{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T20:22:45Z","timestamp":1784060565925,"version":"3.55.0"},"reference-count":72,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"vor","delay-in-days":82,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013060","name":"European Molecular Biology Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013060","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/Papatheodorou-Group\/CATD_snakemake.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioadv\/vbae048","type":"journal-article","created":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T21:40:42Z","timestamp":1711230042000},"source":"Crossref","is-referenced-by-count":11,"title":["CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues"],"prefix":"10.1093","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2701-1987","authenticated-orcid":false,"given":"Anna","family":"Vathrakokoili Pournara","sequence":"first","affiliation":[{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridge, CB10 1SD, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5777-9815","authenticated-orcid":false,"given":"Zhichao","family":"Miao","sequence":"additional","affiliation":[{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridge, CB10 1SD, United Kingdom"},{"name":"Open Targets, Wellcome Genome Campus , Hinxton CB10 1SD, United Kingdom"},{"name":"GMU-GIBH Joint School of Life Sciences, Guangzhou Laboratory, Guangzhou Medical University , Guangzhou, 511436, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ozgur Yilimaz","family":"Beker","sequence":"additional","affiliation":[{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridge, CB10 1SD, United Kingdom"},{"name":"Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla 34956, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadja","family":"Nolte","sequence":"additional","affiliation":[{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridge, CB10 1SD, United Kingdom"},{"name":"Department of Biotechnology and Systems Biology, National Institute of Biology , Ljubljana, 121-1000, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alvis","family":"Brazma","sequence":"additional","affiliation":[{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridge, CB10 1SD, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7270-5470","authenticated-orcid":false,"given":"Irene","family":"Papatheodorou","sequence":"additional","affiliation":[{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridge, CB10 1SD, United Kingdom"},{"name":"Open Targets, Wellcome Genome Campus , Hinxton CB10 1SD, United Kingdom"},{"name":"Earlham Institute, Norwich Research Park , Norwich NR4 7UZ, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"2024041802082919400_vbae048-B1","doi-asserted-by":"crossref","first-page":"e6098","DOI":"10.1371\/journal.pone.0006098","article-title":"Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus","volume":"4","author":"Abbas","year":"2009","journal-title":"PLoS One"},{"key":"2024041802082919400_vbae048-B2","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.cels.2021.05.006","article-title":"AutoGeneS: automatic gene selection using multi-objective optimization for RNA-seq deconvolution","volume":"12","author":"Aliee","year":"2021","journal-title":"Cell Syst"},{"key":"2024041802082919400_vbae048-B3","doi-asserted-by":"crossref","first-page":"10765","DOI":"10.3390\/ijms241310765","article-title":"Comparative analysis of cell mixtures deconvolution and gene signatures generated for blood, immune and cancer cells","volume":"24","author":"Alonso-Moreda","year":"2023","journal-title":"Int J Mol Sci"},{"key":"2024041802082919400_vbae048-B4","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1186\/s13059-017-1349-1","article-title":"xCell: digitally portraying the tissue cellular heterogeneity landscape","volume":"18","author":"Aran","year":"2017","journal-title":"Genome Biol"},{"key":"2024041802082919400_vbae048-B5","doi-asserted-by":"crossref","first-page":"5650","DOI":"10.1038\/s41467-020-19015-1","article-title":"Benchmarking of cell type deconvolution pipelines for transcriptomics data","volume":"11","author":"Avila Cobos","year":"2020","journal-title":"Nat Commun"},{"key":"2024041802082919400_vbae048-B6","doi-asserted-by":"crossref","first-page":"16878","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci Rep"},{"key":"2024041802082919400_vbae048-B7","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1186\/s13059-016-1070-5","article-title":"Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression","volume":"17","author":"Becht","year":"2016","journal-title":"Genome Biol"},{"key":"2024041802082919400_vbae048-B8","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/978-1-4939-7493-1_12","article-title":"Profiling