correlateGenes: Per-gene correlation statistics

In MarioniLab/scran: Methods for Single-Cell RNA-Seq Data Analysis

Per-gene correlation statistics

Description

Compute per-gene correlation statistics by combining results from gene pair correlations.

Usage

correlateGenes(stats)

Arguments

stats

A DataFrame of pairwise correlation statistics, returned by correlatePairs.

Details

For each gene, all of its pairs are identified and the corresponding p-values are combined using Simes' method. This tests whether the gene is involved in significant correlations to any other gene. Per-gene statistics are useful for identifying correlated genes without regard to what they are correlated with (e.g., during feature selection).

Value

A DataFrame with one row per unique gene in stats and containing the fields:

gene:

A field of the same type as stats$gene1 specifying the gene identity.

rho:

Numeric, the correlation with the largest magnitude across all gene pairs involving the corresponding gene.

p.value:

Numeric, the Simes p-value for this gene.

FDR:

Numeric, the adjusted p.value across all rows.

Author(s)

Aaron Lun

References

Simes RJ (1986). An improved Bonferroni procedure for multiple tests of significance. Biometrika 73:751-754.

See Also

correlatePairs, to compute stats.

Examples

library(scuttle)
sce <- mockSCE()
sce <- logNormCounts(sce)
pairs <- correlatePairs(sce, iters=1e5, subset.row=1:100)

g.out <- correlateGenes(pairs)
head(g.out)

MarioniLab/scran documentation built on Sept. 7, 2024, 6:25 a.m.