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.
| Version: | 0.2.1 |
| Depends: | R (≥ 3.1.0) |
| Imports: | e1071 (≥ 1.6-4), gptk (≥ 1.08), impute (≥ 1.38.1), preprocessCore (≥ 1.26.1), RColorBrewer (≥ 1.1-2), sfsmisc (≥ 1.0-27) |
| Suggests: | knitr |
| Published: | 2015-09-11 |
| Author: | Jiarui Ding, Sohrab Shah |
| Maintainer: | Jiarui Ding <jiaruid at cs.ubc.ca> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | xseq results |
| Reference manual: | xseq.pdf |
| Vignettes: |
xseq – Assessing Functional Impact on Gene Expression of Mutations in Cancer |
| Package source: | xseq_0.2.1.tar.gz |
| Windows binaries: | r-devel: xseq_0.2.1.zip, r-release: xseq_0.2.1.zip, r-oldrel: xseq_0.2.1.zip |
| macOS binaries: | r-release: xseq_0.2.1.tgz, r-oldrel: xseq_0.2.1.tgz |
| Old sources: | xseq archive |
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