Abstract
We propose TransPlatformer for translating toxicogenomics from one platform to another. Transcriptomic profiling has evolved through multiple generations of technology, from microarrays (e.g., Affymetrix, CodeLink) to more recent high-throughput sequencing and targeted panels such as S1500+. Microarrays, which dominated gene expression studies in the early 2000s, provided affordable and high-throughput transcript quantification but suffered from cross-hybridization issues and limited dynamic range . RNA-Seq, introduced in the late 2000s, revolutionized transcriptomics by enabling unbiased and comprehensive gene expression analysis, albeit at higher costs and computational demands .
Despite advances, many studies rely on historical microarray data, necessitating the translation of legacy data into modern platforms to ensure continuity and comparability. This translation is complicated by factors such as platform-specific probe design, differences in transcript coverage, and batch effects . Existing methods for cross-platform mapping include statistical normalization, machine learning models, and biological anchoring approaches.
The ability to translate transcriptomic data between platforms has broad implications, including enhanced meta-analyses, improved toxicological modeling, and better integration of historical datasets with contemporary research. TransPlatformer seeks to contribute to this effort by evaluating translation methodologies and proposing novel strategies to improve cross-platform gene expression harmonization.
In this repository there are code examples for TransPlatformer implementation
- Developers:
- Contributors:
-
Researcher: Cong, Guojing ; Auerbach, Scott
- Release Date:
- 2025-03-17
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)Primary Award/Contract Number:AC05-00OR22725niehsPrimary Award/Contract Number:AC05-00OR22725
- Code ID:
- 152858
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Country of Origin:
- United States
Citation Formats
Cong, Guojing, Auerbach, Scott, Cong, Guojing, and Auerbach, Scott.
TransPlatformer.
Computer Software.
https://github.com/goooopy/TransPlatformer.
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR), niehs.
17 Mar. 2025.
Web.
doi:10.11578/dc.20250317.2.
Cong, Guojing, Auerbach, Scott, Cong, Guojing, & Auerbach, Scott.
(2025, March 17).
TransPlatformer.
[Computer software].
https://github.com/goooopy/TransPlatformer.
https://doi.org/10.11578/dc.20250317.2.
Cong, Guojing, Auerbach, Scott, Cong, Guojing, and Auerbach, Scott.
"TransPlatformer." Computer software.
March 17, 2025.
https://github.com/goooopy/TransPlatformer.
https://doi.org/10.11578/dc.20250317.2.
@misc{
doecode_152858,
title = {TransPlatformer},
author = {Cong, Guojing and Auerbach, Scott and Cong, Guojing and Auerbach, Scott},
abstractNote = {We propose TransPlatformer for translating toxicogenomics from one platform to another. Transcriptomic profiling has evolved through multiple generations of technology, from microarrays (e.g., Affymetrix, CodeLink) to more recent high-throughput sequencing and targeted panels such as S1500+. Microarrays, which dominated gene expression studies in the early 2000s, provided affordable and high-throughput transcript quantification but suffered from cross-hybridization issues and limited dynamic range . RNA-Seq, introduced in the late 2000s, revolutionized transcriptomics by enabling unbiased and comprehensive gene expression analysis, albeit at higher costs and computational demands .
Despite advances, many studies rely on historical microarray data, necessitating the translation of legacy data into modern platforms to ensure continuity and comparability. This translation is complicated by factors such as platform-specific probe design, differences in transcript coverage, and batch effects . Existing methods for cross-platform mapping include statistical normalization, machine learning models, and biological anchoring approaches.
The ability to translate transcriptomic data between platforms has broad implications, including enhanced meta-analyses, improved toxicological modeling, and better integration of historical datasets with contemporary research. TransPlatformer seeks to contribute to this effort by evaluating translation methodologies and proposing novel strategies to improve cross-platform gene expression harmonization.
In this repository there are code examples for TransPlatformer implementation},
doi = {10.11578/dc.20250317.2},
url = {https://doi.org/10.11578/dc.20250317.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20250317.2}},
year = {2025},
month = {mar}
}