DGCA v1.0.2
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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>.
Readme
DGCA
The goal of DGCA is to calculate differential correlations across conditions.
It simplifies the process of seeing whether two correlations are different without having to rely solely on parametric assumptions by leveraging non-parametric permutation tests and adjusting the resulting empirical p-values for multiple corrections using the qvalue R package.
It also has several other options including calculating the average differential correlation between groups of genes, gene ontology enrichment analyses of the results, and differential correlation network identification via integration with MEGENA.
Installation
You can install DGCA from CRAN with:
install.packages("DGCA")
You can install the development version of DGCA from github with:
# install.packages("devtools")
devtools::install_github("andymckenzie/DGCA")
Basic Example
library(DGCA)
data(darmanis); data(design_mat)
ddcor_res = ddcorAll(inputMat = darmanis, design = design_mat, compare = c("oligodendrocyte", "neuron"))
head(ddcor_res, 3)
# Gene1 Gene2 oligodendrocyte_cor oligodendrocyte_pVal neuron_cor neuron_pVal
# 1 CACYBP NACA -0.070261455 0.67509118 0.9567267 0
# 2 CACYBP SSB -0.055290516 0.74162636 0.9578999 0
# 3 NDUFB9 SSB -0.009668455 0.95405875 0.9491904 0
# zScoreDiff pValDiff empPVals pValDiff_adj Classes
# 1 10.256977 1.100991e-24 1.040991e-05 0.6404514 0/+
# 2 10.251847 1.161031e-24 1.040991e-05 0.6404514 0/+
# 3 9.515191 1.813802e-21 2.265685e-05 0.6404514 0/+
Vignettes
There are three vignettes available in order to help you learn how to use the package:
- DGCA Basic: This will get you going quickly.
- DGCA: This is a more extended version that explains a bit about how the package works and shows several of the options available in the package.
- DGCA Modules: This will show you how to use the package to perform module-based and network-based analyses.
The second two vignettes can be found in inst/doc.
Applications
You can view the manuscript describing DGCA in detail as well as several applications here:
Material for associated simulations and networks created from MEGENA can be found here:
Functions in DGCA
| Name | Description | |
| extractModuleGO | Extract results from the module GO analysis | |
| makeDesign | Create a design matrix from a character vector. | |
| dCorAvg | Get average empirical differential correlations. | |
| ddcorAll | Calls the DGCA pairwise pipeline. | |
| getGroupsFromDesign | Split input matrix(es) based on the design matrix. | |
| plotModuleGO | Plot extracted results from module-based GO enrichment analysis using ggplot2. | |
| corMats-class | An S4 class to store correlation matrices and associated info. | |
| getCors | Compute matrices necessary for differential correlation calculation. | |
| ddMEGENA | Integration function to use MEGENA to perform network analyses of DGCA results. | |
| findGOTermEnrichment | Find GO enrichment for a gene vector (using GOstats). | |
| ages_darmanis | Brain sample ages vector. | |
| moduleGO | Perform module GO-trait correlation | |
| pairwiseDCor | Calculate pairwise differential correlations. | |
| plotGOTwoGroups | Plot results from a hypergeometric enrichment test to compare two conditions. | |
| matCorSig | Calculate correlation matrix p-values. | |
| matCorr | Calculate a correlation matrix. | |
| plotGOOneGroup | Plot results from a hypergeometric enrichment test for one condition. | |
| dcTopPairs | Creates a data frame for the top differentially correlated gene pairs in your data set. | |
| bigEmpPVals | Use speed-optimized sorting to calculate p-values observed and simulated null test statistic using a reference pool distribution. | |
| ddplot | Create a heatmap showing the correlations in two conditions. | |
| design_mat | Design matrix of cell type specifications of the single-cell RNA-seq samples. | |
| filterGenes | Filter rows out of a matrix. | |
| getDCorPerm | Get permuted groupwise correlations and pairwise differential correlations. | |
| getDCors | Get groupwise correlations and pairwise differential correlations. | |
| plotVals | Creates a dotplot of the overall values for an individual gene in multiple conditions. | |
| switchGenesToHGCN | Switches a gene vector to cleaned HGNC symbols. | |
| matNSamp | Find the number of non-missing values. | |
| permQValue | Calculate q-values from DGCA class objects based on permutation-based empirical null statistics. | |
| moduleDC | Calculate modular differential connectivity (MDC) | |
| plotCors | Plot gene pair correlations in multiple conditions. | |
| topDCGenes | Ranks genes by their total number of differentially correlated gene pairs. | |
| adjustPVals | Adjusts a numeric vector of p-values. | |
| dCorMats | Finds differential correlations between matrices. | |
| dCorrs | Differential correlation between two conditions. | |
| DGCA | DGCA: An R package for Differential Gene Correlation Analysis | |
| dCorClass | Classify differential correlations. | |
| darmanis | Single-cell gene expression data from different brain cell types. | |
| dcPair-class | S4 class for pairwise differential correlation matrices and associated info. | |
| ddcorFindSignificant | Find groups of differentially correlated gene symbols. | |
| ddcorGO | Gene ontology of differential correlation-classified genes. | |
| No Results! | ||
Vignettes of DGCA
| Name | ||
| DGCA_basic.Rmd | ||
| No Results! | ||
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Details
| VignetteBuilder | knitr |
| RoxygenNote | 5.0.1 |
| License | GPL-3 |
| LazyData | true |
| NeedsCompilation | no |
| Packaged | 2019-12-09 02:23:17 UTC; mckena01 |
| Repository | CRAN |
| Date/Publication | 2019-12-09 10:30:16 UTC |
| suggests | abind , AnnotationDbi , cowplot , doMC , fdrtool , ggplot2 , GOstats , gplots , HGNChelper , igraph , impute , knitr , Matrix , MEGENA , org.Hs.eg.db , plotrix , stats , testthat , utils |
| imports | matrixStats , methods , WGCNA |
| depends | R (>= 3.2) |
| Contributors | Bin Zhang, Andrew McKenzie |
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