locfdr: Estimation of non-null density

In kdkorthauer/MADGiC: Fits an empirical Bayesian hierarchical model to obtain posterior probabilities that each gene is a driver.

Description

This function utilizes the algorithm for local false discovery rate (locfdr) estimation from Efron (2001) to estimate the non-null density of SIFT scores. Null scores are obtained from the simulated datasets created using shuffle.muts

Usage

  locfdr(nonsil.mutab, exome.SIFT, dir = "./simulated",
    N = 100)

Arguments

dir	a character string that contains the directory path to the simulated datasets
N	a numeric value that corresponds to how many simulated datasets are contained in the directory dir
nonsil.mutab	a matrix containing one row per nonsilent mutation and 8 columns (Ensembl gene name, chromosome, position, variant type (SNP, In_frame, Frame_shift), reference allele, tumor allele 1, tumor allele 2, and sample id.
exome.SIFT	list object with one item per chromosome where each item contains matrix with one row per coding base pair and 7 columns: position, nucleotide, CpG context, FI score for mutation to "A", FI score for mutation to "C", FI score for mutation to "G", and FI score for mutation to "T".

Value

a list object with three elements:

f0	numeric vector containing the estimated null density of FI scores over nbins bins
f1	numeric vector containing the estimated non-null density of FI scores over nbins bins
nbins	numeric value that represents how many bins were used for the spline regression

Note

This internal function is not intended to be called by the user.

kdkorthauer/MADGiC documentation built on June 13, 2020, 1:35 p.m.