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.
|Author||Jiarui Ding, Sohrab Shah|
|Date of publication||2015-09-11 08:04:31|
|Maintainer||Jiarui Ding <email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.