tumor infiltrating immune cells with CIBERSORT","volume":"1711","author":"Chen","year":"2018","journal-title":"Methods Mol Biol"},{"key":"2024041802082919400_vbae048-B9","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/s43018-022-00356-3","article-title":"Cell type and gene expression deconvolution with BayesPrism enables bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology","volume":"3","author":"Chu","year":"2022","journal-title":"Nat Cancer"},{"key":"2024041802082919400_vbae048-B10","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1186\/s13059-023-03016-6","article-title":"Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes. p","volume":"24","author":"Cobos","year":"2023","journal-title":"Genome Biol"},{"key":"2024041802082919400_vbae048-B11","doi-asserted-by":"crossref","article-title":"CZ CELL\u00d7GENE discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data","author":"CZI Single-Cell Biology Program","DOI":"10.1101\/2023.10.30.563174"},{"key":"2024041802082919400_vbae048-B12","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1186\/s13059-020-02048-6","article-title":"Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows","volume":"21","author":"Denisenko","year":"2020","journal-title":"Genome Biol"},{"key":"2024041802082919400_vbae048-B13","doi-asserted-by":"crossref","first-page":"ii141","DOI":"10.1093\/bioinformatics\/btac499","article-title":"SimBu\u202f: Bias-aware simulation of bulk RNA-seq data with variable cell type composition","volume":"38","author":"Dietrich","year":"2022","journal-title":"Bioinformatics"},{"key":"2024041802082919400_vbae048-B14","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1186\/s12859-018-2442-5","article-title":"Deblender: a semi\u2212\/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples","volume":"19","author":"Dimitrakopoulou","year":"2018","journal-title":"BMC Bioinform"},{"key":"2024041802082919400_vbae048-B15","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1093\/bib\/bbz166","article-title":"SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references","volume":"22","author":"Dong","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024041802082919400_vbae048-B16","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1038\/s41467-020-14561-0","article-title":"Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants","volume":"11","author":"Donovan","year":"2020","journal-title":"Nat Commun"},{"key":"2024041802082919400_vbae048-B17","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1593\/neo.121950","article-title":"Breast fibroblasts modulate early dissemination, tumorigenesis, and metastasis through alteration of extracellular matrix characteristics","volume":"15","author":"Dumont","year":"2013","journal-title":"Neoplasia"},{"key":"2024041802082919400_vbae048-B18","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1093\/nar\/30.1.207","article-title":"Gene expression omnibus: NCBI gene expression and hybridization array data repository","volume":"30","author":"Edgar","year":"2002","journal-title":"Nucleic Acids Res"},{"key":"2024041802082919400_vbae048-B19","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1093\/bib\/bbx008","article-title":"Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions","volume":"19","author":"Evans","year":"2017","journal-title":"Brief Bioinform"},{"key":"2024041802082919400_vbae048-B20","doi-asserted-by":"crossref","first-page":"13924","DOI":"10.1073\/pnas.1402665111","article-title":"Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism","volume":"111","author":"Fadista","year":"2014","journal-title":"Proc Natl Acad Sci USA"},{"key":"2024041802082919400_vbae048-B21","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s13073-019-0638-6","article-title":"Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data","volume":"11","author":"Finotello","year":"2019","journal-title":"Genome Med"},{"key":"2024041802082919400_vbae048-B22","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/s41592-019-0355-5","article-title":"Cell composition analysis of bulk genomics using single-cell data","volume":"16","author":"Frishberg","year":"2019","journal-title":"Nat Methods"},{"key":"2024041802082919400_vbae048-B23","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1038\/s41592-023-02166-6","article-title":"Challenges and perspectives in computational deconvolution of genomics data","volume":"21","author":"Garmire","year":"2024","journal-title":"Nat Methods"},{"key":"2024041802082919400_vbae048-B24","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1093\/bioinformatics\/btt351","article-title":"CellMix: a comprehensive toolbox for gene expression