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Distributed Collaboration for Data, Analysis Pipelines, and Results in Single-Cell Omics

Hutton, A.; Ai, L.; Meyer, J. G.

2024-07-30 cell biology
10.1101/2024.07.30.605714 bioRxiv
Show abstract

Single-cell omics data analysis pipelines are complicated to design and difficult to share or reproduce. We describe a web platform that enables no-code analysis pipeline design, simple computing via the Open Science Grid, and sharing of entire data analysis pipelines, their input data, and interactive results. We expect this platform to increase the accessibility and reproducibility of single-cell omics.

Matching journals

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

1
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4
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Top 0.3%
2.4%
10
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Top 2%
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11
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12
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○ 637 papers in training set
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13
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14
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Top 3%
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15
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17
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18
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● 5266 papers in training set
Top 56%
1.0%
19
Wellcome Open Research
○ 67 papers in training set
Top 1%
0.8%
20
iScience
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Top 37%
0.8%
21
Communications Biology
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Top 31%
0.8%
22
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23
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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.