VAMForCollection: VAM method for multiple gene sets

In VAM: Variance-Adjusted Mahalanobis

VAM method for multiple gene sets

Description

Executes the Variance-adjusted Mahalanobis (VAM) method (vam) on multiple gene sets, i.e., a gene set collection.

Usage

    vamForCollection(gene.expr, gene.set.collection, tech.var.prop, 
        gene.weights, center=FALSE, gamma=TRUE)

Arguments

gene.expr	An n x p matrix of gene expression values for n cells and p genes.
gene.set.collection	List of m gene sets for which scores are computed. Each element in the list corresponds to a gene set and the list element is a vector of indices for the genes in the set. The index value is defined relative to the order of genes in the gene.expr matrix. Gene set names should be specified as list names.
tech.var.prop	See description in vam
gene.weights	See description in vam. If specified as a single vector of weights, weights must be specified for all p genes and the same weights are used for all gene sets. To use different weights for each set, specify as a list of the same length as the gene.set.collection list. In this case, each list element should be a vector of gene weights of the same length as the size of the corresponding gene set.
center	See description in vam
gamma	See description in vam

Value

A list containing two elements:

• "cdf.value": n x m matrix of 1 minus the one-sided p-values for the m gene sets and n cells.

• "distance.sq": n x m matrix of squared adjusted Mahalanobis distances for the m gene sets and n cells.

See Also

vam,vamForSeurat

Examples

    # Simulate Poisson expression data for 10 genes and 10 cells
    gene.expr=matrix(rpois(100, lambda=2), nrow=10)
    # Simulate technical variance proportions
    tech.var.prop=runif(10)
    # Define a collection with two disjoint sets that span the 10 genes
    collection=list(set1=1:5, set2=6:10)    
    # Execute VAM on both sets using default values for center and gamma
    vamForCollection(gene.expr=gene.expr, gene.set.collection=collection,
        tech.var.prop=tech.var.prop)
    # Create weights that prioritize the first 2 genes for the first set 
    # and the last 2 genes for the second set
    gene.weights = list(c(2,2,1,1,1),c(1,1,1,2,2))
    # Execute VAM using the weights
    vamForCollection(gene.expr=gene.expr, gene.set.collection=collection,
        tech.var.prop=tech.var.prop, gene.weights=gene.weights)

VAM documentation built on Nov. 5, 2023, 5:06 p.m.