DGCA: Differential Gene Correlation Analysis
Performs differential correlation analysis on input
matrices, with multiple conditions specified by a design matrix. Contains
functions to filter, process, save, visualize, and interpret differential
correlations of identifier-pairs across the entire identifier space, or with
respect to a particular set of identifiers (e.g., one). Also contains several
functions to perform differential correlation analysis on clusters (i.e., modules)
or genes. Finally, it contains functions to generate empirical p-values for the
hypothesis tests and adjust them for multiple comparisons. Although the package
was built with gene expression data in mind, it is applicable to other types of
genomics data as well, in addition to being potentially applicable to data from
other fields entirely. It is described more fully in the manuscript
introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.
| Version: |
1.0.2 |
| Depends: |
R (≥ 3.2) |
| Imports: |
WGCNA, matrixStats, methods |
| Suggests: |
knitr, impute, gplots, fdrtool, testthat, ggplot2, plotrix, GOstats, HGNChelper, org.Hs.eg.db, AnnotationDbi, abind, MEGENA, Matrix, doMC, igraph, cowplot, stats, utils |
| Published: |
2019-12-09 |
| Author: |
Bin Zhang [aut],
Andrew McKenzie [aut, cre] |
| Maintainer: |
Andrew McKenzie <amckenz at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
DGCA results |
Documentation:
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