ArticleScientific reports2024
Embracing the informative missingness and silent gene in analyzing biologically diverse samples.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Peripheral blood mononuclear cell proteomic profiling reveals cytoskeletal- and chromatin-associated protein signatures in autism spectrum disorder.Brain, behavior, & immunity - health · 2026Article
- Real-Time Detection System for Road Roughness Based on Ultrasonic Technology.Sensors (Basel, Switzerland) · 2026Article
- DeepProtein: deep learning library and benchmark for protein sequence learning.Bioinformatics (Oxford, England) · 2025Article
- Myocardial Proteome in Human Heart Failure With Preserved Ejection Fraction.Journal of the American Heart Association · 2025Article
- iDDN: determining trans-omics network structure and rewiring with integrative differential dependency networks.Bioinformatics advances · 2025Article
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11 authors.
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Abstract
Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.
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