# DGCA v1.0.1

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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 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 up-and-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 | |

dcTopPairs | Creates a data frame for the top differentially correlated gene pairs in your data set. | |

matCorSig | Calculate correlation matrix p-values. | |

ddcorAll | Calls the DGCA pairwise pipeline. | |

ddMEGENA | Integration function to use MEGENA to perform network analyses of DGCA results. | |

ddplot | Create a heatmap showing the correlations in two conditions. | |

pairwiseDCor | Calculate pairwise differential correlations. | |

moduleGO | Perform module GO-trait correlation | |

matCorr | Calculate a correlation matrix. | |

ddcorFindSignificant | Find groups of differentially correlated gene symbols. | |

ddcorGO | Gene ontology of differential correlation-classified genes. | |

adjustPVals | Adjusts a numeric vector of p-values. | |

ages_darmanis | Brain sample ages vector. | |

getGroupsFromDesign | Split input matrix(es) based on the design matrix. | |

makeDesign | Create a design matrix from a character vector. | |

switchGenesToHGCN | Switches a gene vector to cleaned HGNC symbols. | |

topDCGenes | Ranks genes by their total number of differentially correlated gene pairs. | |

dCorMats | Finds differential correlations between matrices. | |

dCorrs | Differential correlation between two conditions. | |

extractModuleGO | Extract results from the module GO analysis | |

filterGenes | Filter rows out of a matrix. | |

findGOTermEnrichment | Find GO enrichment for a gene vector (using GOstats). | |

getCors | Compute matrices necessary for differential correlation calculation. | |

permQValue | Calculate q-values from DGCA class objects based on permutation-based empirical null statistics. | |

plotCors | Plot gene pair correlations in multiple conditions. | |

plotModuleGO | Plot extracted results from module-based GO enrichment analysis using ggplot2. | |

plotVals | Creates a dotplot of the overall values for an individual gene in multiple conditions. | |

matNSamp | Find the number of non-missing values. | |

moduleDC | Calculate modular differential connectivity (MDC) | |

plotGOOneGroup | Plot results from a hypergeometric enrichment test for one condition. | |

plotGOTwoGroups | Plot results from a hypergeometric enrichment test to compare two conditions. | |

getDCorPerm | Get permuted groupwise correlations and pairwise differential correlations. | |

getDCors | Get groupwise correlations and pairwise differential correlations. | |

dCorAvg | Get average empirical differential correlations. | |

dCorClass | Classify differential correlations. | |

design_mat | Design matrix of cell type specifications of the single-cell RNA-seq samples. | |

DGCA | DGCA: An R package for Differential Gene Correlation Analysis | |

darmanis | Single-cell gene expression data from different brain cell types. | |

bigEmpPVals | Use speed-optimized sorting to calculate p-values observed and simulated null test statistic using a reference pool distribution. | |

No Results! |

## Vignettes of DGCA

Name | ||

DGCA_basic.Rmd | ||

No Results! |

## Last month downloads

## Details

VignetteBuilder | knitr |

RoxygenNote | 5.0.1 |

License | GPL-3 |

LazyData | true |

NeedsCompilation | no |

Packaged | 2016-11-17 15:51:18 UTC; amckenz |

Repository | CRAN |

Date/Publication | 2016-11-17 18:33:47 |

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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