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SCnorm: robust normalization of single-cell RNA-seq data Normalization is an important first step in analyzing RNA-seq expression data to allow for accurate comparisons of a gene’s expression across samples Typically, normalization methods estimate a scale factor per sample to adjust for diferences in the amount of sequencing each sample receives
SCnorm: A quantile-regression based approach for robust To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data for in an effort to make expression counts comparable across genes and or samples samples1 In this work, we present a method for between-sample normalization, although specific features (Supplementary Section S1) RNA-seq experiments2,3
Christina Kendziorski - Biostatistics and Medical Informatics Research Grant to CK Title (R01): Statistical Methods for the Genomic Analysis of Gene Expression Data Principal Investigator: Christina Kendziorski Period: July 2006 - July 2011 Agency: National Institute of General Medical Sciences Annual Direct Costs: $175,000 00
SCnorm: robust normalization of single-cell RNA-seq data Normalization is an important first step in analyzing RNA-seq expression data to allow for accurate comparisons of a gene’s expression across samples Typically, normalization methods estimate a scale factor per sample to adjust for diferences in the amount of sequencing each sample receives