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10.1038/s41587-021-01186-x http://scihub22266oqcxt.onion/10.1038/s41587-021-01186-x 35228707!10015653!35228707     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Biotechnol 2022 ; 40 (5): 681-691 Nephropedia Template TP {{tp|p=35228707|t=2022. Multiscale PHATE identifies multimodal signatures of COVID-19 |pdf=|usr=}}{{35228707}} gab.com Text Multiscale PHATE identifies multimodal signatures of COVID-19 JO: Nat Biotechnol http://www.kidney.de/pget.php?&tw=1&pmid=35228707 #FOAvip As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16(hi)CD66b(lo) neutrophil and IFN-gamma(+) granzyme B(+) Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables. Twit Text FOAVip Multiscale PHATE identifies multimodal signatures of COVID-19 ????Nat Biotechnol http://www.kidney.de/pget.php?&tw=1&pmid=35228707 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35228707 #FOAvip English Wikipedia <ref>{{Cite pmid|35228707}}</ref> Multiscale PHATE identifies multimodal signatures of COVID-19 #MMPMID35228707 Kuchroo M; Huang J; Wong P; Grenier JC; Shung D; Tong A; Lucas C; Klein J; Burkhardt DB; Gigante S; Godavarthi A; Rieck B; Israelow B; Simonov M; Mao T; Oh JE; Silva J; Takahashi T; Odio CD; Casanovas-Massana A; Fournier J; Farhadian S; Dela Cruz CS; Ko AI; Hirn MJ; Wilson FP; Hussin JG; Wolf G; Iwasaki A; Krishnaswamy S Nat Biotechnol 2022[May]; 40 (5): 681-691 PMID35228707show ga As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16(hi)CD66b(lo) neutrophil and IFN-gamma(+) granzyme B(+) Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables. |*COVID-19[MESH] |*Single-Cell Analysis/methods[MESH] |Chromatin[MESH] |Exome Sequencing[MESH] |Humans[MESH]Multiscale PHATE identifies multimodal signatures of COVID-19 (epmc).pdf DeepDyve Pubget Overpricing