Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures

By Thalia E. Chan, M. P. Stumpf, Ann C. Babtie. bioRxiv, 2016

541 citations recorded by the source corpus.

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here: https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6a1f812dd73fb266ec1f7ad0093e300bff234d18

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In Molecular Biology

1. Malte D. Luecken
2. Qing Nie
3. Fabian J Theis
4. Zixuan Cang
5. Tieliu Shi
6. Baitang Ning
7. Geng Chen
8. Aviv Regev

Citations in the corpus

• Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “While there exist GRN inference methods that were specifically developed for scRNA-seq data (SCONE: Matsumoto et al , 2017; PIDC: Chan et al , 2017; SCENIC: Aibar et al , 2017), a recent comparison has shown both bulk and single-cell methods to perform poorly on these data (Chen & Mar, 2018).”
• Inferring spatial and signaling relationships between cells from single cell transcriptomic data (2020, methodology): “To interrogate whether two genes affect each other across cells through space, we utilize partial information decomposition 28,29,34 to compute the intercellular gene – gene regulatory information fl ow (Fig.”
• Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “PIDC is another software designed to infer gene regulatory network from single-cell data using multivariate information theory (Chan et al., 2017).”