rankGenes: Potential disease gene ranking
In palenic/genePrioritization: Find Potential Disease-Related Genes

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

View source: R/rankGenes.R

Ranks genes in a coexpression matrix according to their coexpression with known disease-related "seed" genes. Takes as input a correlation matrix with shape (nGenes, nSeed) and computes column-wise ranks according to decreasing coexpression, such that each seed gene has a separate ranked coexpression list and highest coexpression corresponds to rank 1. Note that the coexpression between each seed and itself is set to NA, so all other genes but the seed are ranked. Used alternatively to prioritizeCandidates, after findCoexpression as part of genePrioritization workflow.

1	rankGenes(corrMatrix, antiCorrelation=FALSE)

`corrMatrix`	A correlation matrix with the seed genes as the columns and all genes as the rows. Both rows and columns have to be named with gene symbols or IDs.
`antiCorrelation`	A logical value (default FALSE). If TRUE, anti-correlation will be considered as a significant correlation, thus strongly anti-correlated genes will have a high rank.

A named matrix of shape (nGenes, nSeed) with the rank of each gene in each seed gene coexpression list. Genes are ranked according to their decreasing coexpression. Rank(seed, seed) is set to NA.

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, findCoexpression, 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)