Dataset Information

ICELLNET v2: a versatile method for cell-cell communication analysis from human transcriptomic data.

ABSTRACT:

Summary

Several methods have been developed in the past years to infer cell-cell communication networks from transcriptomic data based on ligand and receptor expression. Among them, ICELLNET is one of the few approaches to consider the multiple subunits of ligands and receptors complexes to infer and quantify cell communication. In here, we present a major update of ICELLNET. As compared to its original implementation, we (i) drastically expanded the ICELLNET ligand-receptor database from 380 to 1669 biologically curated interactions, (ii) integrated important families of communication molecules involved in immune crosstalk, cell adhesion, and Wnt pathway, (iii) optimized ICELLNET framework for single-cell RNA sequencing data analyses, (iv) provided new visualizations of cell-cell communication results to facilitate prioritization and biological interpretation. This update will broaden the use of ICELLNET by the scientific community in different biological fields.

Availability and implementation

ICELLNET package is implemented in R. Source code, documentation and tutorials are available on GitHub (https://github.com/soumelis-lab/ICELLNET).

SUBMITTER: Massenet-Regad L

PROVIDER: S-EPMC10955248 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

ICELLNET v2: a versatile method for cell-cell communication analysis from human transcriptomic data.

Massenet-Regad Lucile L Soumelis Vassili V

Bioinformatics (Oxford, England) 20240301 3

<h4>Summary</h4>Several methods have been developed in the past years to infer cell-cell communication networks from transcriptomic data based on ligand and receptor expression. Among them, ICELLNET is one of the few approaches to consider the multiple subunits of ligands and receptors complexes to infer and quantify cell communication. In here, we present a major update of ICELLNET. As compared to its original implementation, we (i) drastically expanded the ICELLNET ligand-receptor database fro ...[more]

PMID: 38490248

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Project description:BackgroundAnalyzing the human transcriptome is crucial in advancing precision medicine, and the plethora of over half a million human microarray samples in the Gene Expression Omnibus (GEO) has enabled us to better characterize biological processes at the molecular level. However, transcriptomic analysis is challenging because the data is inherently noisy and high-dimensional. Gene set analysis is currently widely used to alleviate the issue of high dimensionality, but the user-defined choice of gene sets can introduce biasness in results. In this paper, we advocate the use of a fixed set of transcriptomic modules for such analysis. We apply independent component analysis to the large collection of microarray data in GEO in order to discover reproducible transcriptomic modules that can be used as features for machine learning. We evaluate the usability of these modules across six studies, and demonstrate (1) their usage as features for sample classification, and also their robustness in dealing with small training sets, (2) their regularization of data when clustering samples and (3) the biological relevancy of differentially expressed features.ResultsWe identified 139 reproducible transcriptomic modules, which we term fundamental components (FCs). In studies with less than 50 samples, FC-space classification model outperformed their gene-space counterparts, with higher sensitivity (p < 0.01). The models also had higher accuracy and negative predictive value (p < 0.01) for small data sets (less than 30 samples). Additionally, we observed a reduction in batch effects when data is clustered in the FC-space. Finally, we found that differentially expressed FCs mapped to GO terms that were also identified via traditional gene-based approaches.ConclusionsThe 139 FCs provide biologically-relevant summarization of transcriptomic data, and their performance in low sample settings suggest that they should be employed in such studies in order to harness the data efficiently.

Dataset Information

ICELLNET v2: a versatile method for cell-cell communication analysis from human transcriptomic data.

Summary

Availability and implementation

Publications

ICELLNET v2: a versatile method for cell-cell communication analysis from human transcriptomic data.

Similar Datasets

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