SoCube: an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data

Author:

Zhang Hongning1,Lu Mingkun1,Lin Gaole1,Zheng Lingyan1,Zhang Wei1,Xu Zhijian2,Zhu Feng134

Affiliation:

1. Polytechnic Institute, The Second Affiliated Hospital, College of Pharmaceutical Sciences, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058 , China

2. Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203 , China

3. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 330110 , China

4. Westlake Laboratory of Life Sciences and Biomedicine , Hangzhou 310024 , China

Abstract

Abstract Doublets formed during single-cell RNA sequencing (scRNA-seq) severely affect downstream studies, such as differentially expressed gene analysis and cell trajectory inference, and limit the cellular throughput of scRNA-seq. Several doublet detection algorithms are currently available, but their generalization performance could be further improved due to the lack of effective feature-embedding strategies with suitable model architectures. Therefore, SoCube, a novel deep learning algorithm, was developed to precisely detect doublets in various types of scRNA-seq data. SoCube (i) proposed a novel 3D composite feature-embedding strategy that embedded latent gene information and (ii) constructed a multikernel, multichannel CNN-ensembled architecture in conjunction with the feature-embedding strategy. With its excellent performance on benchmark evaluation and several downstream tasks, it is expected to be a powerful algorithm to detect and remove doublets in scRNA-seq data. SoCube is freely provided as an end-to-end tool on the Python official package site PyPi (https://pypi.org/project/socube/) and open-source on GitHub (https://github.com/idrblab/socube/).

Funder

Information Technology Center of Zhejiang University

Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare

Westlake Laboratory

Key R&D Program of Zhejiang Province

‘Double Top-Class’ University Project

Fundamental Research Fund for Central Universities

Natural Science Foundation of Zhejiang Province

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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