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.
Package details |
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Author | Jiarui Ding, Sohrab Shah |
Maintainer | Jiarui Ding <jiaruid@cs.ubc.ca> |
License | GPL (>= 2) |
Version | 0.2.1 |
Package repository | View on CRAN |
Installation |
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