geneScores: Permutation t-Scores for Gene Expression
In annavesely/sumSome: True Discovery Guarantee by Sum-Based Tests

geneScores

R Documentation

Permutation t-Scores for Gene Expression

Description

This function computes t-scores for different permutations of gene expression data. A gene's score is calculated by performing the two-sample t test for the null hypothesis that the mean expression value is the same between two populations.

Usage

geneScores(expr, labels, alternative = "two.sided", alpha = 0.05, B = 200, seed = NULL,
           truncFrom = 3.2, truncTo = 0, squares = FALSE)

Arguments

`expr`	matrix where rows correspond to genes, and columns to samples.
`labels`	numeric/character vector with two levels, denoting the population of each sample.
`alternative`	direction of the alternative hypothesis (`greater`, `lower`, `two.sided`).
`alpha`	significance level.
`B`	number of permutations, including the identity.
`seed`	seed.
`truncFrom`	truncation parameter: values less extreme than `truncFrom` are truncated. If `NULL`, statistics are not truncated.
`truncTo`	truncation parameter: truncated values are set to `truncTo`. If `NULL`, statistics are not truncated.
`squares`	logical, `TRUE` to use squared t-scores.

Details

Truncation parameters should be such that truncTo is not more extreme than truncFrom.

The significance level alpha should be in the interval [1/B, 1).

Value

geneScores returns an object of class sumGene, containing

statistics: numeric matrix of scores, where columns correspond to genes, and rows to permutations. The first permutation is the identity
alpha: significance level
truncFrom: transformed first truncation parameter
truncTo: transformed second truncation parameter

Author(s)

Anna Vesely.

References

Goeman J. J. and Solari A. (2011). Multiple testing for exploratory research. Statistical Science, doi: 10.1214/11-STS356.

Vesely A., Finos L., and Goeman J. J. (2023). Permutation-based true discovery guarantee by sum tests. Journal of the Royal Statistical Society, Series B (Statistical Methodology), doi: 10.1093/jrsssb/qkad019.

Examples

# simulate 20 samples of 100 genes
set.seed(42)
expr <- matrix(c(rnorm(1000, mean = 0, sd = 10), rnorm(1000, mean = 13, sd = 10)), ncol = 20)
rownames(expr) <- seq(100)
labels <- rep(c(1,2), each = 10)

# simulate pathways
pathways <- lapply(seq(3), FUN = function(x) sample(rownames(expr), 3*x))

# create object of class sumGene
res <- geneScores(expr = expr, labels = labels, alpha = 0.2, seed = 42)
res
summary(res)

# confidence bound for the number of true discoveries and the TDP within pathways
out <- geneAnalysis(res, pathways = pathways)
out

annavesely/sumSome documentation built on Jan. 28, 2025, 8:15 a.m.