{"doi":"10.1016/j.crmeth.2022.100382","title":"Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data","abstract":"Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.","journal":"Cell Reports Methods","year":2023,"id":316570,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":98,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9514,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":556876,"name":"Jielin Xu","orcid":null,"position":1,"is_corresponding":false},{"id":1020248,"name":"Yajie Meng","orcid":"0000-0002-2384-1158","position":2,"is_corresponding":false},{"id":1020249,"name":"Changcheng Lu","orcid":"0000-0001-9263-8463","position":3,"is_corresponding":false},{"id":1020250,"name":"Lijun Cai","orcid":"0009-0009-8057-4146","position":4,"is_corresponding":false},{"id":841279,"name":"Xiangxiang Zeng","orcid":"0000-0001-6201-0114","position":5,"is_corresponding":false},{"id":70301,"name":"Ruth Nussinov","orcid":"0000-0002-8115-6415","position":6,"is_corresponding":false},{"id":69831,"name":"Feixiong Cheng","orcid":"0000-0002-1736-2847","position":7,"is_corresponding":false},{"id":1020247,"name":"Junlin Xu","orcid":"0000-0003-1057-1504","position":0,"is_corresponding":true}],"reference_count":73,"raw_metadata":null,"created_at":"2026-07-19T01:06:38.213358Z","pmid":"36814845","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}