correlateGenes {scran} | R Documentation

## 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.

`limited`:

  Logical, indicates whether the combined p-value is at its lower bound.

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

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

[Package scran version 1.16.0 Index]