dcanr-package {dcanr} | R Documentation

## dcanr: Differential co-expression/association network analysis

### Description

Methods and an evaluation framework for the inference of differential co-expression/association networks.

### Details

There are three categories of functions available

1. Differential co-expression methods (DC) - These functions are used to perform a differential co-expression analysis on experimental data with binary conditions.

2. Functions to evaluate DC methods - These functions are used to evaluate methods implemented in the package and novel methods on simulated data. Expression data is simulated for 2 conditions, wild-type and knock-down of given genes.

3. By-products of implementations

### Differential co-expression methods (DC)

- `dcMethods`

- `dcScore`

- `dcTest`

- `dcAdjust`

- `dcNetwork`

### Functions to evaluate DC methods

Accessors of simulated data:

- `getConditionNames`

- `getSimData`

- `getTrueNetwork`

- `plotSimNetwork`

Functions for evaluating inference methods

- `dcPipeline`

- `dcEvaluate`

### By-products of implementations

These are functions used in the package but have further uses in general:

- `cor.pairs` - a faster implementation of pairwise correlation computation

- `mi.ap` - pairwise computation of mutual information MI with data discretisation performed using adaptive partitioning

- `perfMethods` - available performance metrics

- `performanceMeasure` - performance measures of prediction algorithms. Predictions have to be binary

### Author(s)

Maintainer: Dharmesh D. Bhuva bhuva.d@wehi.edu.au (0000-0002-6398-9157)

### See Also

Useful links:

- https://davislaboratory.github.io/dcanr/

- https://github.com/DavisLaboratory/dcanr

- Report bugs at https://github.com/DavisLaboratory/dcanr/issues

[Package dcanr version 1.2.0 Index]