{"doi":"10.1007/978-1-0716-2756-3_14","title":"A Guide to Trajectory Inference and RNA Velocity","abstract":null,"journal":"Methods in Molecular Biology","year":2023,"id":599498,"datarank":0.4943755299006494,"base_score":3.295836866004329,"endowment":3.295836866004329,"self_citation_contribution":0.4943755299006494,"citation_network_contribution":0.0,"self_endowment_contribution":0.4943755299006494,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":26,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":58992,"name":"Koen Van den Berge","orcid":"0000-0003-2214-0947","position":1,"is_corresponding":false},{"id":58991,"name":"Kelly Street","orcid":"0000-0001-6379-5013","position":2,"is_corresponding":false},{"id":851678,"name":"Simone Tiberi","orcid":"0000-0002-3054-9964","position":3,"is_corresponding":false},{"id":1536391,"name":"Philipp Weiler","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A Guide to Trajectory Inference and RNA Velocity","abstract":"Technological developments have led to an explosion of high-throughput single-cell data, which are revealing unprecedented perspectives on cell identity. Recently, significant attention has focused on investigating, from single-cell RNA-sequencing (scRNA-seq) data, cellular dynamic processes, such as cell differentiation, cell cycle and cell (de)activation. In particular, trajectory inference methods, by ordering cells along a trajectory, allow estimating a differentiation tree of cells. While trajectory inference tools typically work with gene expression levels, common scRNA-seq protocols allow the identification and quantification of unspliced pre-mRNAs and mature spliced mRNAs for each gene. By exploiting the abundance of unspliced and spliced mRNA, one can infer the RNA velocity of individual cells, i.e., the time derivative of the gene expression state of cells. Whereas traditional trajectory inference methods reconstruct cellular dynamics given a population of cells of varying maturity, RNA velocity relies on a dynamical model describing splicing dynamics. Here, we initially discuss conceptual and theoretical aspects of both approaches, then illustrate how they can be combined together, and finally present an example use case on real data.","is_dataset_classified":null,"base_score":3.295836866004329,"endowment":3.295836866004329,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"36495456","pmcid":null,"openalex_id":"https://openalex.org/W4311673709","authors":[],"funders":[],"total_grants":0,"fwci":9.4885,"citation_percentile":0.98728814,"influential_citations":0,"citation_trend":[{"year":2023,"count":7},{"year":2024,"count":7},{"year":2025,"count":5},{"year":2026,"count":7}],"oa_status":"closed","license":"https://www.springernature.com/gp/researchers/text-and-data-mining","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/978-1-0716-2756-3_14","host_type":"publisher"},{"url":"https://doi.org/10.1007/978-1-0716-2756-3_14","host_type":"book series"},{"url":"https://pubmed.ncbi.nlm.nih.gov/36495456","host_type":"repository"},{"url":"https://biblio.ugent.be/publication/01HW2HMPWC6SFWXACRACJDYN89","host_type":"repository"},{"url":"https://hdl.handle.net/11585/906914","host_type":"repository"},{"url":"https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=66998","host_type":"repository"},{"url":"https://push-zb.helmholtz-munich.de/frontdoor.php?source_opus=66998","host_type":"repository"},{"url":"https://www.zora.uzh.ch/254777","host_type":"repository"}],"fields_of_study":["Single-cell and spatial transcriptomics","RNA Research and Splicing","Gene Regulatory Network Analysis"],"mesh_terms":["Cell Differentiation","RNA","RNA Splicing","RNA, Messenger","Sequence Analysis, RNA","Gene Expression Profiling","Single-Cell Analysis"],"keywords":["Trajectory","RNA","Inference","Computational biology","Population","Biology","Gene expression","RNA splicing","Computer science","Expression (computer science)","Gene","Genetics","Artificial intelligence","Physics","Cell differentiation","Transcription","Gene regulation","Bioinformatics","Splicing","Trajectory Inference","Single-cell Rna Sequencing","Rna Velocity"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T10:20:41.971181Z","pmid":null,"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":[]}