Description Usage Arguments Value
This function implements the TIGRESS model of Haury et al. for gene regulatory network (GRN) inference from gene expression data. Given a matrix of expression data, TIGRESS infers regulation from transcription factor (TFs) to any target gene by performing a regression from the TF expression data to the target gene expression, and scoring the candidate TFs by a stability selection (SS) score.
1 2 3 |
expdata |
A matrix of expression. Each row is an experiment, each column a gene. The gene names are the column names. |
tflist |
The list of TFs names. The TF names should match the names in the expression matrix (default: all genes are considered TFs). |
targetlist |
The list of targets' names. The targets' names should match the names in the expression matrix (default: all genes are considered targets). |
alpha |
The |
nstepsLARS |
Number of LARS steps to perform in stability selection
(default |
nsplit |
Number of sample splits to perform in stability selection
(default |
normalizeexp |
A boolean indicating whether we should mean center and
scale to unit variance the expression data for each gene (default
|
scoring |
The method for scoring a feature in stability selection. If
|
allsteps |
A boolean indicating whether we should output the solutions
for all values of LARS steps up to nstepsLARS, or only for nstepsLARS. It
does not cost more computation to compute all solutions (default
|
verb |
A boolean indicating verbose mode. If |
usemulticore |
A boolean indicating whether multicore parallel computing
is used. This requires the package |
A list of matrices (or a single matrix if allsteps=FALSE
) with
the scores of each TF x target candidate interaction. Each row corresponds
to a TF, each column to a target.
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