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In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

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Published on: May 5, 2023

Comparative evaluation of gene selection approaches in transcriptomics: bias correction and visualization with

Dongyue Yu¹, Chen Li², Shuo Yan³

¹Institute of Entomology, College of Life Sciences, Nankai University, Weijin Road, Nankai District, Tianjin 300071, China.

|May 18, 2026

Summary

TransPro, a new framework, improves differential gene selection in transcriptomics by accounting for gene interactions and reducing bias. This leads to more reliable biological interpretation and reproducible results across diverse datasets.

Keywords:

correlation bias differential gene selection gene set enrichment analysis multidimensional visualization multivariate feature selection standardized workflow transcriptomics

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Area of Science:

Transcriptomics
Bioinformatics
Computational Biology

Background:

Current differential gene selection methods in transcriptomics often analyze genes independently, neglecting gene-gene interactions and leading to systematic bias.
Fragmented workflows and inconsistent metrics hinder reproducibility and interpretability of downstream analyses like pathway enrichment.

Purpose of the Study:

To develop an integrated, open-source framework (TransPro) for systematic benchmarking, bias correction, and reproducible visualization in differential gene selection.
To address limitations of conventional methods by incorporating interaction-aware gene selection and standardized downstream analyses.

Main Methods:

Developed TransPro, comprising TransProPy (Python) for multivariate AUC-based complementarity quantification and ensemble learning for interaction-aware gene selection.
TransProR (R) package offers standardized differential analysis, pathway enrichment, and seven visualization workflows (e.g., circular dendrograms, interaction networks).
Validated using 12 diverse cancer and normal tissue datasets with varying origins, platforms, and batch structures.

Main Results:

TransProPy demonstrated improved enrichment results where conventional methods failed, particularly under stringent thresholds.
Positively and negatively correlated genes showed near-equal proportions, with pathway activation/suppression patterns aligning with gene-level correlations.
Quantitative comparisons confirmed significant differences in core enriched gene proportions compared to conventional methods (p < 0.001).

Conclusions:

TransPro provides a unified and reproducible framework that corrects method-specific biases in differential gene selection.
The framework bridges computational discovery with biological interpretation, enhancing the reliability of transcriptomic data analysis.
Openly accessible code, workflows, and data promote reproducibility and community engagement.