{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:03:39Z","timestamp":1726041819250},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030298937"},{"type":"electronic","value":"9783030298944"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29894-4_36","type":"book-chapter","created":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T17:41:59Z","timestamp":1566495719000},"page":"445-456","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Clustering of Small-Sample Single-Cell RNA-Seq Data via Feature Clustering and Selection"],"prefix":"10.1007","author":[{"given":"Edwin","family":"Vans","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alok","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashwini","family":"Patil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daichi","family":"Shigemizu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatsuhiko","family":"Tsunoda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"key":"36_CR1","unstructured":"Single-cell RNA-seq datasets. \n                      https:\/\/hemberg-lab.github.io\/scRNA.seq.datasets\/\n                      \n                    . Accessed 08 Sep 2018"},{"key":"36_CR2","unstructured":"SEURAT: R toolkit for single cell genomics (2018). \n                      https:\/\/satijalab.org\/seurat\/\n                      \n                    . Accessed 5 Dec 2018"},{"key":"36_CR3","unstructured":"Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (2007)"},{"issue":"11","key":"36_CR4","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1101\/gr.177725.114","volume":"24","author":"FH Biase","year":"2014","unstructured":"Biase, F.H., Cao, X., Zhong, S.: Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing. Genome Res. 24(11), 1787\u20131796 (2014). \n                      https:\/\/doi.org\/10.1101\/gr.177725.114","journal-title":"Genome Res."},{"issue":"2","key":"36_CR5","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1038\/nbt.3102","volume":"33","author":"F Buettner","year":"2015","unstructured":"Buettner, F., et al.: Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33(2), 155\u2013160 (2015). \n                      https:\/\/doi.org\/10.1038\/nbt.3102","journal-title":"Nat. Biotechnol."},{"issue":"5","key":"36_CR6","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","volume":"36","author":"A Butler","year":"2018","unstructured":"Butler, A., Hoffman, P., Smibert, P., Papalexi, E., Satija, R.: Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36(5), 411\u2013420 (2018). \n                      https:\/\/doi.org\/10.1038\/nbt.4096","journal-title":"Nat. Biotechnol."},{"key":"36_CR7","doi-asserted-by":"publisher","unstructured":"Fan, X., et al.: Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol. 16(1) (2015). \n                      https:\/\/doi.org\/10.1186\/s13059-015-0706-1","DOI":"10.1186\/s13059-015-0706-1"},{"issue":"1","key":"36_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.cell.2016.01.047","volume":"165","author":"M Goolam","year":"2016","unstructured":"Goolam, M., et al.: Heterogeneity in Oct4 and Sox2 targets biases cell fate in 4-cell mouse embryos. Cell 165(1), 61\u201374 (2016). \n                      https:\/\/doi.org\/10.1016\/j.cell.2016.01.047","journal-title":"Cell"},{"issue":"11","key":"36_CR9","doi-asserted-by":"publisher","first-page":"e1004575","DOI":"10.1371\/journal.pcbi.1004575","volume":"11","author":"M Guo","year":"2015","unstructured":"Guo, M., Wang, H., Potter, S.S., Whitsett, J.A., Xu, Y.: SINCERA: a pipeline for single-cell RNA-seq profiling analysis. PLOS Comput. Biol. 11(11), e1004575 (2015). \n                      https:\/\/doi.org\/10.1371\/journal.pcbi.1004575","journal-title":"PLOS Comput. Biol."},{"issue":"3","key":"36_CR10","doi-asserted-by":"publisher","first-page":"658","DOI":"10.3390\/biology1030658","volume":"1","author":"D Hebenstreit","year":"2012","unstructured":"Hebenstreit, D.: Methods, challenges and potentials of single cell RNA-seq. Biology 1(3), 658\u2013667 (2012). \n                      https:\/\/doi.org\/10.3390\/biology1030658","journal-title":"Biology"},{"issue":"7","key":"36_CR11","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1101\/gr.110882.110","volume":"21","author":"S Islam","year":"2011","unstructured":"Islam, S., et al.: Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21(7), 1160\u20131167 (2011). \n                      https:\/\/doi.org\/10.1101\/gr.110882.110","journal-title":"Genome Res."},{"issue":"6172","key":"36_CR12","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1126\/science.1247651","volume":"343","author":"DA Jaitin","year":"2014","unstructured":"Jaitin, D.A., et al.: Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343(6172), 776\u2013779 (2014). \n                      https:\/\/doi.org\/10.1126\/science.1247651","journal-title":"Science"},{"issue":"13","key":"36_CR13","doi-asserted-by":"publisher","first-page":"e117","DOI":"10.1093\/nar\/gkw430","volume":"44","author":"Z Ji","year":"2016","unstructured":"Ji, Z., Ji, H.: TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44(13), e117\u2013e117 (2016). \n                      https:\/\/doi.org\/10.1093\/nar\/gkw430","journal-title":"Nucleic Acids Res."