GetExpressionDistribution

A matrix of gene expression values where
each row corresponds to a patient and each column is a gene.

expr

A data.frame of mutations. The data.frame should have
three columns of characters: sample, hgnc_symbol, and variant_type.
The variant_type column cat be either "HOMD", "HLAMP",
  "MISSENSE", "NONSENSE", "FRAMESHIFT", "INFRAME", "SPLICE", "NONSTOP",
  "STARTGAINED", "SYNONYMOUS", "OTHER", "FUSION", "COMPLEX".

A matrix containing the copy number calls, where each element is coded:
<ul>
<li> -2, homozygous deletions
  </li>
<li> -1, hemizygous deletions
  </li>
<li> 0, neutral
  </li>
<li> 1, gain
  </li>
<li> 2, amplifications
  </li>
</ul>


cna.call

A character vector of official HGNC gene names

gene

Character, either Gaussian  ("gauss") or
Student ("student")

type

Logical, specifying whether to plot the fitted model

show.plot


Get the conditional distributions for a set of genes


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.

GetExpressionDistribution: Get the conditional distributions for a set of genes

Description

Usage

Arguments

Value