
A dataset containing the The Cancer Genome Atlas acute myeloid leukemia somatic mutation data


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

mut function

A data frame with 2311 rows and 12 variables:
<ul>
<li> sample. character, patient identifier
  </li>
<li> hgnc_symbol. character, official HGNC gene symbols
  </li>
<li> variant_type. character, mutation type, can be either "HOMD", "HLAMP",
  "MISSENSE", "NONSENSE", "FRAMESHIFT", "INFRAME", "SPLICE", "NONSTOP",
  "STARTGAINED", "SYNONYMOUS", "OTHER", "FUSION", "COMPLEX"
  </li>
<li> ...
</li>
</ul>

Format

<a href="https://www.synapse.org/#!Synapse:syn1729383">https://www.synapse.org/#!Synapse:syn1729383</a>

mut: TCGA AML somatic mutation data

Description

Usage

Arguments

Format

Source