Addressing multiple facets of ligand-receptor network inference including single-cell proteomics
Villemin, J.-P.; Giroux, P.; Maillard, M.; Colombo, P.-E.; Larbouret, C.; Colinge, J.
Show abstract
Distinct ligand-receptor interaction (LRI) inference tools often produce markedly different results, and their performance can vary considerably across datasets. Indeed, performance is influenced by differences in experimental designs and dataset-specific features, making it difficult to establish a universal LRI tool. To address this challenge, we expanded our SingleCellSignalR Bioconductor package to provide an integrated framework that incorporates alternative scoring strategies and adjustable analytical depth. We motivate this choice through the analysis of two single-cell transcriptomics datasets that exemplify contrasting experimental designs. Leveraging the new framework flexibility, we present a detailed analysis of paired single-cell proteomics and transcriptomics data, providing, to our knowledge, the first direct comparison of LRI inference across these complementary modalities at single-cell resolution. Finally, we demonstrate how the same framework seamlessly accommodates additional underexplored data types from the LRI perspective, including patient-derived mouse xenografts and bulk RNA sequencing of upstream-sorted cell populations.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2 96%
- Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms 95%
- FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies 95%
Similar papers in this journal
- Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer 95%
- Non-linear Archetypal Analysis of Single-cell RNA-seq Data by Deep Autoencoders 94%
- Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners 94%
Similar papers in this journal
Similar papers in this journal
- SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics 96%
- CelLink: integrating single-cell multi-omics data with weak feature linkage and imbalanced cell populations 95%
- STAN, a computational framework for inferring spatially informed transcription factor activity across cellular contexts 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.