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Benchmarking AlphaSC: A Leap in Single-Cell Data Processing

Vuong, H.; Luu, T.; Nguyen, N.; Tra, N.; Nguyen, H.; Nguyen, H.; Truong, T.; Pham, S.

2023-11-30 bioinformatics
10.1101/2023.11.28.569108 bioRxiv
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AO_SCPLOWBSTRACTC_SCPLOWWe benchmarked AlphaSC, BioTurings GPU-accelerated single-cell analytics package, against other popular tools including Scanpy, Seurat, and RAPIDS. The results demonstrate that AlphaSC operates thousands of times faster than Seurat and Scanpy. Additionally, it surpasses RAPIDS, another GPU-accelerated package from NVIDIA, by an order of magnitude in terms of speed while also consuming considerably less RAM and GPU memory. Importantly, this significant increase in AlphaSCs performance does not compromise its quality. 1

Matching journals

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

1
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Top 0.2%
18.3%
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Top 0.3%
14.9%
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50% of probability mass above
4
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7.8%
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8
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4.0%
9
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Top 1%
2.6%
10
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Top 8%
1.9%
11
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● 468 papers in training set
Top 3%
1.9%
12
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188 papers in training set
Top 1%
1.3%
13
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Top 53%
1.1%
14
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○ 88 papers in training set
Top 3%
1.0%
15
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● 38 papers in training set
Top 0.9%
1.0%
16
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○ 230 papers in training set
Top 5%
0.8%
17
Journal of Computational Biology
○ 48 papers in training set
Top 1%
0.8%
18
Gigabyte
● 62 papers in training set
Top 1%
0.6%
19
Molecular Ecology Resources
○ 171 papers in training set
Top 2%
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.