GSNA (version 0.1.4.2)

gsnORAtest_cpp: gsnORAtest_cpp

Description

This function performs ORA analysis and returns a data.frame containing various statistics including fold enrichment, and 1 and 2-tailed p-values. (see details)

Usage

gsnORAtest_cpp(l, bg, geneSetCollection)

Value

A data frame containing the results of overrepresentation analysis.

• ID: the gene set identifiers.

• a: the number of genes observed in the background but not in l or the queried gene set.

• b: the number of observed genes in l but not the queried gene set.

• c: the number of observed genes in the queried gene set but not l and

• d: the number of observed genes in both l and the queried gene set, i.e. the overlap.

• N: the number of observed genes the queried gene set.

• Enrichment: The fold overrepresentation of genes in the overlap set d calculated as: $$E = (d / (c+d)) / ((b+d)/(a+b+c+d))$$

• P_2S: 2-sided Fisher p-value. (NOT log-transformed.)

• P_1S: 1-sided Fisher p-value. (NOT log-transformed.)

Arguments

l

(required) A character vector containing a list of gene identifiers. These are generally differentially expressed genes either genes significantly up or significantly down, but they can also be a list of genes that came out of a genetic screen, gene loci with differential chromatin accessibility generated by ATAC-Seq data, lists of genes from GWAS, etc. The order of the genes is unimportant.

bg

(required) A character vector containing a list of gene identifiers corresponding to the total background of observable genes.

geneSetCollection

(required) A list of gene sets, in which the gene sets are character vectors containing gene symbols, and the list names are the corresponding gene set identifiers. NOTE: This must be a list, not a tmod object. It is trivial to extract such a list from a tmod object, however. The $MODULES2GENES field of the tmod object contains a suitable list.

Details

This is the main workhorse function for the ORA test in the GSNA package, however, it performs no filtering of the output data set, nor p-value adjustment, and most users of the package will want to use gsnORAtest() function instead, which calculates adjusted p-values, filters the output data for significance, and can include a Title field in the output data.frame.

See Also

gsnORAtest

Examples

library(GSNA)

# From a differential expression data set, we can generate a
# subset of genes with significant differential expression,
# up or down. Here we will extract genes with significant
# negative differential expression with
# avg_log2FC < 0 and p_val_adj <= 0.05 from **Seurat** data:

sig_DN.genes <-
   toupper( rownames(subset( Bai_CiHep_v_Fib2.de,
                      avg_log2FC < 0  & p_val_adj < 0.05 )) )

# Using all the genes in the differential expression data set,
# we can obtain a suitable background:
bg <- rownames( Bai_CiHep_v_Fib2.de )

# Next we need a gene set collection in the form of a list of
# character vectors. We can convert the **Bai_gsc.tmod** object
# included in the sample data to such a list:
Bai.gsc <- tmod2gsc( Bai_gsc.tmod )

# Now, we can do a overrepresentation analysis search on this
# data using **Bai.gsc**:
sig_DN.gsnora <- gsnORAtest_cpp( l = sig_DN.genes,
                                 bg = bg,
                                 geneSetCollection = Bai.gsc )