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10.1016/j.cell.2021.04.048 http://scihub22266oqcxt.onion/10.1016/j.cell.2021.04.048 34062119!8238499!34062119     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34062119&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\34062119.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Cell 2021 ; 184 (13): 3573-3587.e29 Nephropedia Template TP {{tp|p=34062119|t=2021. Integrated analysis of multimodal single-cell data |pdf=|usr=}}{{34062119}} gab.com Text Integrated analysis of multimodal single-cell data JO: Cell http://www.kidney.de/pget.php?&tw=1&pmid=34062119 #FOAvip The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity. Twit Text FOAVip Integrated analysis of multimodal single-cell data ????Cell http://www.kidney.de/pget.php?&tw=1&pmid=34062119 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34062119 #FOAvip English Wikipedia <ref>{{Cite pmid|34062119}}</ref> Integrated analysis of multimodal single-cell data #MMPMID34062119 Hao Y; Hao S; Andersen-Nissen E; Mauck WM 3rd; Zheng S; Butler A; Lee MJ; Wilk AJ; Darby C; Zager M; Hoffman P; Stoeckius M; Papalexi E; Mimitou EP; Jain J; Srivastava A; Stuart T; Fleming LM; Yeung B; Rogers AJ; McElrath JM; Blish CA; Gottardo R; Smibert P; Satija R Cell 2021[Jun]; 184 (13): 3573-3587.e29 PMID34062119show ga The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity. |3T3 Cells[MESH] |Animals[MESH] |COVID-19/immunology[MESH] |Cell Line[MESH] |Gene Expression Profiling/methods[MESH] |Humans[MESH] |Immunity/immunology[MESH] |Leukocytes, Mononuclear/immunology[MESH] |Lymphocytes/immunology[MESH] |Mice[MESH] |SARS-CoV-2/*immunology[MESH] |Sequence Analysis, RNA/methods[MESH] |Single-Cell Analysis/*methods[MESH] |Transcriptome/immunology[MESH]Integrated analysis of multimodal single-cell data (epmc).pdf DeepDyve Pubget Overpricing