README.Rmd

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

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

## Installation

Download the package from Bioconductor 

```{r getPackage, eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("dcanr")
```

Or install the development version of the package from Github.

```{r, eval = FALSE}
BiocManager::install("DavisLaboratory/dcanr")
```

Load the installed package into an R session.

```{r Load, message=FALSE}
library(dcanr)
```

## Example

This example shows how a differential network can be derived. Simulated data
within the package is used.

```{r example}
#load simulated data
data(sim102)

#get expression data and conditions for 'UME6' knock-down
simdata <- getSimData(sim102, cond.name = 'UME6', full = FALSE)
emat <- simdata$emat
ume6_kd <- simdata$condition

#apply the z-score method with Spearman correlations
z_scores <- dcScore(emat, ume6_kd, dc.method = 'zscore', cor.method = 'spearman')

#perform a statistical test: the z-test is selected automatically
raw_p <- dcTest(z_scores, emat, ume6_kd)

#adjust p-values (raw p-values from dcTest should NOT be modified)
adj_p <- dcAdjust(raw_p, f = p.adjust, method = 'fdr')

#get the differential network
dcnet <- dcNetwork(z_scores, adj_p)
plot(dcnet, vertex.label = '', main = 'Differential co-expression network')
```

Edges in the differential network are coloured based on the score (negative to
positive represented from purple to green respectively).