| Title: | Collection Of Human And Mouse TF Regulons |
|---|---|
| Description: | DoRothEA is a gene regulatory network containing signed transcription factor (TF) - target gene interactions. DoRothEA regulons, the collection of a TF and its transcriptional targets, were curated and collected from different types of evidence for both human and mouse. A confidence level was assigned to each TF-target interaction based on the number of supporting evidence. |
| Authors: | Pau Badia-i-Mompel [cre] (ORCID: <https://orcid.org/0000-0002-1004-3923>), Daniel Dimitrov [aut] (ORCID: <https://orcid.org/0000-0002-5197-2112>), Christian H. Holland [aut] (ORCID: <https://orcid.org/0000-0002-3060-5786>), Luz Garcia-Alonso [aut], Alberto Valdeolivas [ctb], Minoo Ashtiani [ctb], Attila Gabor [ctb] |
| Maintainer: | Pau Badia-i-Mompel <[email protected]> |
| License: | GPL-3 + file LICENSE |
| Version: | 1.25.0 |
| Built: | 2026-10-10 10:44:07 UTC |
| Source: | https://github.com/bioc/dorothea |
This function converts DoRothEA's regulons that are stored in a table to the
format required by the viper function.
df2regulon(df)df2regulon(df)
df |
A regulon table from dorothea package. |
Regulons in the viper format.
# acessing (human) dorothea regulons # for mouse regulons: data(dorothea_mm, package = "dorothea") data(dorothea_hs, package = "dorothea") # convert to the format required by viper viper_regulons = df2regulon(dorothea_hs)# acessing (human) dorothea regulons # for mouse regulons: data(dorothea_mm, package = "dorothea") data(dorothea_hs, package = "dorothea") # convert to the format required by viper viper_regulons = df2regulon(dorothea_hs)
A table reporting signed human TF-target interactions. This database covers in total 1395 TFs targeting 20,244 genes with 486,676 unique interactions. In addition, each TF is accompanied with an empirical confidence level that was derived from the number of supporting evidences for this TF/interaction. The range is from A (high quality) to E (low quality).
A table of human TF-target interactions:
TF identifier as HGNC symbols
Summary confidence score classifying regulons based on their quality
target identifier as HGNC symbols
mode of regulation indicating the effect of a TF on the target
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6673718/
A table reporting signed human TF-target interactions for cancer application. The difference to "dorothea_hs" is that TGCA gene expression data was used instead of GTEx to infer the networks with ARACNE. This database covers in total 1344 TFs targeting 20,582 genes with 213,230 unique interactions. In addition, each TF is accompanied with an empirical confidence level that was derived from the number of supporting evidences for this TF/interaction. The range is from A (high quality) to E (low quality).
A table of human TF-target interactions:
TF identifier as HGNC symbols
Summary confidence score classifying regulons based on their quality
target identifier as HGNC symbols
mode of regulation indicating the effect of a TF on the target
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6673718/
A table reporting signed human TF-target interactions. This database covers in total 1179 TFs targeting 17,410 genes with 410,455 unique interactions. In addition, each TF is accompanied with an empirical confidence level that was derived from the number of supporting evidences for this TF/interaction. The range is from A (high quality) to E (low quality).
A table of mouse TF-target interactions:
TF identifier as MGI symbols
summary confidence score classifying regulons based on their quality
target identifier as MGI symbols
mode of regulation indicating the effect of a TF on the target
https://www.ncbi.nlm.nih.gov/pubmed/31525460
A table reporting signed human TF-target interactions. The difference to "dorothea_mm" is that TGCA gene expression data was used instead of GTEx to infer the networks with ARACNE.This database covers in total 1096 TFs targeting 17,695 genes with 187,955 unique interactions. In addition, each TF is accompanied with an empirical confidence level that was derived from the number of supporting evidences for this TF/interaction. The range is from A (high quality) to E (low quality).
A table of mouse TF-target interactions:
TF identifier as MGI symbols
summary confidence score classifying regulons based on their quality
target identifier as MGI symbols
mode of regulation indicating the effect of a TF on the target
https://www.ncbi.nlm.nih.gov/pubmed/31525460
This table lists all human TF-target interactions that were derived from the four lines of evidences. Each interaction is assigned a confidence score based on the number of supporting evidences. The table provides also all required information to trace back the origin of the interaction.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6673718/
This function is a convenient wrapper for the
run_viper function using DoRothEA regulons.
run_viper(input, regulons, options = list(), tidy = FALSE, assay_key = "RNA")run_viper(input, regulons, options = list(), tidy = FALSE, assay_key = "RNA")
input |
An object containing a gene expression matrix with genes
(HGNC/MGI symbols) in rows and samples in columns. The object can be a
simple matrix/data frame or more complex objects such as
|
regulons |
|
options |
A list of named options to pass to
|
tidy |
Logical, whether computed TF activities scores should be returned in a tidy format. |
assay_key |
Only applies if the input is a
|
A matrix of normalized enrichment scores for each TF across all
samples. Of note, if you provide Bioconductor objects as input the function
will return this object with added TF activities at appropriate slots. e.g.
Seurat object with a new assay called dorothea. For all
other inputs the function will return a matrix. If tidy is
TRUE the normalized enrichment scores are returned in a tidy format
(not supported for Bioconductor objects).
# use example gene expression matrix from bcellViper package library(bcellViper) data(bcellViper, package = "bcellViper") # acessing (human) dorothea regulons # for mouse regulons: data(dorothea_mm, package = "dorothea") data(dorothea_hs, package = "dorothea") # run viper tf_activities <- run_viper(dset, dorothea_hs, options = list(method = "scale", minsize = 4, eset.filter = FALSE, cores = 1, verbose = FALSE))# use example gene expression matrix from bcellViper package library(bcellViper) data(bcellViper, package = "bcellViper") # acessing (human) dorothea regulons # for mouse regulons: data(dorothea_mm, package = "dorothea") data(dorothea_hs, package = "dorothea") # run viper tf_activities <- run_viper(dset, dorothea_hs, options = list(method = "scale", minsize = 4, eset.filter = FALSE, cores = 1, verbose = FALSE))