CQRM: A Composite Quantile Regression Model for Differential Expression Analysis of Non-Coding RNA Count Data
TL;DR
The CQRM complements mean-based DE methods by capturing heterogeneous ncRNA expression changes, supporting the identification of candidate ncRNA biomarkers and regulatory processes relevant to development and disease.
Abstract
Background/Objectives: Non-coding RNAs (ncRNAs) play important roles in various biological processes, primarily via regulating gene expression at multiple levels. Nevertheless, compared to protein-coding genes, ncRNAs are relatively understudied. They are characterized by low expression, large variation, and more dynamic expression across different tissues and cell types, which creates unique challenges in differential expression (DE) analysis. Methods: We propose the CQRM, an adaptive multi-quantile testing framework for ncRNA count data that incorporates jittering to accommodate discrete counts, combines dependent evidence across quantiles, and selects the most informative quantile subset for distinguishing conditions. Results: The CQRM captures diverse expression differences, including changes in variability, skewness, and modality beyond mean shifts. Model-based simulations showed that the CQRM improved the detection of differentially expressed ncRNAs while controlling the false discovery rate relative to existing DE tools, especially when DE genes exhibited multimodal expression patterns and counts were low. Critical ncRNAs and biologically interpretable targeted pathways were identified when we further applied our method to a Smart-seq-total single-cell RNA-seq dataset and a human organ developmental RNA-seq dataset. Conclusions: CQRM complements mean-based DE methods by capturing heterogeneous ncRNA expression changes, supporting the identification of candidate ncRNA biomarkers and regulatory processes relevant to development and disease.