dcanr-package: dcanr: Differential co-expression/association network...

In 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 [e-mail] (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

dcanr documentation built on Nov. 8, 2020, 5:48 p.m.