Table 1 CCI tools included in this study

Statistical-based tools

  CellCall

Embedded pathway activity analysis for activity score; hypergeometric testing for significance of pathway activity

Single subunit

Ligand-receptor pairs; downstream TF regulation

R

[15]

  CellChat

Law of mass action for communication probability; permutation test for significant interactions

Multi-subunit

Ligand-receptor pairs; signaling cofactors and pathways

R

[11]

  CellPhoneDB

The mean of average ligand and receptor expression values for interaction enrichment; permutation test for significant interactions

Multi-subunit

Ligand-receptor pairs

Python

[10]

  ICELLNET

Product of ligand and receptor expression values for communication score; geometric mean for multi-subunit complexes; Wilcoxon statistical test for highly potential interactions

Multi-subunit

Ligand-receptor pairs

R

[16]

  iTALK

Finding differentially expressed ligand and receptor genes between cell types

Single subunit

Ligand-receptor pairs

R

[17]

  SingleCellSignalR

Regularized product of ligand and receptor for lr-score; estimate lr-score cutoff for filtering interactions

Single subunit

Ligand-receptor pairs

R

[18]

Network-based tools

  Connectome

Cell types as nodes, interactions as edges; gene-wise z-score of ligand and receptor expression values as edge weights; system-wide Wilcoxon rank sum test for significant edges filtering

Single subunit

Ligand-receptor pairs

R

[19]

  CytoTalk

Integrate two de novo intracellular signaling networks by known ligand-receptor interactions; optimal subnetwork searching for significant interactions

Single subunit

Ligand-receptor pairs

R

[20]

  Domino

Construction global signaling network; cluster specific signaling subnetwork for prediction

Multi-subunit

Ligand-receptor pairs; TF regulation

R

[21]

  NATMI

Cell types as nodes, interactions as edges; mean expression or specificity for edge weights; edge weight ranks for confident interactions

Single subunit

Ligand-receptor pairs

Python

[22]

  NicheNet

Weighted network prior knowledge model; compute ligand activity and regulatory potential score using network propagation; select interactions by potential score

Single subunit

Ligand-receptor pairs; ligand-target pairs; receptor-target pairs

R

[12]

  scMLnet

Construct primary ligand-receptor, TF-target, receptor-TF subnetworks using highly expressed genes; merge three subnetworks as final output

Single subunit

Ligand-receptor pairs; receptor-TF pairs; TF-target pairs

R

[23]

ST-based tools

  CellPhoneDB v3

L-R expression for enrichment; permutation test for significance; filter interactions based on spatial microenvironment

Multi-subunit

Ligand-receptor pairs; spatial microenvironment

Python

[13]

  Giotto

Spatial proximity for interacting cell types; spatial co-expression for interactions

Single subunit

Ligand-receptor pairs; cell type colocalization; L-R co-expression

R

[24]

  stLearn

Identify interactions by L-R co-expression and cell type density

Single subunit

Ligand-receptor pairs; cell type colocalization; L-R co-expression

Python

[25]