get_specificity: Calculation and statistical assessment of synergistic...

In korkutlab/imogimap: What the Package Does (One Line, Title Case)

Calculation and statistical assessment of synergistic associations using TCGA data.

Description

A helper function to allow user calculate specifity pvalue for a subset of its dataset. To calculate specificity pvalue for all data points set specificity = TRUE in im_syng_TCGA.

Usage

get_specificity(
  df_syng,
  method = "max",
  ndatamin = 8,
  N_iteration_specificity = 10000
)

Arguments

df_syng	a dataframe with 10 columns as outputed by im_syng_tcga.
method	A character string indicating which synergy score to be used. one of "max" or "independence". Default is "max".
ndatamin	minimum number of samples. Synergy score calculation will be skipped for matrices with number of rows less than ndatamin
N_iteration_specificity	Number of iterations for random sampling for specificity p.value calculation. Default is 1000.

Details

A specificity p.value is computed using random sampling with replacement from two null models, generated from one of the two genes against a set of genes randomly selected from the genome. Two P-values are calculated for the synergistic interaction of the pair against the two null models. The highest of the two P-values is used to assess the specificity of the interaction against the whole genome. The number of randomly selected genes in each null model is determined by N_iteration_specificity.

Value

Specificty pvalues for each row of dataframe

Examples

df <- im_syng_tcga(onco_gene=c("TGFB1","SERPINB9"), cohort=c("ucec"),specificity = F)
df <- get_specificity(df)

korkutlab/imogimap documentation built on March 17, 2023, 8:22 a.m.