Normalization is an important step in the analysis of single-cell RNA-seq data. While no single method outperforms all others in all datasets, the choice of normalization can have profound impact on the results. Data-driven metrics can be used to rank normalization methods and select the best performers. Here, we show how to use R/Bioconductor to calculate normalization factors, apply them to compute normalized data, and compare several normalization approaches. Finally, we briefly show how to perform downstream analysis steps on the normalized data.
Normalization of Single-Cell RNA-Seq Data
Davide Risso
2021
Abstract
Normalization is an important step in the analysis of single-cell RNA-seq data. While no single method outperforms all others in all datasets, the choice of normalization can have profound impact on the results. Data-driven metrics can be used to rank normalization methods and select the best performers. Here, we show how to use R/Bioconductor to calculate normalization factors, apply them to compute normalized data, and compare several normalization approaches. Finally, we briefly show how to perform downstream analysis steps on the normalized data.File in questo prodotto:
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