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>.
- Bin Zhang [aut], Andrew McKenzie [aut, cre]
- Date of publication
- 2016-11-17 18:33:47
- Andrew McKenzie <firstname.lastname@example.org>
- Adjusts a numeric vector of p-values.
- Brain sample ages vector.
- Use speed-optimized sorting to calculate p-values observed...
- An S4 class to store correlation matrices and associated...
- Single-cell gene expression data from different brain cell...
- Get average empirical differential correlations.
- Classify differential correlations.
- Finds differential correlations between matrices.
- Differential correlation between two conditions.
- S4 class for pairwise differential correlation matrices and...
- Creates a data frame for the top differentially correlated...
- Calls the DGCA pairwise pipeline.
- Find groups of differentially correlated gene symbols.
- Gene ontology of differential correlation-classified genes.
- Integration function to use MEGENA to perform network...
- Create a heatmap showing the correlations in two conditions.
- Design matrix of cell type specifications of the single-cell...
- DGCA: An R package for Differential Gene Correlation Analysis
- Extract results from the module GO analysis
- Filter rows out of a matrix.
- Find GO enrichment for a gene vector (using GOstats).
- Compute matrices necessary for differential correlation...
- Get permuted groupwise correlations and pairwise differential...
- Get groupwise correlations and pairwise differential...
- Split input matrix(es) based on the design matrix.
- Create a design matrix from a character vector.
- Calculate a correlation matrix.
- Calculate correlation matrix p-values.
- Find the number of non-missing values.
- Calculate modular differential connectivity (MDC)
- Perform module GO-trait correlation
- Calculate pairwise differential correlations.
- Calculate q-values from DGCA class objects based on...
- Plot gene pair correlations in multiple conditions.
- Plot results from a hypergeometric enrichment test for one...
- Plot results from a hypergeometric enrichment test to compare...
- Plot extracted results from module-based GO enrichment...
- Creates a dotplot of the overall values for an individual...
- Switches a gene vector to cleaned HGNC symbols.
- Ranks genes by their total number of differentially...
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