plot_igraph: Plotting functions for Gene Regulatory Network.

In ubioinformat/TraRe: Transcriptional Rewiring

Plotting functions for Gene Regulatory Network.

Description

Collection of functions for generating graphs layouts to plot GRN obtained from NET_run() method. return_layout() generates a layout from the graph object returned by NET_run() and return_layout_phenotype() plots targets according to the t-statistic from the differential expression analysis of the desired phenotype. plot_igraph() takes in the igraph object and generated layout and generates plot.

Usage

plot_igraph(
  mygraph = NULL,
  mytitle = "",
  titlecol = "black",
  mylayout = NULL,
  includelegend = FALSE
)

return_layout(regs = NULL, targets = NULL, namehash = NULL)

return_layout_phenotype(
  regs = NULL,
  targets = NULL,
  varfile = NULL,
  namehash = NULL
)

orderGraphWeights(graph, edgelist)

heatmapplot(
  heatm,
  plotname = "",
  myzlim = c(min(heatm), max(heatm)),
  cvec = c("red", "white", "blue"),
  showRows = TRUE
)

plot_expression_row(
  mymat = NULL,
  rowdesc = "Regulators",
  plotheight = 200,
  myshowrows = TRUE,
  samps2pheno = NULL,
  phenostrs = c("nonrespond", "responder"),
  htmlfile = "./",
  imgdir = "imgs/",
  modnum = 1,
  plotwidth = 800,
  mycvec = c("darkorange", "gray100", "darkblue"),
  plotzlim = c(-10, 10)
)

plot_correlation_row(
  cormats = NULL,
  rowdesc = "regulators",
  xnames = NULL,
  ynames = NULL,
  plotheight = 200,
  myshowrows = TRUE,
  htmlfile = "./",
  imgdir = "imgs/",
  modnum = 1,
  plotwidth = 200,
  mycvec = c("darkred", "gray100", "darkgreen"),
  plotzlim = c(-1, 1),
  plottitle = NULL
)

plot_gene_pair_scatter(
  pname,
  myx,
  myy,
  xgenename,
  ygenename,
  mylabels,
  alltext = NULL,
  plotdir = ""
)

plot_gene_pair_scatter_by_class(
  plotdir,
  pname,
  myx,
  myy,
  xgenename,
  ygenename,
  mylabels,
  lab1text,
  lab2text,
  alltext
)

Arguments

mygraph	igraph object returned from NET_run().
mytitle	Desired tittle.
titlecol	Color for the tittle.
mylayout	desired layout.
includelegend	whether to include legend, boolean. (Default: FALSE)
regs	regulators name list
targets	targets name list
namehash	list containing the drivers genes as names and transcripts as values. If only genes are required, leave it empty.
varfile	two column file containing, gene names as rows, t-statistic from the differential expression analysis of the desired phenotype column and a boolean variable for regulator (1) - no regulator (0) column.
graph	igraph object
edgelist	list containing the edges of the igraph object.
heatm	input matrix for plot.
plotname	name of the plot.
myzlim	the range of z values for which colors should be plotted.
cvec	vector of colors for the palette of the plot.
showRows	boolean specifying the option of showing row names.
mymat	input matrix for plot.
rowdesc	name uses to specify regulator.
plotheight	height of the plot.
myshowrows	boolean specifying the option of showing row names.
samps2pheno	matrix of sample to phenotype.
phenostrs	strings uses distinguish for phenotypes.
htmlfile	directory to html files.
imgdir	directory for image.
modnum	the number of supermodule.
plotwidth	width of the plot.
mycvec	vector of colors for the palette of the plot.
plotzlim	the range of z values for which colors should be plotted.
cormats	input correlation matrix for plot.
xnames	names for the x axis.
ynames	names for the x axis.
plottitle	the title of the plot.
pname	name for the plot.
myx	the coordinates of points of the first gene.
myy	the coordinates of points of the second gene.
xgenename	the names of the first gene.
ygenename	the names of the second gene.
mylabels	integer class labels.
alltext	text label for the plot.
plotdir	directory for the plot.
lab1text	text label of a class.
lab2text	text label of the other class.

Value

plot of the desired single GRN using a specific layout.

Examples

   ## Assume we have run the rewiring method and the `NET_run()` method to generate the
   ## igraph object. We are going to generate and plot both layouts for the example.
   ## We are going to generate all the files we need except for the igraph object, which
   ## is included as an example file in this package.

   ## We load the igraph object that we generated from the `NET_run()` example.
   ## Note: the igraph object is inside the list `NET_run()` generates.

   graph <- readRDS(paste0(system.file('extdata',package='TraRe'),
                    '/graph_netrun_example.rds'))$graphs$VBSR

   ## We first generate the normal layout for the plot.
   ## We need the drivers and target names.

   drivers <- readRDS(paste0(system.file('extdata',package='TraRe'),'/tfs_linker_example.rds'))
   drivers_n <- rownames(drivers)

   targets <- readRDS(paste0(system.file('extdata',package='TraRe'),'/targets_linker_example.rds'))
   targets_n <- rownames(targets)

   ## As for this example we are working at gene level (we dont have transcripts inside genes),
   ## we will generate a dictionary with genes as keys and values (see param `namehash`)

   normal_layout <- return_layout(drivers_n,targets_n)

   ## We now generate the phenotype layout and the `varfile` we ned for this layout.
   ## (I leave here a way to generate) We need to separate our expression matrix by
   ## a binary phenotype, for this case, i will consider the first 40 samples are
   ## responding to a treatment (R) and the rest not (NR).

   gnames <- c(drivers_n,targets_n)
   expmat <-rbind(drivers,targets)

   phenotype <- utils::read.delim(paste0(system.file('extdata',package='TraRe'),
                                  '/phenotype_rewiring_example.txt'))

   expmat_R <- expmat[,phenotype$Class=='R']
   expmat_NR <- expmat[,phenotype$Class=='NR']


   varfile <- t(as.matrix(sapply(gnames,
              function(x) c(stats::t.test(expmat_R[x,],expmat_NR[x,])$statistic,
              if(x%in%drivers_n) 1 else 0))))

   colnames(varfile)<-c('t-stat','is-regulator')

   phenotype_layout <- return_layout_phenotype(drivers_n,targets_n,varfile)

   plot_igraph(graph,mytitle='Normal Layout',titlecol='black',mylayout=normal_layout)
   plot_igraph(graph,mytitle='Phenotype Layout',titlecol='black',mylayout=phenotype_layout)

ubioinformat/TraRe documentation built on March 10, 2024, 1:11 a.m.