datasets

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.

Jiarui Ding

xseq

Assessing Functional Impact on Gene Expression of Mutations in
Cancer

net function

A list with 16 elements:
<ul>
<li>List names are official HGNC gene symbols
  </li>
<li>Each element of the list is a vector, and the name of the vector is an official HGNC gene symbol.
  Each element of the vector is a real number between 0 and 1,
  representing the assocation strongth between two genes.
  The names of the elements of the vector are also official HGNC gene symbols.
</li>
</ul>

net: A networks containing gene associations

Description

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