Article

PIANO: Probabilistic Inference Autoencoder Networks for multi-Omics enables robust generative modeling of gene expression and scales single-cell integration to 100 million cells

2026-08-12

Abstract excerpt

Single-cell RNA technologies enable the routine acquisition of transcriptomic atlases. However, these molecular profiles are influenced by overlapping sources of variation. Since these covariates confound comparisons, data integration is the first step in most analyses. Three challenges remain: correcting strong batch effects, scaling to millions of cells, and modeling how covariates influence gene expression. To...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7887b712-ce4d-5cc3-84e8-f91e7636dede
DOI: 10.64898/2026.08.06.743394

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

PIANO: Probabilistic Inference Autoencoder Networks for multi-Omics enables robust generative modeling of gene expression and scales single-cell integration to 100 million cellsDOI 10.64898/2026.08.06.743394Read

Interpretable deep generative ensemble learning for single-cell omics with HydraSemantic proximity Read

multiHIVE: Hierarchical Multimodal Deep Generative Modeling for Single-cell MultiomicsSemantic proximity Read

scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico ExplorationSemantic proximity Read

scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature linksSemantic proximity Read

HoloCell: A Generative Foundation Model for Holistic Cellular ModelingSemantic proximity Read

scGRIP: a graph-based explainable AI framework for single-cell multi-omics Gene Regulatory Inference with Prior KnowledgeSemantic proximity Read

scEMB: Learning context representation of genes based on large-scale single-cell transcriptomicsSemantic proximity Read

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseasesSemantic proximity Read

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic dataSemantic proximity Read

CoxFormer enables spatial omics inference with multimodal generative modelingSemantic proximity Read

Parameter-free representations outperform single-cell foundation models on downstream benchmarksSemantic proximity Read

GLUER: integrative analysis of single-cell omics and imaging data by deep neural networkSemantic proximity Read