},{"key":"36_CR14","unstructured":"Ji, Z., Ji, H.: TSCAN: Tools for Single-Cell ANalysis, October 2018. \n                      https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/TSCAN.html"},{"issue":"5","key":"36_CR15","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1038\/nmeth.4236","volume":"14","author":"VY Kiselev","year":"2017","unstructured":"Kiselev, V.Y., et al.: SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14(5), 483\u2013486 (2017). \n                      https:\/\/doi.org\/10.1038\/nmeth.4236","journal-title":"Nat. Methods"},{"issue":"1","key":"36_CR16","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.cell.2015.05.047","volume":"162","author":"JH Levine","year":"2015","unstructured":"Levine, J.H., et al.: Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162(1), 184\u2013197 (2015). \n                      https:\/\/doi.org\/10.1016\/j.cell.2015.05.047","journal-title":"Cell"},{"issue":"2","key":"36_CR17","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/tit.1982.1056489","volume":"28","author":"S Lloyd","year":"1982","unstructured":"Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129\u2013137 (1982). \n                      https:\/\/doi.org\/10.1109\/tit.1982.1056489","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"5","key":"36_CR18","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","volume":"161","author":"EZ Macosko","year":"2015","unstructured":"Macosko, E.Z., et al.: Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161(5), 1202\u20131214 (2015). \n                      https:\/\/doi.org\/10.1016\/j.cell.2015.05.002","journal-title":"Cell"},{"key":"36_CR19","unstructured":"Ramazzotti, D., Wang, B., Sano, L.D., Batzoglou, S.: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR), January 2019"},{"issue":"5","key":"36_CR20","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/nbt.3192","volume":"33","author":"R Satija","year":"2015","unstructured":"Satija, R., Farrell, J.A., Gennert, D., Schier, A.F., Regev, A.: Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33(5), 495\u2013502 (2015). \n                      https:\/\/doi.org\/10.1038\/nbt.3192","journal-title":"Nat. Biotechnol."},{"issue":"5","key":"36_CR21","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1038\/nmeth.1315","volume":"6","author":"F Tang","year":"2009","unstructured":"Tang, F., et al.: mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6(5), 377\u2013382 (2009). \n                      https:\/\/doi.org\/10.1038\/nmeth.1315","journal-title":"Nat. Methods"},{"issue":"7500","key":"36_CR22","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1038\/nature13173","volume":"509","author":"B Treutlein","year":"2014","unstructured":"Treutlein, B., et al.: Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509(7500), 371\u2013375 (2014). \n                      https:\/\/doi.org\/10.1038\/nature13173","journal-title":"Nature"},{"issue":"4","key":"36_CR23","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/nmeth.4207","volume":"14","author":"B Wang","year":"2017","unstructured":"Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., Batzoglou, S.: Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat. Methods 14(4), 414\u2013416 (2017). \n                      https:\/\/doi.org\/10.1038\/nmeth.4207","journal-title":"Nat. Methods"},{"issue":"6","key":"36_CR24","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.tibtech.2010.03.002","volume":"28","author":"D Wang","year":"2010","unstructured":"Wang, D., Bodovitz, S.: Single cell analysis: the new frontier in \u2018omics\u2019. Trends Biotechnol. 28(6), 281\u2013290 (2010). \n                      https:\/\/doi.org\/10.1016\/j.tibtech.2010.03.002","journal-title":"Trends Biotechnol."},{"issue":"301","key":"36_CR25","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","volume":"58","author":"JH Ward","year":"1963","unstructured":"Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236\u2013244 (1963). \n                      https:\/\/doi.org\/10.1080\/01621459.1963.10500845","journal-title":"J. Am. Stat. Assoc."},{"issue":"12","key":"36_CR26","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.1093\/bioinformatics\/btv088","volume":"31","author":"C Xu","year":"2015","unstructured":"Xu, C., Su, Z.: Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31(12), 1974\u20131980 (2015). \n                      https:\/\/doi.org\/10.1093\/bioinformatics\/btv088","journal-title":"Bioinformatics"},{"issue":"9","key":"36_CR27","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1038\/nsmb.2660","volume":"20","author":"L Yan","year":"2013","unstructured":"Yan, L., et al.: Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20(9), 1131\u20131139 (2013). \n                      https:\/\/doi.org\/10.1038\/nsmb.2660","journal-title":"Nat. Struct. Mol. Biol."},{"key":"36_CR28","doi-asserted-by":"publisher","unstructured":"\u017durauskien\u0117, J., Yau, C.: pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinform. 17(1) (2016). \n                      https:\/\/doi.org\/10.1186\/s12859-016-0984-y","DOI":"10.1186\/s12859-016-0984-y"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2019: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29894-4_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T17:47:15Z","timestamp":1566496035000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-29894-4_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030298937","9783030298944"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29894-4_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuvu, Yanuka Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fiji","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}