Sabeena M Rasheed, Lamees S Bakhsh, Reem S Alhameedi, Sharifah Mohidin
Perceived Stress Among Nurses at a Tertiary Care Teaching Hospital in Saudi Arabia During the COVID-19 Pandemic
Cureus
normalization critical step analysis singlecell rnasequencing scrnaseq datasets main goal make gene counts comparable within cells normalization methods must account technical biological variability numerous normalization methods developed addressing different sources dispersion making specific assumptions count data selection normalization method direct impact downstream analysis example differential gene expression cluster identification thus objective review guide reader making informed decision appropriate normalization method use aim first give overview different single cell sequencing platforms methods commonly used including isolation library preparation protocols next discuss inherent sources variability scrnaseq datasets describe categories normalization methods include examples also delineate imputation batcheffect correction methods furthermore describe datadriven metrics commonly used evaluate performance normalization methods also discuss common scrnaseq methods toolkits used integrated data analysis according correction performed normalization methods broadly classified within betweensample algorithms moreover respect mathematical model used normalization methods classified global scaling methods generalized linear models mixed methods machine learningbased methods methods depict pros cons make different statistical assumptions however better performing normalization method instead metrics silhouette width knearest neighbor batcheffect test highly variable genes recommended assess performance normalization methods