candidateScoring: Candidate gene ranking
In palenic/genePrioritization: Find Potential Disease-Related Genes

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

View source: R/candidateScoring.R

Takes as input a ranked coexpression list between a set of genes and a set of seed genes, computes a score for each candidate gene as the product of their relative ranks (rank/(nGenes-1)), and finally ranks them according to their increasing score: lower scores indicate a higher coexpression, and thus a higher probability of being involved in the given phenotype. Rank(seed, seed) should be NA: if not, throws a warning. Used alternatively to prioritizeCandidates, after rankGenes as part of genePrioritization workflow.

1	candidateScoring(rankMatrix, candidateGenes=NULL)

`rankMatrix`	A rank matrix with shape (nGenes, nSeed). Both rows and columns have to be named with gene symbols or IDs. Rank(seed, seed) should be set to NA.
`candidateGenes`	(default NULL) A vector or list of candidate gene symbols or IDs. If a list, each gene has to be a list element. If NULL, all other genes in the matrix which are not part of the seed genes will be considered candidates. If supplied, least one candidate gene has to be present in the rank matrix rownames. Missing or duplicated genes are removed.

A named vector of scores for the candidates, sorted from lowest (most correlated to seed genes) to highest (least correlated).

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, rankGenes

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)