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CellFateExplorer: An integrated platform for exploring cell fate

Huang, Z.; Ma, H.; Peng, Y.; Zhao, C.; Yu, L.

2025-02-08 cell biology
10.1101/2025.02.04.636565 bioRxiv
Show abstract

Trajectory inference algorithms based on single-cell omics data are powerful tools for predicting and dissecting cell differentiation. However, most existing tools are tailored to specific datasets and lack generalizability across diverse data types. To address this limitation, we developed CellFateExplorer, systematically evaluates the performance of x trajectory inference methods across y datasets. Through an interactive web interface, CellFateExplorer provides guidance on method selection and downstream analysis for specific datasets. In summary, CellFateExplorer is an integrated platform for exploring cell fate.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Bioinformatics
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Top 0.6%
30.7%
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Top 0.1%
3.2%
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○ 12 papers in training set
Top 0.1%
2.1%
9
Genome Biology
○ 637 papers in training set
Top 5%
2.1%
10
PLOS ONE
● 5266 papers in training set
Top 45%
1.9%
11
iScience
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Top 18%
1.7%
12
Scientific Reports
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Top 53%
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13
International Journal of Molecular Sciences
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Top 8%
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14
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Top 6%
1.1%
15
Nucleic Acids Research
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Top 11%
1.1%
16
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1.1%
17
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Top 5%
1.1%
18
Nature Communications
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Top 51%
1.1%
19
Life
○ 29 papers in training set
Top 0.5%
1.1%
20
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● 38 papers in training set
Top 0.9%
1.1%
21
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○ 34 papers in training set
Top 0.5%
1.0%
22
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188 papers in training set
Top 2%
0.8%
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Top 7%
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24
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● 37 papers in training set
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Top 0.4%
0.6%
26
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28
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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.