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

View source: R/correlateGenes.R

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

1 |

`stats` |
A DataFrame of pairwise correlation statistics, 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 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.

Aaron Lun

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

`correlatePairs`

, to compute `stats`

.

1 2 3 4 5 6 7 | ```
library(scuttle)
sce <- mockSCE()
sce <- logNormCounts(sce)
pairs <- correlatePairs(sce, iters=1e5, subset.row=1:100)
g.out <- correlateGenes(pairs)
head(g.out)
``` |

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