tni.plot.checks {RTN} | R Documentation

## Plot regulon target counts.

### Description

This funtion can help to check whether the numbers of positive and negative targets are reasonably well balanced in the regulons.

### Usage

    tni.plot.checks(object, minRegulonSize = 15, option = c("barplot","edf","points"))

### Arguments

`object` | a preprocessed object of class 'TNI' `TNI-class` already evaluated by the `tni.dpi.filter` method.
`minRegulonSize` | a single integer or numeric value specifying the minimum number of elements in a regulon (only affects the 'barplot' option).
`option` | plot option.

### Value

A plot showing the distribution of regulons' positive and negative targets.

### Note

We have observed that transcription factor (TF) regulons reconstructed from RTN exhibit different proportions of positive and negative targets. While the proportion can vary between different regulons, we have observed a consistent higher proportion of positive targets, especially when using RNA-seq data. RTN uses mutual information (MI) to assess TF-target associations, assigning the direction of the inferred associations by Spearman's correlations. Dam et al. (2018) have acknowledged that different RNA-seq normalization methods introduce different biases in co-expression analysis, usually towards positive correlation, possibly affected by read-depth differences between samples and the large abundance of 0 values present in RNA-seq-derived expression matrices. This funtion can help to check whether the numbers of positive and negative target genes are reasonably well balanced in the regulons.

### Author(s)

Mauro Castro, Gordon Robertson

### References

Dam et al. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform. 2018 Jul 20;19(4):575-592. doi: 10.1093/bib/bbw139.

### Examples

    data(tniData)

    ## Not run:

    # preprocessing
    rtni <- tni.constructor(expData=tniData$expData,
            regulatoryElements=c("PTTG1","E2F2","FOXM1","E2F3","RUNX2"),
            rowAnnotation=tniData$rowAnnotation)

    # compute regulons
    rtni <- tni.permutation(rtni, nPermutations = 1000)
    rtni <- tni.permutation(rtni)
    rtni <- tni.dpi.filter(rtni)

    # check target distribution
    tni.plot.checks(rtni)

    ## End(Not run)

[Package RTN version 2.16.0 Index]