netGOTFPlot: Plot a network for Ontologies, genes and TFs

In cbiagii/pcitRif: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis

Plot a network for Ontologies, genes and TFs

Description

Generate the plot of groupGO network result of getGroupGO function, and the integrated network of genes, GOs and TFs.

Usage

netGOTFPlot(
  netCond,
  resultsGO,
  netGO,
  anno,
  groupBy = "pathways",
  TFs = NULL,
  genes = NULL,
  keyTFs = NULL,
  size = 0.5,
  type = NULL
)

Arguments

netCond	Network of a specific condition. Can be found in result of runAnalysis (see NetworkData and NetworkData).
resultsGO	Dataframe with the results of getGroupGO (first element of list). This result can be filtered by applying filters for pathways selection.
netGO	Dataframe with the results of getGroupGO (second element of list).
anno	Annotation of gene or TFs. Can be found in result of runAnalysis function (see NetworkData).
groupBy	Which variables do you want to group in GO type? The options are: 'pathways', 'TFs' and 'genes' (default: 'pathways').
TFs	A character with selected TFs.
genes	A character with selected genes.
keyTFs	TFs identified as importants by runAnalysis (see NetworkData). This argument is used only if the type argument equals Integrated.
size	Size of nodes labels (default: 0.5).
type	Type of plot selected (GO or Integrated). If GO will plot the associated GO grouped by some variable, and if Integrated will plot a integrated network with genes, GO and TFs.

Value

Returns a list with the plot of the network for GO or integrated output under a condition and the table used to plot the network.

Examples

## Not run:  
# load the annotation package
library(org.Hs.eg.db)

# load the CeTF class object resulted from runAnalysis function
data(CeTFdemo)

# getting the genes in network of condition 1
genes <- unique(c(as.character(NetworkData(CeTFdemo, 'network1')[, 'gene1']),
                 as.character(NetworkData(CeTFdemo, 'network1')[, 'gene2'])))

# performing getGroupGO analysis
cond1 <- getGroupGO(genes = genes,
                    ont = 'BP',
                    keyType = 'ENSEMBL',
                    annoPkg = org.Hs.eg.db, 
                    level = 3)

# selecting only first 12 pathways
t1 <- head(cond1$results, 12)

# subsetting the network to have only the first 12 pathways
t2 <- subset(cond1$netGO, cond1$netGO$gene1 %in% as.character(t1[, 'ID']))

# generate the GO plot grouping by pathways
pt <- netGOTFPlot(netCond = NetworkData(CeTFdemo, 'network1'),
              resultsGO = t1,
              netGO = t2,
              anno = NetworkData(CeTFdemo, 'annotation'),
              groupBy = 'pathways',
              keyTFs = NetworkData(CeTFdemo, 'keytfs'), 
              type = 'GO')
pt$plot
head(pt$tab$`GO:0006807`)

# generate the Integrated plot
pt <- netGOTFPlot(netCond = NetworkData(CeTFdemo, 'network1'),
              resultsGO = t1,
              netGO = t2,
              anno = NetworkData(CeTFdemo, 'annotation'),
              groupBy = 'pathways',
              keyTFs = NetworkData(CeTFdemo, 'keytfs'), 
              type = 'Integrated')
pt$plot
head(pt$tab)

## End(Not run)

cbiagii/pcitRif documentation built on Feb. 5, 2023, 9:03 p.m.