Normalisation remains a critical and continuously evolving aspect of pre-processing scRNA-seq data supported by scater. With judicious exploratory data analysis as part of the pre-processing, quality control and normalisation of scRNA-seq data using tools in scater, important known covariates can be identified. Simple size-factor normalisation methods, including the single-cell specific methods in the scran package, are seamlessly integrated into a scater workflow. Methods for correcting for batch effects and removing unwanted variation are supported both with internal methods and through harmonious integration with a multitude of tools available in the R/Bioconductor environment. Once identified, important covariates and latent variables can be flagged for inclusion in downstream statistical models or their effects regressed out of normalised expression values.
Some of the sentences are a bit redundant, suggest:
Normalisation is a critical aspect of scRNA-seq data processing that is supported by scater. Scaling normalisation methods, including the single-cell specific methods in the scran package, are seamlessly integrated into a scater workflow. Methods for identifying and removing batch effects and other types of unwanted variation are supported both with internal methods and through integration with a multitude of tools available in the R/Bioconductor environment. Once identified, important covariates and latent variables can be flagged for inclusion in downstream statistical models or their effects regressed out of normalised expression values.
davismcc
commented
8 years ago
Some of the sentences are a bit redundant, suggest: