{"doi":"10.1093/bioinformatics/btv715","title":"<i>destiny</i>\n                    : diffusion maps for large-scale single-cell data in R","abstract":"<h4>Unlabelled</h4>: Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single-cell expression data. Here we present destiny, an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming.<h4>Availability and implementation</h4>destiny is an open-source R/Bioconductor package \"bioconductor.org/packages/destiny\" also available at www.helmholtz-muenchen.de/icb/destiny A detailed vignette describing functions and workflows is provided with the package.<h4>Contact</h4>carsten.marr@helmholtz-muenchen.de or f.buettner@helmholtz-muenchen.de<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.","journal":"Bioinformatics","year":2015,"id":9892,"datarank":0.9805037396386959,"base_score":6.536691597591305,"endowment":6.536691597591305,"self_citation_contribution":0.9805037396386959,"citation_network_contribution":0.0,"self_endowment_contribution":0.9805037396386959,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":689,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0474,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2015-12-14","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":29455,"name":"Laleh Haghverdi","orcid":"0000-0001-9280-9170","position":1,"is_corresponding":false},{"id":3572,"name":"Maren Büttner","orcid":"0000-0002-6189-3792","position":2,"is_corresponding":false},{"id":42,"name":"Fabian Joachim Theis","orcid":"0000-0002-2419-1943","position":3,"is_corresponding":false},{"id":21016,"name":"Carsten Marr","orcid":"0000-0003-2154-4552","position":4,"is_corresponding":false},{"id":29,"name":"Florian Buettner","orcid":"0000-0001-5587-6761","position":5,"is_corresponding":false},{"id":3570,"name":"Philipp Angerer","orcid":"0000-0002-0369-2888","position":0,"is_corresponding":true}],"reference_count":7,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","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":[]}