{"id":"https://openalex.org/W2895380123","doi":"https://doi.org/10.13016/m27s7hw65","title":"MULTI-DIMENSIONAL ANALYSIS APPROACHES FOR HETEROGENEOUS SINGLE-CELL DATA","display_name":"MULTI-DIMENSIONAL ANALYSIS APPROACHES FOR HETEROGENEOUS SINGLE-CELL DATA","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2895380123","doi":"https://doi.org/10.13016/m27s7hw65","mag":"2895380123"},"language":"en","primary_location":{"id":"pmh:oai:drum.lib.umd.edu:1903/21244","is_oa":true,"landing_page_url":"https://doi.org/10.13016/M27S7HW65","pdf_url":"http://hdl.handle.net/1903/21244","source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/1903/21244","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101567726","display_name":"Yang Shen","orcid":"https://orcid.org/0000-0003-4432-2995"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shen, Yang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101567726"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.7139999866485596,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.7139999866485596,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4643078148365021},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.352057546377182}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4643078148365021},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.352057546377182}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:drum.lib.umd.edu:1903/21244","is_oa":true,"landing_page_url":"https://doi.org/10.13016/M27S7HW65","pdf_url":"http://hdl.handle.net/1903/21244","source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},{"id":"mag:2895380123","is_oa":false,"landing_page_url":"https://drum.lib.umd.edu/handle/1903/21244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null},{"id":"doi:10.13016/m27s7hw65","is_oa":true,"landing_page_url":"https://doi.org/10.13016/m27s7hw65","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"thesis"}],"best_oa_location":{"id":"pmh:oai:drum.lib.umd.edu:1903/21244","is_oa":true,"landing_page_url":"https://doi.org/10.13016/M27S7HW65","pdf_url":"http://hdl.handle.net/1903/21244","source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2895380123.pdf","grobid_xml":"https://content.openalex.org/works/W2895380123.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2498546883","https://openalex.org/W2097443686","https://openalex.org/W2065806511","https://openalex.org/W2960636416","https://openalex.org/W2064570018","https://openalex.org/W11177181","https://openalex.org/W2581281058","https://openalex.org/W169993609","https://openalex.org/W2313331603","https://openalex.org/W853366486"],"abstract_inverted_index":{"Improvements":[0],"in":[1,11,22,72,117,130,139,164,210,250],"experimental":[2],"techniques":[3],"have":[4],"led":[5],"to":[6,30,43,51,65,80,124,192,232,240,256,260,315],"an":[7],"explosion":[8],"of":[9,17,34,55,97,111,153,171,176,179,189,196,296],"information":[10,98,158],"biology":[12,165],"research.":[13],"The":[14],"increasing":[15],"number":[16,188],"measurements":[18,84,146,190],"comes":[19],"with":[20,147,167,293,326],"challenges":[21],"analyzing":[23],"resulting":[24],"data,":[25,301,304],"as":[26,28,320,322],"well":[27,321],"opportunities":[29],"obtain":[31],"deeper":[32],"insights":[33,319],"biological":[35,73,318],"systems.":[36,74],"Conventional":[37],"average":[38],"based":[39],"methods":[40,182,235,292],"are":[41,62,78,86,107,207,237],"unfit":[42],"analyze":[44],"high":[45,211],"dimensional":[46,212],"datasets":[47,100],"since":[48],"they":[49,61],"fail":[50],"take":[52],"full":[53,95],"advantage":[54],"such":[56],"rich":[57],"information.":[58],"More":[59],"importantly,":[60],"not":[63,208,273],"able":[64,79,314],"capture":[66,241],"the":[67,151,177,194,245,267,274],"heterogeneity":[68,242],"that":[69,77,183,200,236,253],"is":[70,122,231,254],"prevalent":[71],"Sophisticated":[75],"algorithms":[76,91,132,218],"utilize":[81],"all":[82,145],"available":[83],"simultaneously":[85,184],"hence":[87],"emerging":[88],"rapidly.":[89],"These":[90],"excel":[92],"at":[93,244],"making":[94],"use":[96,185],"within":[99],"and":[101,127,141,203,243,258,262,283,305],"revealing":[102],"detailed":[103],"heterogeneity.":[104],"However,":[105,214],"there":[106],"several":[108],"important":[109],"disadvantages":[110],"existing":[112],"algorithms.":[113],"First,":[114],"specific":[115],"knowledge":[116],"statistics":[118],"or":[119],"machine":[120],"learning":[121],"required":[123],"appropriately":[125],"interpret":[126,257],"tune":[128],"parameters":[129],"these":[131],"for":[133],"future":[134],"use.":[135],"This":[136],"may":[137],"result":[138],"misusage":[140],"misinterpretation.":[142],"Second,":[143],"using":[144],"equal":[148],"weighting":[149],"runs":[150],"risk":[152],"noise":[154],"contamination.":[155],"In":[156,226],"addition,":[157],"overload":[159],"has":[160],"become":[161],"more":[162],"common":[163],"research,":[166],"a":[168,186,251],"large":[169,187],"volume":[170],"irrelevant":[172],"measurements.":[173],"Third,":[174],"regardless":[175],"quality":[178],"measurements,":[180],"analysis":[181,234],"need":[191],"avoid":[193],"\u201ccurse":[195],"dimensionality\u201d,":[197],"which":[198],"warns":[199],"distance":[201,220],"estimation":[202,206,221],"nearest":[204,223],"neighbor":[205,224],"meaningful":[209],"space.":[213],"most":[215],"current":[216],"sophisticated":[217],"involve":[219],"and/or":[222],"estimation.":[225],"this":[227],"dissertation,":[228],"my":[229,291,327],"goal":[230],"build":[233],"complex":[238],"enough":[239],"same":[246],"time":[247],"output":[248],"results":[249],"format":[252],"easy":[255],"familiar":[259],"biologists":[261],"medical":[263],"researchers.":[264],"I":[265,289,312],"tackle":[266],"dimension":[268],"reduction":[269],"problem":[270],"by":[271,287],"finding":[272],"best":[275],"subspace":[276],"but":[277],"dividing":[278],"them":[279,285],"into":[280],"multiple":[281],"subspaces":[282],"examine":[284],"one":[286],"one.":[288],"demonstrate":[290],"three":[294],"types":[295],"datasets:":[297],"image-based":[298],"high-throughput":[299],"screening":[300],"flow":[302],"cytometry":[303,307],"mass":[306],"data.":[308],"From":[309],"each":[310],"dataset,":[311],"was":[313],"discover":[316],"new":[317],"re-validate":[323],"well-established":[324],"findings":[325],"methods.":[328]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}