scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs. Academic Article

abstract

• We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

authors

• Cai, James
• Chapkin, Robert

published proceedings

• Cell Syst

altmetric score

• 17.25

author list (cited authors)

• Yang, Y., Li, G., Zhong, Y., Xu, Q., Lin, Y., Roman-Vicharra, C., Chapkin, R. S., & Cai, J. J.

citation count

• 2

publication date

• April 2023

publisher

• Elsevier  Publisher

keywords

• Cell Communication
• Cell-cell Interaction
• Cellular Communication
• Communication
• Gene Regression Network
• Ligands
• Machine Learning
• Manifold Alignment
• Neural Networks
• Neural Networks, Computer
• Scrna-seq
• Single-cell Rna Sequencing

Digital Object Identifier (DOI)

• 10.1016/j.cels.2023.01.004

start page

• 302

end page

• 311.e4

volume

• 14

issue

• 4

URL

• http://dx.doi.org/10.1016/j.cels.2023.01.004