Assessing Functional Impact on Gene Expression of Mutations in Cancer

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.


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0.2.1 by Jiarui Ding, 4 years ago

Browse source code at

Authors: Jiarui Ding , Sohrab Shah

Documentation:   PDF Manual  

GPL (>= 2) license

Imports e1071, gptk, impute, preprocessCore, RColorBrewer, sfsmisc

Suggests knitr

See at CRAN