TF-target interactions from DoRothEASource:
Imports the dataset from: https://omnipathdb.org/interactions?datasets=dorothea which contains transcription factor (TF)-target interactions from DoRothEA https://github.com/saezlab/DoRothEA DoRothEA is a comprehensive resource of transcriptional regulation, consisting of 16 original resources, in silico TFBS prediction, gene expression signatures and ChIP-Seq binding site analysis.
dorothea( resources = NULL, organism = 9606, dorothea_levels = c("A", "B"), fields = NULL, default_fields = TRUE, references_by_resource = TRUE, exclude = NULL, strict_evidences = TRUE, ... )
interactions not reported in these databases are removed. See
get_interaction_resourcesfor more information.
Interactions are available for human, mouse and rat. Choose among: 9606 human (default), 10116 rat and 10090 Mouse
Vector detailing the confidence levels of the interactions to be downloaded. In dorothea, every TF-target interaction has a confidence score ranging from A to E, being A the most reliable interactions. By default we take A and B level interactions (
c(A, B)). It is to note that E interactions are not available in OmnipathR.
The user can define here the fields to be added. If used, set the next argument, `default_fields`, to FALSE.
whether to include the default fields (columns) for the query type. If FALSE, only the fields defined by the user in the `fields` argument will be added.
if FALSE, removes the resource name prefixes from the references (PubMed IDs); this way the information which reference comes from which resource will be lost and the PubMed IDs will be unique.
Character: datasets or resources to exclude.
Logical: restrict the evidences to the queried datasets and resources. If set to FALSE, the directions and effect signs and references might be based on other datasets and resources. In case of DoRothEA this is not desirable for most of the applications. For most of the other interaction querying functions it is `FALSE` by default.
optional additional arguments
dorothea_grn <- dorothea( resources = c('DoRothEA', 'ARACNe-GTEx_DoRothEA'), organism = 9606, dorothea_levels = c('A', 'B', 'C') ) dorothea_grn #> # A tibble: 32,617 × 16 #> source target sourc…¹ targe…² is_di…³ is_st…⁴ is_in…⁵ conse…⁶ conse…⁷ conse…⁸ #> <chr> <chr> <chr> <chr> <int> <int> <int> <int> <int> <int> #> 1 P01106 O14746 MYC TERT 1 1 0 1 1 0 #> 2 P84022 P05412 SMAD3 JUN 1 1 0 1 1 0 #> 3 Q13485 P05412 SMAD4 JUN 1 1 0 1 1 0 #> 4 P08047 P04075 SP1 ALDOA 1 1 0 1 1 0 #> 5 P04637 P08069 TP53 IGF1R 1 0 1 1 0 1 #> 6 Q05516 P20248 ZBTB16 CCNA2 1 0 1 1 0 1 #> 7 Q01196 P08700 RUNX1 IL3 1 0 1 1 0 1 #> 8 P42224 P38936 STAT1 CDKN1A 1 1 0 1 1 0 #> 9 P40763 P38936 STAT3 CDKN1A 1 1 0 1 1 0 #> 10 Q04206 P08183 RELA ABCB1 1 1 0 1 1 0 #> # … with 32,607 more rows, 6 more variables: sources <chr>, references <chr>, #> # curation_effort <int>, dorothea_level <chr>, n_references <int>, #> # n_resources <int>, and abbreviated variable names ¹source_genesymbol, #> # ²target_genesymbol, ³is_directed, ⁴is_stimulation, ⁵is_inhibition, #> # ⁶consensus_direction, ⁷consensus_stimulation, ⁸consensus_inhibition