Open access

iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.

Hongming Guo Tingfang Wu Wen-Zheng Wang Yelu Jiang Geng Li Liangpeng Nie Yun-Hua Jia Lijun Quan Moli Huang Qiang Lyu

Aug 2026 · PLoS Computational Biology · Vol 22 8, pp. e1014727 · 0 citations · 69 references

Medicine

TL;DR

iDCF (Interpretable Deconvolution of Cell Fractions) is a novel framework that enforces biological topology onto deep neural networks, bridging the gap between computational inference and biological intuition.

Abstract

Precise resolution of cellular heterogeneity within complex tissues is fundamental to deciphering disease etiologies from bulk transcriptomic profiles. While computational deconvolution offers a scalable alternative, current deep learning methods predominantly operate as "black boxes," neglecting the structural constraints of biological laws. This reliance on purely data-driven feature extraction often yields biologically incoherent predictions and limited mechanistic interpretability. iDCF (Interpretable Deconvolution of Cell Fractions) is a novel framework that enforces biological topology onto deep neural networks. The iDCF architecture employs a dual-stream design, synergizing a standard deep network with a knowledge-based sparse neural network (KSNN) explicitly masked by pathway definitions and protein-protein interaction (PPI) networks. In comprehensive benchmarks, iDCF achieves top-tier performance, consistently ranking among state-of-the-art methods in accuracy and robustness. iDCF integrates the SHapley Additive exPlanations (SHAP) framework, bridging the gap between computational inference and biological intuition. The model's decision logic is governed by established biological mechanisms rather than spurious statistical correlations, validating its reliability. Validations across clinical contexts, including Alzheimer's disease, ovarian cancer, and diabetes, demonstrate iDCF's ability to recover disease-relevant cellular dynamics. iDCF offers a high-performance, interpretable, and biologically grounded tool for deconvolving cell-type proportions, facilitating deeper insights into tissue heterogeneity in health and disease.

DOI PubMed Semantic Scholar

Read PDF

Similar papers

Open access Jul 2026

FloREN: Decoding Immune Regulatory Networks through Interpretable Graph Transformer Patient Representations

A Framework for Learning Over REgulatory-Embedding Networks (FloREN), a supervised and interpretable sample representation method that enables improved sample stratification and biomarker discovery and supports downstream analyses that found specific immune network mechanisms in immune-mediated inflammatory diseases (IMIDs).

Iñigo Clemente‐Larramendi, S. Hillion, D. Cornec et al. · bioRxiv · 0 citations

Open access Jul 2026

PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping

Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping (PRISM), a novel three-stage framework integrating biological prior construction, pseudo-label generation, and multi-level ST refinement, shows strong robustness to domain shift and platform heterogeneity.

Yiheng Xu, Xuehao Wang, Shuqi Liu et al. · Bioinformatics · 0 citations

Jul 2026

When Does Deep Representation Learning Help Single-Cell Clustering? A Sensitivity-Aware Diagnostic Benchmark for Biomedical AI Pipelines

Per-dataset analysis reveals three reproducible regimes: probabilistic variational autoencoder variants help on the smallest datasets, deep autoencoders win on mid-scale data with multi-batch or many-type structure, and classical PCA pipelines remain competitive when linear projection already captures the dominant variation.

Phong T. Nguyen, T. Vu, T. Nguyen et al. · arXiv.org · 0 citations

Open access Jul 2026

Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomics

Phenoverse is introduced, an interpretable deep learning framework that learns sample-level disease state representations through cell type-aware residual encoding, prototype learning, and Perceiver-based aggregation that demonstrates that trajectory-derived genes reveal cross-cohort molecular programs and show consistently higher reproducibility than traditional case-control comparisons.

Manoj M Wagle, Yongheng Wang, Soham Samanta et al. · bioRxiv · 0 citations

Open access Jul 2026

Leveraging multiplicity in biologically informed neural networks to uncover disease heterogeneity

Analysis of the space of BINN explanations can turn multiplicity into a tool for studying complex disease mechanisms, and across 100 replicate BINNs for type 2 diabetes, the authors find distinct solution clusters prioritizing either inflammatory or hepatic-metabolic pathways.

Dennis Gankin, P. Beltrao · bioRxiv · 0 citations

Open access Jul 2026

Synthesizing Mechanistic Hypotheses from Single-Cell Omics via Discretized Feature Attribution and Empirical Language Model Grounding

Single-cell multimodal omics offer unprecedented resolution of cellular networks, yet translating continuous computational attributions into structured, testable biological mechanisms remains a persistent bottleneck. To address this limitation, we introduce an analytical pipeline employing decision trees to discretize continuous neural network attributions into explicit regulatory thresholds. These boundaries then structurally constrain large language models, enabling them to integrate established literature with empirical data to synthesize context-specific hypotheses. Applying this continuous-to-discrete framework across sparse datasets yielded novel biological mechanisms. Specifically, the framework articulated a cytoskeletal gating hierarchy governing EGF-stimulated pathways, identified transcriptomic drivers of input resistance in cortical interneurons, and delineated translational logic predicting Ki-67 abundance within spatial transcriptomics. Retrospective benchmarking validated the framework’s capacity to autonomously reconstruct published regulatory logic. Supported by a locally deployable open-weight language model and a code-free interface, this approach establishes an auditable methodology to extract robust experimental hypotheses from high-dimensional single-cell data.

J. Chen, Yunqi Hong, Alexandra Bermudez et al. · bioRxiv · 0 citations