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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Updated: May 5, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Benchmarking algorithms for single-cell multi-omics prediction and integration.

Yinlei Hu^1,2,3, Siyuan Wan^1,2,4, Yuanhanyu Luo^5,6

¹Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.

|September 25, 2024

Summary

This summary is machine-generated.

This study benchmarks 32 algorithms for single-cell multi-omics data analysis. totalVI and scArches excel at protein prediction, LS_Lab at chromatin accessibility, and Seurat, MOJITOO, scAI, totalVI, and UINMF for integration.

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Last Updated: May 5, 2026

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Area of Science:

Computational Biology
Genomics
Proteomics

Background:

Single-cell multi-omics technologies offer deep biological insights.
Numerous algorithms exist for predicting protein/chromatin data and integrating multi-omics datasets.
Systematic comparisons of these algorithms are lacking.

Purpose of the Study:

To benchmark the performance of 14 protein abundance/chromatin accessibility prediction algorithms.
To evaluate 18 single-cell multi-omics integration algorithms.
To provide guidance for selecting optimal algorithms.

Main Methods:

Conducted a benchmark study using 47 single-cell multi-omics datasets.
Evaluated 14 prediction algorithms and 18 integration algorithms.
Utilized a comprehensive dataset for rigorous performance assessment.

Main Results:

totalVI and scArches demonstrated superior performance in protein abundance prediction.
LS_Lab was the top algorithm for chromatin accessibility prediction.
Seurat, MOJITOO, scAI led in vertical integration; totalVI and UINMF excelled in horizontal and mosaic integration.

Conclusions:

Identified top-performing algorithms for specific single-cell multi-omics tasks.
Developed a pipeline to aid researchers in algorithm selection.
The findings facilitate more effective multi-omics data analysis.