Dongshunyi Li1, Jun Ding1, Ziv Bar-Joseph1,2. 1. Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. 2. Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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.
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.
Authors: Han Kyoung Choe; Hee-Dae Kim; Sung Ho Park; Han-Woong Lee; Jae-Yong Park; Jae Young Seong; Stafford L Lightman; Gi Hoon Son; Kyungjin Kim Journal: Proc Natl Acad Sci U S A Date: 2013-03-18 Impact factor: 11.205
Authors: Xiao Wang; William E Allen; Matthew A Wright; Emily L Sylwestrak; Nikolay Samusik; Sam Vesuna; Kathryn Evans; Cindy Liu; Charu Ramakrishnan; Jia Liu; Garry P Nolan; Felice-Alessio Bava; Karl Deisseroth Journal: Science Date: 2018-06-21 Impact factor: 47.728