deconvolution","volume":"29","author":"Gaujoux","year":"2013","journal-title":"Bioinformatics"},{"key":"2024041802082919400_vbae048-B25","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1016\/j.meegid.2011.08.014","article-title":"Semi-supervised nonnegative matrix factorization for gene expression deconvolution: a case study","volume":"12","author":"Gaujoux","year":"2012","journal-title":"Infect Genet Evol"},{"key":"2024041802082919400_vbae048-B26","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nmeth.4179","article-title":"Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput","volume":"14","author":"Gierahn","year":"2017","journal-title":"Nat Methods"},{"key":"2024041802082919400_vbae048-B27","doi-asserted-by":"crossref","first-page":"e27156","DOI":"10.1371\/journal.pone.0027156","article-title":"Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples","volume":"6","author":"Gong","year":"2011","journal-title":"PLoS One"},{"key":"2024041802082919400_vbae048-B28","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1038\/nature24277","article-title":"Genetic effects on gene expression across human tissues","volume":"550","author":"GTEx Consortium","year":"2017","journal-title":"Nature"},{"key":"2024041802082919400_vbae048-B29","first-page":"lqaa110","article-title":"Computational deconvolution to estimate cell type-specific gene expression from bulk data","volume":"3","author":"Jaakkola","year":"2021","journal-title":"NAR Genomics and Bioinformatics"},{"key":"2024041802082919400_vbae048-B30","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.ccr.2012.02.022","article-title":"Accessories to the crime: functions of cells recruited to the tumor microenvironment","volume":"21","author":"Hanahan","year":"2012","journal-title":"Cancer Cell"},{"key":"2024041802082919400_vbae048-B31","first-page":"e1006976","volume-title":"PLOS Computational Biology","author":"Hao","year":"2019"},{"key":"2024041802082919400_vbae048-B32","doi-asserted-by":"crossref","article-title":"Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods","author":"Hu","DOI":"10.1101\/2023.01.05.522919"},{"key":"2024041802082919400_vbae048-B33","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.celrep.2012.08.003","article-title":"CEL-Seq: single-Cell RNA-Seq by multiplexed linear amplification","volume":"2","author":"Hashimshony","year":"2012","journal-title":"Cell Rep"},{"key":"2024041802082919400_vbae048-B34","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1038\/s41593-017-0029-5","article-title":"Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex","volume":"21","author":"Hrvatin","year":"2018","journal-title":"Nat Neurosci"},{"key":"2024041802082919400_vbae048-B35","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1038\/nature08987","article-title":"International network of cancer genome projects","volume":"464","author":"Hudson","year":"2010","journal-title":"Nature"},{"key":"2024041802082919400_vbae048-B36","doi-asserted-by":"crossref","first-page":"e88843","DOI":"10.1172\/jci.insight.88843","article-title":"Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy","volume":"1","author":"Inkeles","year":"2016","journal-title":"JCI Insight"},{"key":"2024041802082919400_vbae048-B37","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1038\/s41467-020-15816-6","article-title":"Accurate estimation of cell composition in bulk expression through robust integration of single-cell information","volume":"11","author":"Jew","year":"2020","journal-title":"Nat Commun"},{"key":"2024041802082919400_vbae048-B38","doi-asserted-by":"crossref","first-page":"6238","DOI":"10.1158\/0008-5472.CAN-18-3560","article-title":"Comprehensive benchmarking and integration of tumor microenvironment cell estimation methods","volume":"79","author":"Jim\u00e9nez-S\u00e1nchez","year":"2019","journal-title":"Cancer Res"},{"key":"2024041802082919400_vbae048-B39","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s13059-021-02290-6","article-title":"A benchmark for RNA-seq deconvolution analysis under dynamic testing environments","volume":"22","author":"Jin","year":"2021","journal-title":"Genome Biol"},{"key":"2024041802082919400_vbae048-B40","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s12967-020-02235-w","article-title":"Poor clinical outcome in metastatic melanoma is associated with a microRNA-modulated immunosuppressive tumor microenvironment","volume":"18","author":"Jorge","year":"2020","journal-title":"J Transl Med"},{"key":"2024041802082919400_vbae048-B41","doi-asserted-by":"crossref","first-page":"e1007510","DOI":"10.1371\/journal.pcbi.1007510","article-title":"CDSeq: a novel complete deconvolution method for dissecting heterogeneous samples using gene expression data","volume":"15","author":"Kang","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2024041802082919400_vbae048-B42","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1186\/s12864-017-4167-7","article-title":"SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles","volume":"18","author":"Lopez","year":"2017","journal-title":"BMC