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shinyUMAP: an online tool for promoting understanding of single cell omics data visualization

Misra, R.; O'Leary, K.; Chen, W.; Zheng, D.

2025-09-01 bioinformatics
10.1101/2025.08.27.672621 bioRxiv
Show abstract

Visualization is widely used to help explore and interpret high dimensional single cell (sc) omics data, such as scRNA-seq expression data. In particular, uniform manifold approximation and projection (UMAP) has become nearly ubiquitous in scientific publications that apply single cell omics technologies. Some experts have expressed concerns that the global cell-cell relationship, especially the spatial distances among cell clusters in a dataset, may not be faithfully depicted in a 2-dimensional (2D) UMAP. To help users to better appreciate this issue with their own data, we created an online server for the community to upload their single cell data and interactively make UMAPs with different hyper-parameters to witness how the distribution of cell clusters changes. The server thus can help promote proper usages of UMAP, especially to avoid the common pitfalls in misinterpretation of inter-cluster relationships in single cell studies. Availability and ImplementationShinyUMAP is freely available as an online Shiny server implemented in Python at https://scviewer.shinyapps.io/shinyUMAP/.

Matching journals

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

1
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◐ 1204 papers in training set
Top 0.6%
30.0%
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2
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Top 2%
7.6%
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3
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○ 637 papers in training set
Top 2%
6.5%
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4
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◐ 354 papers in training set
Top 1%
6.5%
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50% of probability mass above
5
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Top 26%
6.1%
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6
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○ 165 papers in training set
Top 0.2%
5.3%
7
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○ 385 papers in training set
Top 3%
3.9%
8
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
3.1%
9
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○ 78 papers in training set
Top 0.7%
3.0%
10
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.7%
11
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.3%
12
Scientific Reports
○ 3612 papers in training set
Top 47%
2.1%
13
Nature Computational Science
○ 55 papers in training set
Top 0.5%
2.1%
14
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 4%
1.7%
15
PLOS Computational Biology
● 1863 papers in training set
Top 15%
1.6%
16
iScience
○ 1154 papers in training set
Top 24%
1.3%
17
eLife
● 5828 papers in training set
Top 57%
1.3%
18
Communications Biology
○ 993 papers in training set
Top 24%
1.1%
19
GigaScience
◐ 212 papers in training set
Top 3%
1.1%
20
Nature Protocols
○ 33 papers in training set
Top 0.6%
0.8%
21
Nature Biotechnology
○ 172 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.