Identifying Signaling Genes in Spatial Single-cell Expression Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2020 Sep 5

PMID 32886099

Citations 24

Authors

Dongshunyi Li

Jun Ding

Ziv Bar-Joseph

Affiliations

Soon will be listed here.

Abstract

Motivation: Recent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell-cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact.

Results: We developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells.

Availability And Implementation: MESSI is available at: https://github.com/doraadong/MESSI.

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

Intracellular spatial transcriptomic analysis toolkit (InSTAnT).

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Spatial Cellular Networks from omics data with SpaCeNet.

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Cell-cell communication: new insights and clinical implications.

Su J, Song Y, Zhu Z, Huang X, Fan J, Qiao J Signal Transduct Target Ther. 2024; 9(1):196.

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The diversification of methods for studying cell-cell interactions and communication.

Armingol E, Baghdassarian H, Lewis N Nat Rev Genet. 2024; 25(6):381-400.

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A guidebook of spatial transcriptomic technologies, data resources and analysis approaches.

Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J Comput Struct Biotechnol J. 2024; 21:940-955.

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