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

A Label-Free Multi-Metric Pipeline for Benchmarking Single-Cell RNA-Sequencing Clustering and Testing the Reproducibility of Cell-Type Heterogeneity

A discovered sub-population from single-cell transcriptomic data is only meaningful if it is reproducible, yet clustering is usually done with one method on one embedding and rarely tested. We present a label- free, multi-metric pipeline that reframes clustering as an auditable, methods-blind decision and separates two notions of stability that are commonly conflated: reproducibility under cell resampling (bootstrap) and reproducibility under re-embedding (retraining the representation). The pipeline evaluates seven clustering configurations across cluster counts using five non-redundant quality metrics. As a whole- dataset control on a mouse retinal atlas, it recovers an eight-cell-type annotation at 96.3% accuracy (adjusted Rand index, ARI = 0.91) without labels. We then validate the discovery mode on two cell types with opposite ground truth. On bipolar cells, which have well-established subtypes, the pipeline accepts the sub-structure: across-embedding reproducibility rises with cluster number to a high plateau (mean pairwise ARI ∼0.93 near the ∼15 known bipolar subtypes), with quality metrics improving in parallel. On rod photoreceptors, treated as homogeneous, it rejects over-clustering: the metric-selected partition passes a bootstrap-stability check but is not reproducible when the embedding is retrained (mean pairwise ARI = 0.69), and the metrics do not improve with cluster number. On synthetic data, the test recovers real structure down to a 5% subpopulation while rejecting null data (high sensitivity and specificity). Bootstrap stability alone is therefore insufficient evidence for sub-population; the across-embedding test discriminates real sub-structure from over-clustering and applies to any cell type as a reproducible alternative to single-method, single-embedding clustering.

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

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.

MOFUN-CCC: A Multi-omics Intermediate Fusion Network for Digital White Blood Cell Count Prediction.

A novel multi-modal deep learning model with intermediate fusion: multi-omics fusion neural network- computational cell counting (MOFUN-CCC) designed to predict absolute cell counts directly by integrating gene expression and DNA methylation data within a supervised framework, assuming that the underlying true cell components are shared across the two omics data.

Cross-modal mapping of cancer stem-like cell plasticity using deep learning

Abstract Cancer stem-like cells (CSCs) play a pivotal role in driving tumor heterogeneity, therapeutic resistance, and disease progression. Despite the power of single-cell RNA sequencing (scRNA-seq) to resolve intratumoral hierarchies, there remains a need for robust, scalable tools to consistently profile CSCs across both single-cell and bulk transcriptomic data. To address this, we developed ACSCeND—a unified, machine learning–based framework that enables high-resolution CSC state classification and tissue-level deconvolution. ACSCeND comprises (i) a supervised classifier trained on curated scRNA-seq datasets to assign cells into pluripotent-like, multipotent-like, or unipotent-like states, and (ii) an attention-guided autoencoder that deconvolves CSC subtype proportions from bulk RNA sequencing data. Compared to existing tissue deconvolution tools, ACSCeND achieves superior performance, with higher accuracy across synthetic and real-world samples. Applied to over 25 000 tumor profiles from The Cancer Genome Atlas (TCGA), PREdiction of Clinical Outcomes from Genomics (PRECOG), tumor-relapse, and checkpoint inhibitor studies, ACSCeND reveals that CSC abundance strongly correlates with poor disease-free survival and reduced immunotherapy efficacy. Moreover, it uncovers distinct CSC-state-specific molecular programs, offering insights into CSC-driven heterogeneity and tumor evolution. The model also recapitulates known developmental hierarchies in noncancerous tissues, supporting its broader biological relevance. By integrating single-cell precision with bulk-level applicability, ACSCeND offers a robust, interpretable approach to profiling CSC dynamics and establishes CSC state as a clinically meaningful, pan-cancer biomarker for guiding stemness-informed therapies. ACSCeND is available as a python package (through pip) at https://pypi.org/project/ACSCeND/.

scLGGCL: Label-Guided Graph Contrastive Learning for Single-Cell Fusion Clustering.

scDNM-VAE enables directly inspectable deep clustering of single-cell RNA-seq data through signed dendritic gating

Deep clustering models for single-cell RNA sequencing often assign cells through latent or centroid-based mechanisms that are difficult to inspect. We introduce scDNM-VAE (single-cell Dendritic Neuron Model Variational Autoencoder), a deep clustering framework that combines a variational autoencoder with a dendritic neuron-inspired head. Cluster assignments are governed by learnable signed synaptic weights and thresholds: the weight sign determines the direction of a gate's response to a latent coordinate, its magnitude controls steepness, and the weight-threshold pair determines the transition location. The trained clustering function can therefore be inspected directly without fitting a post-hoc explanation model. We benchmark scDNM-VAE on four datasets spanning immune, cortical, cardiac, and hematopoietic cells against scVI followed by KMeans and an MLP-DEC ablation. scDNM-VAE performs better than scVI on PBMC3k, comparably on the Human Heart Cell Atlas and Paul15, and worse on Zeisel, while producing biologically coherent marker-gene signatures. Ablating each cluster's three highest-magnitude synaptic dimensions causes numerically greater reassignment than random-dimension ablation across all datasets, but the margins are modest and negligible on Zeisel. These results show that signed dendritic gating supports competitive clustering with a parameter-inspectable decision function, while indicating that decision-relevant information is distributed across the latent space.