correlateGenes {scran} | R Documentation |
Compute per-gene correlation statistics by combining results from gene pair correlations.
correlateGenes(stats)
stats |
A DataFrame returned by |
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).
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 identity of the gene corresponding to each row.
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.
limited
:Logical, indicates whether the combined p-value is at its lower bound.
Aaron Lun
Simes RJ (1986). An improved Bonferroni procedure for multiple tests of significance. Biometrika 73:751-754.
correlatePairs
to compute stats
.
set.seed(0) ncells <- 100 null.dist <- correlateNull(ncells, iters=100000) exprs <- matrix(rpois(ncells*100, lambda=10), ncol=ncells) out <- correlatePairs(exprs, null.dist=null.dist) g.out <- correlateGenes(out) head(g.out)