get_effect_size: Get effect sizes and classify instances of conditional...

In ggruenhagen3/coselens: COnditional SELection on the Excess of NonSynonymous Substitutions

Get effect sizes and classify instances of conditional selection

Description

Get effect sizes and classify instances of conditional selection

Usage

get_effect_size(coselens_full, mutation.class = "sub")

Arguments

coselens_full:	full results table produced by coselens (coselens_out$full)
mutation.class:	class of mutations for which effect sizes will be calculated. Options: "sub" (all coding substitutions, default), "ind" (indels), "mis" (missense substitutions), "trunc" (truncating substitutions, including nonsense and essential splice site substitutions), "global" (combination of coding substitutions and indels, using Fisher's combined test for p-values)

Value

dataframe with rows representing genes and the following columns

• gene_name: name of the gene

• num.driver.group1: estimate of the number of drivers per sample per gene in group 1

• num.driver.group2: estimate of the number of drivers per sample per gene in group 2

• Delta.Nd: absolute difference in the average number of driver mutations per sample (group 1 minus group 2)

• classification: classification of conditional selection. The most frequent classes are strict dependence (drivers only in group 1), facilitation (drivers more frequent in group 1), independence, inhibition (drivers less frequent in group 1), and strict inhibition (drivers absent from group 1). If negative selection is present, other possibilities are strict dependence with sign change (drivers positively selected in group 1 but negatively selected in group 2), strict inhibition with sign change (drivers positively selected in group 2 but negatively selected in group 1), aggravation (purifying selection against mutations becomes stronger in group 1), and relaxation (purifying selection against mutations becomes weaker in group 1).

• dependency: dependency index, measuring the association between the grouping variable (group 1 or 2) and the average number of drivers observed in a gene. It serves as a quantitative measure of the qualitative effect described in "classification". In the most common cases, a value of 1 indicates strict dependence or inhibition (drivers only observed in one group) and a value of 0 (or NA) indicates independence.

• pval: p-value for conditional selection

• qval: q-value for conditional selection using Benjamini-Hochberg correction of false discovery rate.

ggruenhagen3/coselens documentation built on April 17, 2025, 11:56 a.m.