RankActiveLigands: Predict ligand activity using cell-resolved gene signatures

In BlishLab/scriabin: Single-cell resolved interaction analysis through binning

Predict ligand activity using cell-resolved gene signatures

Description

Predict ligand activity using cell-resolved gene signatures

Usage

RankActiveLigands(
  seu,
  signature_matrix,
  potential_ligands = NULL,
  species = "human",
  database = "OmniPath",
  ligands = NULL,
  recepts = NULL,
  ...
)

Arguments

seu	A seurat object
signature_matrix	The output of GenerateCellSignature
potential_ligands	character vector of ligands to include in active ligand ranking.
species	character. Name of species from which to load ligand-receptor databases. One of: "human", "mouse", "rat". Default: "human"
database	Name of ligand-receptor database to use. Default: "OmniPath" When species is "human", one of: OmniPath, CellChatDB, CellPhoneDB, Ramilowski2015, Baccin2019, LRdb, Kirouac2010, ICELLNET, iTALK, EMBRACE, HPMR, Guide2Pharma, connectomeDB2020, talklr, CellTalkDB When species is "mouse" or "rat", only "OmniPath" is supported. To pass a custom ligand-receptor database to this function, set database = "custom"
ligands	Character vector of custom ligands to use for interaction graph generation. Ignored unless database = "custom" When ligands is supplied, recepts must also be supplied and equidimensional.
recepts	Character vector of custom receptors to use for interaction graph generation. Ignored unless database = "custom" When recepts is supplied, ligands must also be supplied and equidimensional.
...	Additional arguments passed to IDPotentialLigands

Value

Returns a matrix where columns are cells, rows are potential ligands, and values are pearson coefficients corresponding to each ligand's predicted activity in that cell.

References

Browaeys, et al. Nat Methods (2019)

BlishLab/scriabin documentation built on Sept. 16, 2024, 1:19 a.m.