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使用SciDAP加速单细胞测序数据分析：一种用户友好的方法。

Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach.

作者信息

Kotliar Michael, Kartashov Andrey, Barski Artem

机构信息

Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.

Datirium, LLC, Cincinnati, OH, USA.

出版信息

bioRxiv. 2024 May 22:2024.02.28.582604. doi: 10.1101/2024.02.28.582604.

DOI:10.1101/2024.02.28.582604
PMID:38464095
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10925325/
Abstract

Single-cell (sc) RNA, ATAC and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform (https://SciDAP.com) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.

摘要

单细胞（sc）RNA、ATAC和多组学测序已成为揭示生物学和疾病机制的强大工具。不幸的是，由于数据量庞大以及配置参数复杂，手动分析sc数据面临多重挑战。这种复杂性以及无法重现计算环境，影响了分析结果的可重复性。科学数据分析平台（https://SciDAP.com）使没有计算专业知识的生物学家能够使用用通用工作流语言（CWL）编写的便携式且可重现的管道来分析基于测序的数据。我们的计算管道套件满足了scRNA-Seq、scATAC-Seq和sc多组学数据分析中最常见的需求。在SciDAP上执行时，它为手动数据处理提供了一种用户友好的替代方案，无需编码专业知识。在本方案中，我们描述了使用SciDAP分析sc多组学数据的方法。类似的方法可用于分析scRNA-Seq、scATAC-Seq和scVDJ-Seq数据集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/5f2786dce841/nihpp-2024.02.28.582604v2-f0011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/19b7402ce407/nihpp-2024.02.28.582604v2-f0008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/5f2786dce841/nihpp-2024.02.28.582604v2-f0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/3922635695f4/nihpp-2024.02.28.582604v2-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/cae08aba175e/nihpp-2024.02.28.582604v2-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/57b9b32fafa6/nihpp-2024.02.28.582604v2-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/e4a1e75d3f6c/nihpp-2024.02.28.582604v2-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/3ab66fcec22d/nihpp-2024.02.28.582604v2-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/ebc14fddafb0/nihpp-2024.02.28.582604v2-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/c186feed9e1e/nihpp-2024.02.28.582604v2-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/19b7402ce407/nihpp-2024.02.28.582604v2-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/4debbdc4bcd1/nihpp-2024.02.28.582604v2-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/75f00be19756/nihpp-2024.02.28.582604v2-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b6/11134825/5f2786dce841/nihpp-2024.02.28.582604v2-f0011.jpg

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