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10.1093/nar/gkab632 http://scihub22266oqcxt.onion/10.1093/nar/gkab632 34320202!8344557!34320202     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nucleic+Acids+Res 2021 ; 49 (15): 8505-8519 Nephropedia Template TP {{tp|p=34320202|t=2021. RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data |pdf=|usr=}}{{34320202}} gab.com Text RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data JO: Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=34320202 #FOAvip The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts. Here, we present RCA2, the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability). In addition, RCA2 provides a user-friendly framework incorporating multiple commonly used downstream analysis modules. RCA2 also provides new reference panels for human and mouse and supports generation of custom panels. Furthermore, RCA2 facilitates cell type-specific QC, which is essential for accurate clustering of data from heterogeneous tissues. We demonstrate the advantages of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Scalable supervised clustering methods such as RCA2 will facilitate unified analysis of cohort-scale SC datasets. Twit Text FOAVip RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data ????Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=34320202 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34320202 #FOAvip English Wikipedia <ref>{{Cite pmid|34320202}}</ref> RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data #MMPMID34320202 Schmidt F; Ranjan B; Lin QXX; Krishnan V; Joanito I; Honardoost MA; Nawaz Z; Venkatesh PN; Tan J; Rayan NA; Ong ST; Prabhakar S Nucleic Acids Res 2021[Sep]; 49 (15): 8505-8519 PMID34320202show ga The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts. Here, we present RCA2, the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability). In addition, RCA2 provides a user-friendly framework incorporating multiple commonly used downstream analysis modules. RCA2 also provides new reference panels for human and mouse and supports generation of custom panels. Furthermore, RCA2 facilitates cell type-specific QC, which is essential for accurate clustering of data from heterogeneous tissues. We demonstrate the advantages of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Scalable supervised clustering methods such as RCA2 will facilitate unified analysis of cohort-scale SC datasets. |*Algorithms[MESH] |*Cluster Analysis[MESH] |Animals[MESH] |Arthritis, Rheumatoid/genetics[MESH] |Bone Marrow Cells/metabolism[MESH] |COVID-19/blood/pathology[MESH] |Cohort Studies[MESH] |Datasets as Topic[MESH] |Humans[MESH] |Leukocytes, Mononuclear/metabolism/pathology[MESH] |Mice[MESH] |Organ Specificity[MESH] |Quality Control[MESH] |RNA, Small Cytoplasmic/*genetics[MESH] |RNA-Seq/*methods/standards[MESH] |Single-Cell Analysis/*methods/standards[MESH]RCA2: a scalable supervised clustering algorithm that reduces batch ef(epmc).pdf DeepDyve Pubget Overpricing