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10.1093/bib/bbab281 http://scihub22266oqcxt.onion/10.1093/bib/bbab281 34308480!ä!34308480 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brief+Bioinform 2021 ; 22 (6): ä Nephropedia Template TP {{tp|p=34308480|t=2021. scAdapt: virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species |pdf=|usr=}}{{34308480}} gab.com Text scAdapt: virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34308480 #FOAvip In single cell analyses, cell types are conventionally identified based on expressions of known marker genes, whose identifications are time-consuming and irreproducible. To solve this issue, many supervised approaches have been developed to identify cell types based on the rapid accumulation of public datasets. However, these approaches are sensitive to batch effects or biological variations since the data distributions are different in cross-platforms or species predictions. In this study, we developed scAdapt, a virtual adversarial domain adaptation network, to transfer cell labels between datasets with batch effects. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier and aligned the labeled source centroids and pseudo-labeled target centroids to generate a joint embedding. The scAdapt was demonstrated to outperform existing methods for classification in simulated, cross-platforms, cross-species, spatial transcriptomic and COVID-19 immune datasets. Further quantitative evaluations and visualizations for the aligned embeddings confirm the superiority in cell mixing and the ability to preserve discriminative cluster structure present in the original datasets. Twit Text FOAVip scAdapt: virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34308480 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34308480 #FOAvip English Wikipedia <ref>{{Cite pmid|34308480}}</ref> scAdapt: virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species #MMPMID34308480 Zhou X; Chai H; Zeng Y; Zhao H; Yang Y Brief Bioinform 2021[Nov]; 22 (6): ä PMID34308480show ga In single cell analyses, cell types are conventionally identified based on expressions of known marker genes, whose identifications are time-consuming and irreproducible. To solve this issue, many supervised approaches have been developed to identify cell types based on the rapid accumulation of public datasets. However, these approaches are sensitive to batch effects or biological variations since the data distributions are different in cross-platforms or species predictions. In this study, we developed scAdapt, a virtual adversarial domain adaptation network, to transfer cell labels between datasets with batch effects. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier and aligned the labeled source centroids and pseudo-labeled target centroids to generate a joint embedding. The scAdapt was demonstrated to outperform existing methods for classification in simulated, cross-platforms, cross-species, spatial transcriptomic and COVID-19 immune datasets. Further quantitative evaluations and visualizations for the aligned embeddings confirm the superiority in cell mixing and the ability to preserve discriminative cluster structure present in the original datasets. |*Single-Cell Analysis[MESH] |COVID-19/*genetics/virology[MESH] |Exome Sequencing[MESH] |Humans[MESH] |RNA-Seq[MESH] |SARS-CoV-2/*genetics/isolation & purification[MESH] |Species Specificity[MESH]scAdapt: virtual adversarial domain adaptation network for single cell(epmc).pdf DeepDyve Pubget Overpricing