownames(expr_skele), mart = ensembl )
## -------------------- -
## Enrichment Within Each Tissue
to do...
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## Network Interactions Within Each Tissue
to do...
## -------------------- -
## Similarities and Differences Between Tissues
to do...