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Cross-Platform Omics Pipeline for Integrated Transcriptomics

Gao, L.; Hao, J.; Xin, Z.; Le, H.

2025-06-12 bioinformatics
10.1101/2025.06.11.659031 bioRxiv
Show abstract

The fragmentation of single-cell RNA-seq (scRNA-seq) and spatial transcriptomics (ST) tools across Python/R ecosystems, coupled with escalating computational demands for million-cell datasets, creates critical barriers to reproducible analysis. To overcome this, we present CrosPIT (Cross-Platform Omics Pipeline for Integrated Transcriptomics), a cloud-native framework that unifies cross-language analysis in Google Colab with versioned storage on Google Drive. Pre-configured environments enable rapid switching between CPU/GPU/R runtimes, eliminating local hardware constraints. To demonstrate biological utility, we embedded several modules, such as a module for precision detection of malignant epithelial cells in colorectal cancer. CrosPIT thus delivers a scalable, language-agnostic solution that standardizes workflows through native AnnData/Seurat object interoperability, accelerates integrative omics research, and democratizes access to high-performance transcriptomic analysis.

Matching journals

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

1
Nature Communications
○ 5641 papers in training set
Top 7%
19.2%
Similar papers in this journal
2
Nature Methods
○ 385 papers in training set
Top 0.3%
19.2%
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3
Nature Biotechnology
○ 172 papers in training set
Top 0.2%
10.0%
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4
Bioinformatics
◐ 1204 papers in training set
Top 3%
8.2%
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50% of probability mass above
5
Nucleic Acids Research
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Top 3%
6.5%
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6
Genome Biology
○ 637 papers in training set
Top 3%
4.5%
7
Briefings in Bioinformatics
◐ 354 papers in training set
Top 3%
2.9%
8
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.5%
9
Cell Reports Methods
○ 165 papers in training set
Top 1%
1.8%
10
Communications Biology
○ 993 papers in training set
Top 13%
1.8%
11
Bioinformatics Advances
◐ 203 papers in training set
Top 3%
1.4%
12
Cell Genomics
○ 172 papers in training set
Top 3%
1.2%
13
Genome Medicine
○ 183 papers in training set
Top 3%
1.2%
14
Nature Protocols
○ 33 papers in training set
Top 0.4%
1.2%
15
Nature Computational Science
○ 55 papers in training set
Top 1.0%
1.2%
16
BMC Bioinformatics
○ 457 papers in training set
Top 5%
1.1%
17
Nature Genetics
○ 286 papers in training set
Top 4%
1.1%
18
Scientific Reports
○ 3612 papers in training set
Top 70%
1.0%
19
Cell Systems
○ 201 papers in training set
Top 4%
0.9%
20
GigaScience
◐ 212 papers in training set
Top 4%
0.9%
21
Genome Research
● 468 papers in training set
Top 6%
0.9%
22
iScience
○ 1154 papers in training set
Top 34%
0.9%
23
Advanced Science
○ 286 papers in training set
Top 10%
0.6%
24
Patterns
○ 78 papers in training set
Top 3%
0.6%
25
ACS Synthetic Biology
● 287 papers in training set
Top 3%
0.5%
26
Science Advances
● 1243 papers in training set
Top 35%
0.5%
27
Journal of Genetics and Genomics
○ 38 papers in training set
Top 1%
0.5%

"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.