ggPanelplot: Create a tmod panel plot using ggplot

In tmod: Feature Set Enrichment Analysis for Metabolomics and Transcriptomics

Create a tmod panel plot using ggplot

Description

Create a tmod panel plot using ggplot

Usage

ggPanelplot(
  res,
  sgenes = NULL,
  auc_thr = 0.5,
  q_thr = 0.05,
  filter_row_q = 0.01,
  filter_row_auc = 0.65,
  q_cutoff = 1e-12,
  cluster = TRUE,
  id_order = NULL,
  effect_size = "auto",
  colors = c("red", "grey", "blue"),
  label_angle = 45,
  add_ids = TRUE,
  mset = NULL
)

Arguments

res	a list with tmod results (each element of the list is a data frame returned by a tmod test function)
sgenes	a list with summaries of significantly DE genes by gene set. Each a element of the list is a matrix returned by tmodDecideTests. If NULL, the bars on the plot will be monochromatic.
auc_thr	gene sets enrichments with AUC (or other effect size) below 'auc_thr' will not be shown
q_thr	gene sets enrichments with q (adjusted P value) above 'q_thr' will not be shown
filter_row_q	Gene sets will only be shown if at least in one contrast q is smaller than 'filter_row_q'
filter_row_auc	Gene sets will only be shown if at least in one contrast AUC (or other effect size if specified) is larger than 'filter_row_auc'
q_cutoff	Any q value below 'q_cutoff' will be considered equal to 'q_cutoff'
cluster	whether to cluster the IDs by similarity
id_order	character vector specifying the order of IDs to be shown. This needs not to contain all IDs shown, but whatever IDs are in this vector, they will be shown on top in the given order.
effect_size	name of the column which contains the effect sizes; by default, the name of this column is taken from the "effect_size" attribute of the first result table.
colors	character vector with at least 1 (when sgenes is NULL) or 3 (when sgenes is not NULL) elements
label_angle	The angle at which column labels are shown
add_ids	add IDs of gene sets to their titles on the plot
mset	an object of the type 'tmodGS'. If the option 'cluster' is TRUE, the mset object is used to cluster the gene sets. By default, the built-in transcription modules are used. See details.

Details

Panel plot is a kind of a heatmap. This is the most compact way of representing the results of several gene set enrichment analyses. Each row of a panel plot shows the result for one gene set, and each column shows corresponds to one analysis. For example, if one tests gene set enrichment for a number of different contrasts, then each contrast will be represented in a separate column.

Each cell of a panel plot shows both the effect size and the p-value. The p-value is encoded as transparency: the enrichments with a lower p-value have stronger colors. The size of the bar corresponds to the effect size, however it is defined. For example, in case of the tmodCERNOtest, tmodZtest or tmodUtest it is the area under curve, AUC.

In addition, the bars may also encode information about the number of up- or down-regulated genes. For this, an object must be created using the function tmodDecideTests. This object provides information about which genes in a particular gene set are regulated in which direction.

The order of the gene sets displayed is, by default, determined by clustering the gene sets based on their overlaps. For this to work, ggPanelplot must know about what genes are contained in which gene sets. This is provided by the parameter 'mset'. By default (when mset is NULL) this is the built-in list of gene sets. If, however, the results of gene set enrichment come from a different set of gene sets, you need to specify it with the mset parameter. See Examples.

Value

The object returned is a ggplot2 object which can be further modified the usual way.

See Also

[tmodDecideTests()], [tmodPanelPlot()]

Examples

## prepare a set of results
data(Egambia)
genes <- Egambia$GENE_SYMBOL
exprs <- Egambia[ , -1:-4 ]
group <- gsub("\\..*", "", colnames(exprs))
## test differential expression using limma
design <- cbind(Intercept=rep(1, 30), TB=rep(c(0,1), each= 15))
## Not run: 
library(limma)
fit <- eBayes( lmFit(Egambia[,-c(1:3)], design))
tt <- topTable(fit, coef=2, number=Inf, genelist=Egambia[,1:3] )
res <- tmodCERNOtest(tt$GENE_SYMBOL)
## show the results using a panel plot
ggPanelplot(list(limma=res))
## add information about the significant genes
sgenes <- tmodDecideTests(tt$GENE_SYMBOL, lfc=tt$logFC, pval=tt$adj.P.Val)
names(sgenes) <- "limma"
ggPanelplot(list(limma=res), sgenes=sgenes)
## we will now compare the results of enrichments for different types of
## differential expression tests on the data
res_utest <- apply(exprs, 1, function(x) wilcox.test(x ~ group)$p.value)
res_ttest <- apply(exprs, 1, function(x) t.test(x ~ group)$p.value)
## Calculate the gene set enrichment analysis results for each of the
## different types of tests
res_tmod <- list()
res_tmod$limma <- res
res_tmod$utest <- tmodCERNOtest(genes[order(res_utest)])
res_tmod$ttest <- tmodCERNOtest(genes[order(res_ttest)])
ggPanelplot(res_tmod)
## Using the `mset` parameter
## First, we generate results using a different set of gene sets
data(cell_signatures)
res_cs <- tmodCERNOtest(tt$GENE_SYMBOL, mset=cell_signatures)
## the following will triger a warning that no clustering is possible
## because ggPanelplot doesn't have the information about the gene set
## contents
ggPanelplot(list(res=res_cs))
## if we use the mset parameter, clustering is available
ggPanelplot(list(res=res_cs), mset=cell_signatures)

## End(Not run)

tmod documentation built on March 31, 2023, 9 p.m.