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A novel Bayesian approach for the scaling and inference of scRNA-seq counts, providing imputation of missing values and true counts recovery of data. BayNorm provides a simple and integrated solution to remove the technical biases of scRNA-seq approaches, whilst enabling robust and accurate detection of cell-specific changes in gene expression.

  • Applicable to both UMI and non-UMI containing data.
  • Particularly useful for quantitative analysis of more difficult scRNA-seq datasets such as those from small quiescent cells or microbes.
  • Option to taylor bayNorm priors based on phenotypic information on cell subpopulations.

Figure: bayNorm normalisation reduces experimental batch effects.

Taken from https://www.biorxiv.org/content/biorxiv/early/2018/08/03/384586.full.pdf

 

The bayNorm R package can be downloaded via the following link (Bioconductor):

https://bioconductor.org/packages/release/bioc/html/bayNorm.html

For more information about bayNorm please see the following references:

https://www.biorxiv.org/content/biorxiv/early/2018/08/03/384586.full.pdf

 

Internal case number: 8156

BayNorm

Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing (scRNA-seq) data

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