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Distribution-preserved compression of single-cell atlases for privacy-protected data dissemination and novel cell type discovery

Cai, Z.; Hu, Z.; Sun, S.-R.; Wang, Z.; Yang, F.; Han, J.; Zeng, F.

2024-11-18 bioinformatics
10.1101/2024.11.16.622584 bioRxiv
Show abstract

We introduce SUREv2, a tool for constructing lightweight, transmittable, and privacy-preserving references from single cell atlases. SUREv2 introduces a compressed data structure that maintain the distribution of cells within these atlases and develops an out-of-reference scoring method for identifying novel cell populations. This user-friendly tool shall enhance the analysis of single cell datasets by providing a consistent, privacy-focused reference framework.

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

1
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3.2%
10
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Top 3%
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11
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1.7%
12
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13
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14
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1.1%
15
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● 37 papers in training set
Top 1.0%
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16
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18
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Top 13%
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Top 7%
0.6%
20
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○ 201 papers in training set
Top 5%
0.6%

"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.