findCoexpression: Seed coexpression
In palenic/genePrioritization: Find Potential Disease-Related Genes

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/findCoexpression.R

Computes coexpression of each gene in a gene expression matrix with each of the provided "seed" genes as the Pearson correlation. Used alternatively to prioritizeCandidates as first step of genePrioritization worflow.

1	findCoexpression(counts, seedGenes)

`counts`	A gene expression matrix (genes in rows, samples in columns): can be a count matrix, a microarray result, as long as it's numeric. Rows have to be named with the gene symbol or ID. If desired, normalization has to be previously performed be the user.
`seedGenes`	A vector or list of seed gene symbols or IDs. If a list, each gene has to be a list element. At least one seed gene has to be present in the counts matrix rownames. Missing or duplicated genes are removed.

A named matrix of shape (nGenes, nSeed) with correlation values between each seed and each gene in the matrix.

Chiara Paleni
Politecnico di Milano
Maintainer: Chiara Paleni
E-Mail: <chiara.paleni@polimi.it>

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935433/
Piro, Rosario M et al. “Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR.” Bioinformatics (Oxford, England) vol. 26,18 (2010): i618-24. doi:10.1093/bioinformatics/btq396

prioritizeCandidates, rankGenes, candidateScoring

a <- matrix(c(1,2,3,2,4,6,8,6,4,5,2,8,7,1,5),
nrow=5, ncol=3,byrow=TRUE)
colnames(a) <- c('sample1','sample2','sample3')
rownames(a) <- c('gene1','gene2','gene3','gene4','gene5')
seed <- c('gene1')
candidates <- c('gene2','gene4')
x <- findCoexpression(counts=a, seedGenes=seed)
y <- rankGenes(x)
z <- candidateScoring(y, candidates)