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Infer transcription factor activity from the meta-signature

Source: R/09_downstream.R
mx_tf_activity.Rd

Estimates the activity of each transcription factor (TF) from the expression changes of its target genes, using a univariate linear model (ULM): for each TF, the gene-level meta statistic is regressed on the TF's signed target weights, and the t-value of the slope is the activity score. This is the default method of decoupleR and is well suited to CollecTRI or DoRothEA regulons.

Usage

mx_tf_activity(
  meta_result,
  network,
  id_col = "gene_id",
  stat = c("z", "log2FC"),
  min_size = 5
)

Arguments

meta_result

A metaXpressResult object.

network

A data.frame of TF-target interactions with columns source (TF), target (gene) and mor (mode of regulation: positive for activation, negative for repression). OmniPath exports (source_genesymbol, target_genesymbol, is_stimulation, is_inhibition) are converted automatically.

id_col

Column of meta_table whose values match network$target. Default "gene_id"; use "symbol" when gene IDs are Entrez/Ensembl and a symbol column is present.

stat

Gene-level statistic: "z" (signed meta z-score, default) or "log2FC".

min_size

Minimum number of measured targets per TF. Default 5.

Value

A data.frame sorted by p-value with columns source, score (positive = TF more active in cases), pvalue, padj (BH) and n_targets.

References

Badia-i-Mompel, P. et al. (2022) decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances, 2(1), vbac016. doi:10.1093/bioadv/vbac016

Müller-Dott, S. et al. (2023) Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. Nucleic Acids Research, 51(20), 10934–10949. doi:10.1093/nar/gkad841

See also

mx_drug_connectivity, mx_downstream_plot

Examples

mt <- data.frame(gene_id = paste0("G", 1:200),
                 meta_log2FC = c(rep(2, 20), rnorm(180, sd = 0.3)),
                 meta_pvalue = c(rep(1e-6, 20), runif(180)),
                 meta_padj = 1, i_squared = 0, n_studies = 3L)
res <- new("metaXpressResult", meta_table = mt, method = "fisher",
           n_studies = 3L)
net <- data.frame(source = rep(c("TF_A", "TF_B"), each = 20),
                  target = paste0("G", c(1:20, 101:120)), mor = 1)
mx_tf_activity(res, net)
#>   source      score       pvalue         padj n_targets
#> 1   TF_A 22.2375993 9.838827e-56 1.967765e-55        20
#> 2   TF_B -0.1869722 8.518739e-01 8.518739e-01        20