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10.1186/s13059-022-02605-1 http://scihub22266oqcxt.onion/10.1186/s13059-022-02605-1 35073995!8784862!35073995     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genome+Biol 2022 ; 23 (1): 33 Nephropedia Template TP {{tp|p=35073995|t=2022. IDEAS: individual level differential expression analysis for single-cell RNA-seq data |pdf=|usr=}}{{35073995}} gab.com Text IDEAS: individual level differential expression analysis for single-cell RNA-seq data JO: Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=35073995 #FOAvip We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms. Twit Text FOAVip IDEAS: individual level differential expression analysis for single-cell RNA-seq data ????Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=35073995 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35073995 #FOAvip English Wikipedia <ref>{{Cite pmid|35073995}}</ref> IDEAS: individual level differential expression analysis for single-cell RNA-seq data #MMPMID35073995 Zhang M; Liu S; Miao Z; Han F; Gottardo R; Sun W Genome Biol 2022[Jan]; 23 (1): 33 PMID35073995show ga We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms. |*Software[MESH] |Autistic Disorder/*genetics/metabolism/pathology[MESH] |COVID-19/*genetics/metabolism/pathology/virology[MESH] |Case-Control Studies[MESH] |Exome Sequencing[MESH] |Gene Expression Profiling[MESH] |Gene Expression Regulation[MESH] |Humans[MESH] |Microglia/metabolism/pathology[MESH] |Nerve Tissue Proteins/classification/genetics/metabolism[MESH] |SARS-CoV-2/pathogenicity[MESH] |Sequence Analysis, RNA/*methods[MESH] |Severity of Illness Index[MESH]IDEAS: individual level differential expression analysis for single-ce(epmc).pdf DeepDyve Pubget Overpricing