Open AccessscClassify: sample size estimation and multiscale classification of cells using single and multiple reference
Author(s) -
Lin Yingxin,
Cao Yue,
Kim Hani Jieun,
Salim Agus,
Speed Terence P,
Lin David M,
Yang Pengyi,
Yang Jean Yee Hwa
Publication year - 2020
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.20199389
Subject(s) - biology , sample size determination , computational biology , estimation , sample (material) , statistics , biological system , mathematics , chromatography , chemistry , management , economics
Automated cell type identification is a key computational challenge in single‐cell RNA ‐sequencing (sc RNA ‐seq) data. To capitalise on the large collection of well‐annotated sc RNA ‐seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single‐cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state‐of‐the‐art methodology in automated cell type identification from sc RNA ‐seq data.