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RichPathR: a gene set enrichment analysis and visualization tool

Regmi, B.; Sun, H.-W.; Uhlman, A. J.; Brooks, S. R.

2023-08-30 bioinformatics
10.1101/2023.08.28.555198 bioRxiv
Show abstract

Gene set enrichment analysis (GSEA) is an important step for disease and drug discovery. Genomic, transcriptomics, proteomics and epigenetic analysis of tissue or cells generates gene lists that need to be further investigated in the known biological context. The advent of high-throughput technologies generates the vast number of gene lists that are up or down regulated together. One way of getting meaningful insights of the relationship of these genes is utilizing existing knowledge bases linking them with biological functions or phenotypes. Multiple public databases with annotated gene sets are available for GSEA, and enrichR is the most popular web application still requiring custom tools for large-scale mining. richPathR package is a collection of R functions that helps researchers carry out exploratory analysis and visualization of gene set enrichment using EnrichR. AvailabilityThe package, test data and additional figures can be downloaded from https://github.com/niams-bdmds/richPathR.git. Contactsunh1@nih.gov

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
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19.3%
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13.2%
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Top 1.0%
3.4%
50% of probability mass above
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Top 11%
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Top 3%
2.5%
9
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● 62 papers in training set
Top 0.4%
2.2%
10
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● 242 papers in training set
Top 3%
2.0%
11
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◐ 354 papers in training set
Top 4%
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13
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Top 0.7%
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1.8%
15
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○ 406 papers in training set
Top 4%
1.8%
16
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○ 3612 papers in training set
Top 56%
1.6%
17
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Top 0.4%
1.6%
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Top 2%
1.6%
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Top 3%
1.5%
20
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Top 0.3%
1.4%
21
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Top 0.1%
1.2%
22
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Top 0.8%
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Top 1%
1.1%
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○ 28 papers in training set
Top 0.9%
0.9%
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○ 14 papers in training set
Top 0.7%
0.6%
28
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○ 15 papers in training set
Top 0.3%
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29
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● 38 papers in training set
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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.