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Compute Transcription Factor (TF) activity

compute.TFs.activity.Rd

Infers transcription factor (TF) activity from a gene expression matrix using the VIPER algorithm (Alvarez et al., 2016). The function requires a TF-target gene regulatory network, which can be provided by the user or obtained from OmnipathR resources such as CollecTRI or Dorothea. ARACNE-inferred networks are also supported.

Usage

# S3 method for class 'TFs.activity'
compute(
  RNA.counts,
  TF.collection = "CollecTRI",
  min_targets_size = 5,
  universe = NULL,
  cancer.type = NULL,
  cores = 3,
  scale = TRUE,
  return = TRUE,
  file.name = NULL
)

Arguments

RNA.counts

A gene expression matrix with genes as rows and samples as columns. The matrix should be normalized (e.g., TPM, log2CPM, etc.).

TF.collection

Character. The source of the TF-target network. Options are "CollecTRI" (default), "Dorothea", or "ARACNE".

  • "CollecTRI" and "Dorothea" use prebuilt collections from OmnipathR.

  • "ARACNE" allows user input of a custom network file in a 3-column format: regulator, target, and mutual information.

min_targets_size

Integer. Minimum number of target genes per regulon required for TF activity inference. Default is 5.

universe

Optional. A user-specified data frame of TF-target interactions. If not provided, the function will fetch the relevant network based on the TF.collection argument.

cancer.type

Optional character. Cancer type label used when caching the TF collection.

cores

Integer. Number of cores used by VIPER inference. Default is 4.

scale

Logical. If TRUE (default), z-score scales the TF activity matrix across samples.

return

Logical; if TRUE, saves matrix in Results/ folder. Default is TRUE.

file.name

Optional character suffix used when writing the TF activity matrix to disk.

Value

A data frame of inferred and scaled TF activity scores, with samples as rows and TFs as columns.

References

Alvarez, M. et al. (2016). Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nature Genetics, 48(8), 838-847. https://doi.org/10.1038/ng.3593

Tuerei, D., Korcsmaros, T., & Saez-Rodriguez, J. (2016). OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nature Methods, 13(12), 966-967. https://doi.org/10.1038/nmeth.4077

Garcia-Alonso, L. et al. (2019). Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Research. https://doi.org/10.1101/gr.240663.118

Lachmann, A. et al. (2016). ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics, 32(14), 2233-2235. https://doi.org/10.1093/bioinformatics/btw216

Margolin, A.A. et al. (2006). ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 7(Suppl 1), S7. https://doi.org/10.1186/1471-2105-7-S1-S7

Examples

data("counts.norm.tuto")
tfs_activity <- compute.TFs.activity(counts.norm.tuto, cores = 1)
#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
#> e.g.:
#>   dat <- vroom(...)
#>   problems(dat)
#> Error in if (.keep) . else select(., -!!evs_col): argument is of length zero