#4 · Proteomics
Benchmarking computational omics methods against defined references and scenarios
The dominant thread is systematic evaluation of computational methods under controlled conditions. scArchon provides a modular Snakemake framework benchmarking nine single-cell perturbation response prediction tools across six scRNA-seq datasets. BatchVaria offers a variance-aware evaluation of batch correction in SummarizedExperiment objects, tested on TCGA breast cancer data. A siRNA efficacy benchmark removes cross-validation leakage and compares z-curve RNA encodings, reporting AUC 0.845 on leakage-free validation. A unified benchmark of synthetic data generators for clinical transcriptomic cancer cohorts weighs biological fidelity, statistical similarity and attack-based evaluation. simCRISPR models experimental complexity in pooled screens, benchmarking GxE analysis, normalization and log2FC thresholds. A causal transportability framework with a semi-synthetic Perturb-seq simulator shows that deep models often fail to generalize across settings. What is new is anchoring comparisons to explicit scenario design and leakage-free or external validation rather than single-dataset performance.
Published papers
Interpretable models for scRNA-seq data embedding with multi-scale structure preservation.
Introduces ViScore, a scoring framework for embedding quality metrics, and ViVAE, an interpretable deep-learning model for multi-scale structure preservation…
Advancing morphological profiling with self-supervised learning through comparative insights into representation learning of cellular perturbations.
Large-scale benchmark of self-supervised foundation models (DINO, DINOv2, MAE, iTPN) for Cell Painting morphological profiling on JUMP-CP, outperforming…
TRACER: Twin-contrastive learning for fine-grained and rare spatial domain identification in spatial transcriptomics.
TRACER is a twin-contrastive graph autoencoder for spatial domain identification in spatial transcriptomics, improving detection of rare and fine-grained…
Preprints
Biology-Guided Context-Aware Residual Propagation for Spatial Gene Expression Prediction from Histology
BCARP predicts spatial gene expression from H&E histology via biology-guided residual propagation with nuclei-guided local context and prototype-guided global…
scAnyOmics: Foundation-Model Distillation for Any-to-Any Completion of Multi-omics Virtual Cells
scAnyOmics distills a frozen single-cell foundation model into a multimodal student for any-to-any completion of RNA, protein, and ATAC modalities across…
From WSIs to Bulk, Spatial and Single-Cell Transcriptomics via Multi-Scale Graph Neural Networks
Multi-scale graph neural network predicts bulk, spatial, and single-cell transcriptomics directly from whole-slide histopathology images using pathology…
ALHENA, an H&E encoder distilled from transcriptomic foundation models for multi-scale slide analysis
ALHENA distills transcriptomic foundation model knowledge into an H&E encoder trained on paired Xenium spatial transcriptomics, enabling H&E-only multi-scale…
RFind-sc maps single cells onto biological axes defined by external gene signatures
RFind-sc projects single cells onto interpretable axes defined by external expression signatures, gene sets, or Perturb-seq knockouts, matching or exceeding…
ProtCell: a foundation model for single-cell proteomics
ProtCell, a transformer foundation model pretrained on about 380M cells from 98 datasets, uses masked abundance modeling to integrate and extend fragmented…
How Much Can AI Virtual Cells Gain from Unified Perturbational Modeling?
Proposes unified perturbational modeling (UPM), a VAE-based AI virtual cell predictor integrating heterogeneous Perturb-seq datasets via three unification…
PatHoST: weakly supervised reconstruction of pathogen spatial distributions from host transcriptomes
PatHoST, a weakly supervised framework, reconstructs pathogen spatial distributions from host spatial transcriptomics, validated with M. abscessus benchmarking…
Kontext: Kernel-based method bridging spatial domain identification and cell-cell communication
Kontext is a kernel-based framework jointly representing cells and local environments to identify spatial domains via ligand-receptor interactions, with…
EMBER: Contextualized gene representations enable compositional prediction across cellular perturbations
EMBER, a gene-centric foundation model with frozen contextualized gene embeddings and lightweight probes, predicts single, digenic, and drug-response…
Molecule-resolved inference reveals the spatial organization of transcriptional programs
FICTURE2 is a statistical model inferring the transcriptional program of each individual RNA molecule from gene identity and spatial context, without…
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