RunMLnet: Generate Multi-layer Signal Networks

In YUZIXD/scMLnet: The Construction of Multi-layer Singaling Network

Description

This function constructs the Ligand_Receptor, Receptor_TF, TF_TarGene networks between the central cell and neighboring cells according to scRNA-Seq expression matrix and barcode table

Usage

RunMLnet(
  GCMat,
  BarCluFile,
  RecClu,
  LigClu,
  pval = 0.05,
  logfc = 0.15,
  LigRecLib = "./database/LigRec.txt",
  TFTarLib = "./database/TFTargetGene.txt",
  RecTFLib = "./database/RecTF.txt"
)

Arguments

GCMat	scRNA-seq data. The gene expression matrix（raw) with rows as genes (gene symbols) and columns as cells.
BarCluFile	The annotation results for clustering. The first column is barcode and the second is cell type.
RecClu	character: The central cell
LigClu	character: The neighboring cell
pval	Screening threshold for getting high expressed gene in cluster. The default setting is 0.05.
logfc	Screening threshold for getting high expressed gene in cluster. The default setting is 0.15.
LigRecLib	The file path of Ligand-Receptor interactions. The default setting is 'LigRec.txt' file in the /databases/ folder.
TFTarLib	The file path of TF-TarFget gene interactions. The default setting is 'TFTargetGene.txt' file in the /databases/ folder.
RecTFLib	The file path of Receptor-TF interactions. The default setting is 'RecTF.txt' file in the /databases/ folder.

Value

A list consists of the Ligand_Receptor, Receptor_TF and TF_TarGene signaling subnetwork. The signaling subnetwork is returned as a dataframe object, including three columns: the first column is molecule A, and the second column is molecule B. There is an interaction between the molecules A and B, which correspond to the ligand, receptor, transcription factor or target gene. The third column is used to visualize the signaling subnetwork.

YUZIXD/scMLnet documentation built on Aug. 18, 2021, 7:29 p.m.