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

Kotliar, M.; Kartashov, A.; Barski, A.

2024-02-29 bioinformatics
10.1101/2024.02.28.582604 bioRxiv
Show 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.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
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Top 0.1%
29.8%
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4
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Top 3%
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Top 7%
1.6%
10
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Top 3%
1.4%
11
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○ 88 papers in training set
Top 2%
1.3%
12
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Top 3%
1.1%
13
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Top 4%
1.1%
14
Nature Communications
○ 5641 papers in training set
Top 52%
1.1%
15
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○ 144 papers in training set
Top 3%
1.1%
16
PLOS ONE
● 5266 papers in training set
Top 56%
1.0%
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● 62 papers in training set
Top 1%
0.8%
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0.8%
19
Genomics, Proteomics & Bioinformatics
188 papers in training set
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21
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.