FilterNetwork

The weights of genes, could from the function <code>EstimateExpression</code>

weight

Filter the connected genes with weights less than <code>min.weight</code>

min.weight

min.conn.strength

The minimum number of connections required for a gene to be considered for trans-analysis

min.num.conn

max.num.conn

Logical, whether removing self-connections or not

remove.self.connection

Logical, specifying whether debug information should be printed

debug

A hierarchical Bayesian approach to assess functional impact of mutations on gene expression in cancer. Given a patient-gene matrix encoding the presence/absence of a mutation, a patient-gene expression matrix encoding continuous value expression data, and a graph structure encoding whether two genes are known to be functionally related, xseq outputs: a) the probability that a recurrently mutated gene g influences gene expression across the population of patients;
and b) the probability that an individual mutation in gene g in an individual patient m influences expression within that patient.

FilterNetwork: Filter network

Description

Usage

Arguments

Value

Examples