1 2 3 4 5 6 7 8 9 10 11 12
A numerical matrix of p-values where each row is a gene and each column represents an omics dataset (evidence). Rownames correspond to the genes and colnames to the datasets. All values must be 0<=p<=1. We recommend converting missing values to ones.
A GMT object to be used for enrichment analysis. If a filename, a GMT object will be read from the file.
A character vector of gene names to be used as a
statistical background. By default, the background is all genes that appear
A numeric vector of length two giving the lower and
upper limits for the size of the annotated geneset to pathways in gmt.
Pathways with a geneset shorter than
A maximum merged p-value for a gene to be used for analysis.
Any genes with merged, unadjusted
Significance cutoff for selecting enriched pathways. Pathways with
Statistical method to merge p-values. See section on Merging P-Values
Statistical method to correct p-values. See
The directory and/or file prefix to which the output files for generating enrichment maps should be written. If NA, files will not be written.
A data.table of terms (enriched pathways) containing the following columns:
The database ID of the term
The full name of the term
The associated p-value, adjusted for multiple testing
The number of genes annotated to the term
A character vector of the genes enriched in the term
scores (i.e., omics datasets) that contributed
individually to the enrichment of the term. Each input column is evaluated
separately for enrichments and added to the evidence if the term is found.
To obtain a single p-value for each gene across the multiple omics datasets considered,
the p-values in
scores #' are merged row-wise using a data fusion approach of p-value merging.
The two available methods are:
Fisher's method assumes p-values are uniformly
distributed and performs a chi-squared test on the statistic sum(-2 log(p)).
This method is most appropriate when the columns in
Brown's method extends Fisher's method by accounting for the
covariance in the columns of
scores. It is more appropriate when the
tests of significance used to create the columns in
scores are not
necessarily independent. The Brown's method is therefore recommended for
many omics integration approaches.
To visualize and interpret enriched pathways, ActivePathways provides an option
to further analyse results as enrichment maps in the Cytoscape software.
!is.na(cytoscape.file.tag), four files will be written that can be used
to build enrichment maps. This requires the EnrichmentMap and enhancedGraphics apps.
The four files written are:
A list of significant terms and the
associated p-value. Only terms with
adjusted.p.val <= significant are
written to this file.
A matrix indicating whether the significant terms (pathways)
were also found to be significant when considering only one column from
scores. A one indicates that that term was found to be significant
when only p-values in that column were used to select genes.
A Shortened version of the supplied GMT file, containing only the significantly enriched terms in pathways.txt.
A legend with colours matching contributions
from columns in
How to use: Create an enrichment map in Cytoscape with the file of terms (pathways.txt) and the shortened gmt file (pathways.gmt). Upload the subgroups file (subgroups.txt) as a table using the menu File > Import > Table from File. To paint nodes according to the type of supporting evidence, use the 'style' panel, set image/Chart1 to use the column 'instruct' and the passthrough mapping type. Make sure the app enhancedGraphics is installed. Lastly, use the file legend.pdf as a reference for colors in the enrichment map.
1 2 3 4 5 6 7 8 9
fname_scores <- system.file("extdata", "Adenocarcinoma_scores_subset.tsv", package = "ActivePathways") fname_GMT = system.file("extdata", "hsapiens_REAC_subset.gmt", package = "ActivePathways") dat <- as.matrix(read.table(fname_scores, header = TRUE, row.names = 'Gene')) dat[is.na(dat)] <- 1 ActivePathways(dat, fname_GMT)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.