Haotian Teng1, Ye Yuan2, Ziv Bar-Joseph1. 1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213. 2. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
Abstract
MOTIVATION: Recent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies used methods that only rely on the expression levels of the genes in each cell for such assignments. To fully utilize the data and to improve the ability to identify novel sub-types we developed a new method, FICT, which combines both expression and neighborhood information when assigning cell types. RESULTS: FICT optimizes a probabilistic function that we formalize and for which we provide learning and inference algorithms. We used FICT to analyze both simulated and several real spatial transcriptomics data. As we show, FICT can accurately identify cell types and sub-types improving on expression only methods and other methods proposed for clustering spatial transcriptomics data. Some of the spatial subtypes identified by FICT provide novel hypotheses about the new functions for excitatory and inhibitory neurons. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics FICT is available at: https://github.com/haotianteng/FICT.
MOTIVATION: Recent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies used methods that only rely on the expression levels of the genes in each cell for such assignments. To fully utilize the data and to improve the ability to identify novel sub-types we developed a new method, FICT, which combines both expression and neighborhood information when assigning cell types. RESULTS: FICT optimizes a probabilistic function that we formalize and for which we provide learning and inference algorithms. We used FICT to analyze both simulated and several real spatial transcriptomics data. As we show, FICT can accurately identify cell types and sub-types improving on expression only methods and other methods proposed for clustering spatial transcriptomics data. Some of the spatial subtypes identified by FICT provide novel hypotheses about the new functions for excitatory and inhibitory neurons. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics FICT is available at: https://github.com/haotianteng/FICT.
Authors: Jeffrey R Moffitt; Dhananjay Bambah-Mukku; Stephen W Eichhorn; Eric Vaughn; Karthik Shekhar; Julio D Perez; Nimrod D Rubinstein; Junjie Hao; Aviv Regev; Catherine Dulac; Xiaowei Zhuang Journal: Science Date: 2018-11-01 Impact factor: 47.728