Single cell clustering based on cell-pair differentiability correlation and variance analysis

Bioinformatics
Hao JiangLuonan Chen

Abstract

The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies. We propose a new cell similarity measure based on cell-pair differentiability correlation, which is derived from gene differential pattern among all cell pairs. Through plugging into the framework of hierarchical clustering with this new measure, we further develop a variance analysis based clustering algorithm 'Corr' that can determine cluster number automatically and identify cell types accurately. The robustness and superiority of the proposed algorithm are compared with representative algorithms: shared nearest neighbor (SNN)-Cliq and several other state-of-the-art clustering methods, on many benchmark or real single cell RNA-sequencing datasets in terms of both internal criteria (clustering number and accuracy) and external criteria (purity, adjusted rand index, F1-measure). Moreover, differentiability vector with our new measure provides a new means in identifying potential biomarkers from cancer related single cell datasets even with strong noise. Prognosis analyses fro...Continue Reading

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Citations

Jul 5, 2019·Briefings in Bioinformatics·Ren QiQuan Zou
May 14, 2019·Frontiers in Genetics·Hui TangLuonan Chen
Mar 3, 2020·RNA Biology·Lihong PengLiqian Zhou
May 20, 2020·Frontiers in Genetics·Ruiqing ZhengMin Li
Jun 3, 2021·Interdisciplinary Sciences, Computational Life Sciences·Jin-Xing LiuSheng-Jun Li
Jul 27, 2021·Statistical Science : a Review Journal of the Institute of Mathematical Statistics·Y X Rachel WangHaiyan Huang
Jan 15, 2022·Interdisciplinary Sciences, Computational Life Sciences·Yan WangTao Ding

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