Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1016/j.crmeth.2021.100056 http://scihub22266oqcxt.onion/10.1016/j.crmeth.2021.100056 35475142!9017149!35475142     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cell+Rep+Methods 2021 ; 1 (4): 100056 Nephropedia Template TP {{tp|p=35475142|t=2021. Improved integration of single-cell transcriptome and surface protein expression by LinQ-View |pdf=|usr=}}{{35475142}} gab.com Text Improved integration of single-cell transcriptome and surface protein expression by LinQ-View JO: Cell Rep Methods http://www.kidney.de/pget.php?&tw=1&pmid=35475142 #FOAvip Multimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells). Twit Text FOAVip Improved integration of single-cell transcriptome and surface protein expression by LinQ-View ????Cell Rep Methods http://www.kidney.de/pget.php?&tw=1&pmid=35475142 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35475142 #FOAvip English Wikipedia <ref>{{Cite pmid|35475142}}</ref> Improved integration of single-cell transcriptome and surface protein expression by LinQ-View #MMPMID35475142 Li L; Dugan HL; Stamper CT; Lan LY; Asby NW; Knight M; Stovicek O; Zheng NY; Madariaga ML; Shanmugarajah K; Jansen MO; Changrob S; Utset HA; Henry C; Nelson C; Jedrzejczak RP; Fremont DH; Joachimiak A; Krammer F; Huang J; Khan AA; Wilson PC Cell Rep Methods 2021[Aug]; 1 (4): 100056 PMID35475142show ga Multimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells). |*COVID-19/genetics[MESH] |*Transcriptome/genetics[MESH] |Cluster Analysis[MESH] |Humans[MESH] |Membrane Proteins[MESH] |SARS-CoV-2/genetics[MESH] |Sequence Analysis, RNA/methods[MESH]Improved integration of single-cell transcriptome and surface protein (epmc).pdf DeepDyve Pubget Overpricing