Benchmarking Algorithms for Single-cell Multi-omics Prediction and Integration

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2024 Sep 25

PMID 39322753

Authors

Yinlei Hu

Siyuan Wan

Yuanhanyu Luo

Yuanzhe Li

Tong Wu

Wentao Deng

Chen Jiang

Shan Jiang

Yueping Zhang

Nianping Liu

Zongcheng Yang

Falai Chen

Bin Li

Kun Qu

Affiliations

Soon will be listed here.

Abstract

The development of single-cell multi-omics technology has greatly enhanced our understanding of biology, and in parallel, numerous algorithms have been proposed to predict the protein abundance and/or chromatin accessibility of cells from single-cell transcriptomic information and to integrate various types of single-cell multi-omics data. However, few studies have systematically compared and evaluated the performance of these algorithms. Here, we present a benchmark study of 14 protein abundance/chromatin accessibility prediction algorithms and 18 single-cell multi-omics integration algorithms using 47 single-cell multi-omics datasets. Our benchmark study showed overall totalVI and scArches outperformed the other algorithms for predicting protein abundance, and LS_Lab was the top-performing algorithm for the prediction of chromatin accessibility in most cases. Seurat, MOJITOO and scAI emerge as leading algorithms for vertical integration, whereas totalVI and UINMF excel beyond their counterparts in both horizontal and mosaic integration scenarios. Additionally, we provide a pipeline to assist researchers in selecting the optimal multi-omics prediction and integration algorithm.

Citing Articles

Integrating multiple single-cell multi-omics samples with Smmit.

Wan C, Ji Z bioRxiv. 2023; .

PMID: 37066420 PMC: 10104121. DOI: 10.1101/2023.04.06.535857.

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