run_gsva {decoupleR} | R Documentation |
Calculates regulatory activities using GSVA.
run_gsva( mat, network, .source = .data$source, .target = .data$target, verbose = FALSE, method = "gsva", minsize = 5, ... )
mat |
Matrix to evaluate (e.g. expression matrix).
Target nodes in rows and conditions in columns.
|
network |
Tibble or dataframe with edges and it's associated metadata. |
.source |
Column with source nodes. |
.target |
Column with target nodes. |
verbose |
Gives information about each calculation step. Default: FALSE. |
method |
Method to employ in the estimation of gene-set enrichment. scores per sample. By default this is set to gsva (Hänzelmann et al, 2013). |
minsize |
Integer indicating the minimum number of targets per source. |
... |
Arguments passed on to |
GSVA (Hänzelmann et al., 2013) starts by transforming the input molecular
readouts in mat to a readout-level statistic using Gaussian kernel estimation
of the cumulative density function. Then, readout-level statistics are
ranked per sample and normalized to up-weight the two tails of the rank
distribution. Afterwards, an enrichment score gsva
is calculated
using a running sum statistic that is normalized by subtracting the largest
negative estimate from the largest positive one.
Hänzelmann S. et al. (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 14, 7.
A long format tibble of the enrichment scores for each source across the samples. Resulting tibble contains the following columns:
statistic
: Indicates which method is associated with which score.
source
: Source nodes of network
.
condition
: Condition representing each column of mat
.
score
: Regulatory activity (enrichment score).
Other decoupleR statistics:
decouple()
,
run_aucell()
,
run_fgsea()
,
run_mdt()
,
run_mlm()
,
run_ora()
,
run_udt()
,
run_ulm()
,
run_viper()
,
run_wmean()
,
run_wsum()
inputs_dir <- system.file("testdata", "inputs", package = "decoupleR") mat <- readRDS(file.path(inputs_dir, "input-expr_matrix.rds")) network <- readRDS(file.path(inputs_dir, "input-dorothea_genesets.rds")) run_gsva(mat, network, .source='tf', verbose = FALSE)