Genomics"},{"key":"2024041802082919400_vbae048-B43","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/gm540","article-title":"Correcting for cell-type composition bias in epigenome-wide association studies","volume":"6","author":"Lowe","year":"2014","journal-title":"Genome Med"},{"key":"2024041802082919400_vbae048-B44","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"key":"2024041802082919400_vbae048-B45","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1186\/s13059-023-03123-4","article-title":"Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. p","volume":"24","author":"Maden","year":"2023","journal-title":"Genome Biol"},{"key":"2024041802082919400_vbae048-B46","first-page":"9","article-title":"Immunohistochemistry as an important tool in biomarkers detection and clinical practice","volume":"5","year":"2010","journal-title":"Biomarker Insights"},{"key":"2024041802082919400_vbae048-B47","doi-asserted-by":"crossref","first-page":"eaba2619","DOI":"10.1126\/sciadv.aba2619","article-title":"Deep learning\u2013based cell composition analysis from tissue expression profiles","volume":"6","author":"Menden","year":"2020","journal-title":"Sci Adv"},{"key":"2024041802082919400_vbae048-B48","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1016\/j.celrep.2019.01.041","article-title":"RNA-Seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types","volume":"26","author":"Monaco","year":"2019","journal-title":"Cell Rep"},{"key":"2024041802082919400_vbae048-B49","doi-asserted-by":"crossref","first-page":"D129","DOI":"10.1093\/nar\/gkab1030","article-title":"Expression atlas update: gene and protein expression in multiple species","volume":"50","author":"Moreno","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2024041802082919400_vbae048-B50","doi-asserted-by":"crossref","first-page":"bbab265","DOI":"10.1093\/bib\/bbab265","article-title":"Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples","volume":"22","author":"Nadel","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024041802082919400_vbae048-B51","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/nmeth.3337","article-title":"Robust enumeration of cell subsets from tissue expression profiles","volume":"12","author":"Newman","year":"2015","journal-title":"Nat Methods"},{"key":"2024041802082919400_vbae048-B52","doi-asserted-by":"crossref","first-page":"e1008120","DOI":"10.1371\/journal.pcbi.1008120","article-title":"Deconvolving the contributions of cell-type heterogeneity on cortical gene expression","volume":"16","author":"Patrick","year":"2020","journal-title":"PLoS Comput Biol"},{"key":"2024041802082919400_vbae048-B53","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1038\/nmeth.2639","article-title":"Smart-seq2 for sensitive full-length transcriptome profiling in single cells","volume":"10","author":"Picelli","year":"2013","journal-title":"Nat Methods"},{"key":"2024041802082919400_vbae048-B54","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/1471-2105-11-27","article-title":"Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach","volume":"11","author":"Repsilber","year":"2010","journal-title":"BMC Bioinform"},{"key":"2024041802082919400_vbae048-B55","doi-asserted-by":"crossref","first-page":"2881","DOI":"10.1093\/bioinformatics\/btm453","article-title":"Moderated statistical tests for assessing differences in tag abundance","volume":"23","author":"Robinson","year":"2007","journal-title":"Bioinformatics"},{"key":"2024041802082919400_vbae048-B56","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.cmet.2016.08.020","article-title":"Single-Cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes","volume":"24","author":"Segerstolpe","year":"2016","journal-title":"Cell Metab"},{"key":"2024041802082919400_vbae048-B57","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/nmeth.1439","article-title":"Cell type\u2013specific gene expression differences in complex tissues","volume":"7","author":"Shen-Orr","year":"2010","journal-title":"Nat Methods"},{"key":"2024041802082919400_vbae048-B58","doi-asserted-by":"crossref","first-page":"6495","DOI":"10.1038\/s41467-023-41855-w","article-title":"Benchmarking strategies for cross-species integration of single-cell RNA sequencing data","volume":"14","author":"Song","year":"2023","journal-title":"Nat Commun"},{"key":"2024041802082919400_vbae048-B59","doi-asserted-by":"crossref","first-page":"i436","DOI":"10.1093\/bioinformatics\/btz363","article-title":"Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology","volume":"35","author":"Sturm","year":"2019","journal-title":"Bioinformatics"},{"key":"2024041802082919400_vbae048-B60","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1038\/s41467-022-28655-4","article-title":"Comprehensive