Back

The tidyomics ecosystem: Enhancing omic data analyses

Hutchison, W. J.; Keyes, T. J.; Crowell, H. L.; Soneson, C.; Mu, W.; Park, J.-E.; Davis, E. S.; Nahid, A. A.; Tang, M.; Yuan, V.; Axisa, P.-P.; Kitt, J. W.; Poon, C.-L.; Sato, N.; Kosmac, M.; Serizay, J.; Gottardo, R.; Morgan, M.; Lee, S.; Lawrence, M.; Hicks, S. C.; Nolan, G. P.; Davis, K. L.; Papenfuss, A. T.; Love, M. I.; Mangiola, S.

2023-10-10 bioinformatics
10.1101/2023.09.10.557072 bioRxiv
Show abstract

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

Matching journals

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

1
Bioinformatics
◐ 1204 papers in training set
Top 0.7%
26.9%
Similar papers in this journal
2
Nature Methods
○ 385 papers in training set
Top 0.3%
22.2%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 25%
6.3%
Similar papers in this journal
50% of probability mass above
4
Genome Biology
○ 637 papers in training set
Top 2%
5.6%
Similar papers in this journal
5
Nucleic Acids Research
◐ 1281 papers in training set
Top 4%
4.4%
Similar papers in this journal
6
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
3.3%
7
GigaScience
◐ 212 papers in training set
Top 1%
2.8%
8
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.5%
9
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.4%
10
Journal of Molecular Biology
○ 232 papers in training set
Top 2%
1.7%
11
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 3%
1.5%
12
Cell Reports Methods
○ 165 papers in training set
Top 2%
1.1%
13
Nature Protocols
○ 33 papers in training set
Top 0.4%
1.1%
14
Journal of Proteome Research
● 234 papers in training set
Top 1%
1.1%
15
F1000Research
○ 88 papers in training set
Top 2%
1.1%
16
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.1%
17
Communications Biology
○ 993 papers in training set
Top 26%
1.0%
18
PLOS Computational Biology
● 1863 papers in training set
Top 19%
0.9%
19
Scientific Data
○ 209 papers in training set
Top 3%
0.9%
20
Patterns
○ 78 papers in training set
Top 3%
0.9%
21
Briefings in Bioinformatics
◐ 354 papers in training set
Top 7%
0.9%
22
Scientific Reports
○ 3612 papers in training set
Top 76%
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.