ABDS: a bioinformatics tool suite for analyzing biologically diverse samples

TL;DR

ABDS is a new bioinformatics tool suite designed to better analyze diverse biological samples by improving missing data handling, gene detection, and visualization.

Contribution

ABDS introduces a mechanism-integrated pre-imputation scheme, a cosine-based test for silenced genes, and a unified heatmap for multi-group visualization.

Findings

ABDS improves detection of signature genes by preserving informative missingness.

The cosine-based test effectively identifies group-silenced signature genes.

Unified heatmap visualization enhances interpretation of multiple sample groups.

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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