evaluation of deconvolution methods for human brain gene expression","volume":"13","author":"Sutton","year":"2022","journal-title":"Nat Commun"},{"key":"2024041802082919400_vbae048-B61","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1038\/modpathol.2017.156","article-title":"Implications of the tumor immune microenvironment for staging and therapeutics","volume":"31","author":"Taube","year":"2018","journal-title":"Mod Pathol"},{"key":"2024041802082919400_vbae048-B62","doi-asserted-by":"crossref","first-page":"eabl4896","DOI":"10.1126\/science.abl4896","article-title":"The tabula sapiens: a multiple-organ, single-cell transcriptomic atlas of humans","volume":"376","author":"The Tabula Sapiens Consortium*","year":"2022","journal-title":"Science"},{"key":"2024041802082919400_vbae048-B63","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1186\/s12859-017-1511-5","article-title":"A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide association studies","volume":"18","author":"Teschendorff","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2024041802082919400_vbae048-B51293813","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1038\/nature08987","article-title":"International network of cancer genome projects","author":"The International Cancer Genome Consortium","year":"2010","journal-title":"Nature"},{"key":"2024041802082919400_vbae048-B64","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1038\/s41467-019-10802-z","article-title":"Accurate estimation of cell-type composition from gene expression data","volume":"10","author":"Tsoucas","year":"2019","journal-title":"Nat Commun"},{"key":"2024041802082919400_vbae048-B65","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1038\/s41467-018-08023-x","article-title":"Bulk tissue cell type deconvolution with multi-subject single-cell expression reference","volume":"10","author":"Wang","year":"2019","journal-title":"Nat Commun"},{"key":"2024041802082919400_vbae048-B66","article-title":"Community assessment of methods to deconvolve cellular composition from bulk gene expression"},{"key":"2024041802082919400_vbae048-B67","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1038\/s41591-020-0944-y","article-title":"A single-cell atlas of the peripheral immune response in patients with severe COVID-19","volume":"26","author":"Wilk","year":"2020","journal-title":"Nat Med"},{"key":"2024041802082919400_vbae048-B68","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","article-title":"SCANPY: large-scale single-cell gene expression data analysis","volume":"19","author":"Wolf","year":"2018","journal-title":"Genome Biol"},{"key":"2024041802082919400_vbae048-B69","doi-asserted-by":"crossref","first-page":"e76147","DOI":"10.1371\/journal.pone.0076147","article-title":"High-infiltration of tumor-associated macrophages predicts unfavorable clinical outcome for node-negative breast cancer","volume":"8","author":"Zhang","year":"2013","journal-title":"PLoS One"},{"key":"2024041802082919400_vbae048-B70","doi-asserted-by":"crossref","first-page":"14049","DOI":"10.1038\/ncomms14049","article-title":"Massively parallel digital transcriptional profiling of single cells","volume":"8","author":"Zheng","year":"2017","journal-title":"Nat Commun"},{"key":"2024041802082919400_vbae048-B71","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nprot.2016.154","article-title":"Single-cell barcoding and sequencing using droplet microfluidics","volume":"12","author":"Zilionis","year":"2017","journal-title":"Nat Protoc"}],"container-title":["Bioinformatics Advances"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformaticsadvances\/advance-article-pdf\/doi\/10.1093\/bioadv\/vbae048\/57075504\/vbae048.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformaticsadvances\/article-pdf\/4\/1\/vbae048\/57262617\/vbae048.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformaticsadvances\/article-pdf\/4\/1\/vbae048\/57262617\/vbae048.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T22:18:42Z","timestamp":1713392322000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformaticsadvances\/article\/doi\/10.1093\/bioadv\/vbae048\/7634289"}},"subtitle":[],"editor":[{"given":"Marieke","family":"Kuijjer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":72,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,5]]}},"URL":"https:\/\/doi.org\/10.1093\/bioadv\/vbae048","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.01.19.523443","asserted-by":"object"}]},"ISSN":["2635-0041"],"issn-type":[{"value":"2635-0041","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,1,1]]},"published":{"date-parts":[[2024,1,1]]},"article-number":"vbae048"}}