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Finding and exploring reproducible cell phenotypes with the Stemformatics data portal

Choi, J.; Butcher, S.; Angel, P.; Bransfield, J.; Barry, J.; Faux, N.; Shaban, B.; Pillai, P.; Michalewicz, A.; Wells, C. A.

2023-06-07 systems biology
10.1101/2023.06.05.543705 bioRxiv
Show abstract

Stemformatics is an established online data portal which hosts hundreds of curated gene expression datasets. It has been serving the stem cell research community for over a decade, by hosting transcriptional profiles of pluripotent and adult stem cells and their progeny from multiple tissues and derivation methods. The portal provides easy-to-use online tools to explore gene expression patterns in published data. In recent years, Stemformatics has shifted its focus from curation to collation and integration of public data with shared phenotypes. It now hosts several integrated expression atlases based on human myeloid cells, which allow for easy cross-dataset comparisons and discovery of emerging cell subsets and activation properties. The inclusion of laboratory-derived cell types enables users to benchmark their own data, to assist with cell-type standardisation or improve cell-derivation methods. The sample annotations have been greatly improved to enable better data integration, and the website has also undergone a major upgrade to modernise its visualisation tools. An application programming interface server also provides the data directly for computational users. Stemformatics is an open-source project and readily available at stemformatics.org.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
BMC Bioinformatics
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Top 0.6%
11.5%
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2
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7
PLOS ONE
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8
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● 242 papers in training set
Top 0.9%
4.2%
50% of probability mass above
9
PLOS Computational Biology
● 1863 papers in training set
Top 8%
4.2%
10
BMC Genomics
○ 406 papers in training set
Top 3%
2.7%
11
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Top 6%
2.7%
12
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Top 2%
2.3%
13
iScience
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Top 12%
2.3%
14
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
1.9%
15
BMC Research Notes
○ 33 papers in training set
Top 0.2%
1.8%
16
Genome Biology
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Top 6%
1.7%
17
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Top 1%
1.7%
18
Cell Reports Methods
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Top 2%
1.6%
19
Nature Protocols
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Top 0.4%
1.3%
20
npj Systems Biology and Applications
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Top 1%
1.3%
21
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Top 0.8%
1.1%
22
Heliyon
○ 152 papers in training set
Top 6%
1.1%
23
Neuroinformatics
○ 46 papers in training set
Top 0.7%
1.1%
24
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◐ 61 papers in training set
Top 0.7%
1.1%
25
Molecular Omics
◐ 23 papers in training set
Top 0.3%
1.0%
26
Scientific Reports
○ 3612 papers in training set
Top 70%
1.0%
27
Biomolecules
○ 100 papers in training set
Top 2%
0.9%
28
F1000Research
○ 88 papers in training set
Top 4%
0.8%
29
BMC Biology
○ 265 papers in training set
Top 6%
0.8%
30
Life Science Alliance
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Top 8%
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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.