https://github.com/rhondabacher/SCnorm

Normalization for single cell RNA-seq data
https://github.com/rhondabacher/SCnorm

Last synced: 23 days ago
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Normalization for single cell RNA-seq data

• Host: GitHub
• URL: https://github.com/rhondabacher/SCnorm
• Owner: rhondabacher
• Created: 2016-11-12T18:43:57.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-06-21T18:36:33.000Z (about 1 year ago)
• Last Synced: 2024-04-24T16:04:42.454Z (2 months ago)
• Language: R
• Size: 41.3 MB
• Stars: 45
• Watchers: 3
• Forks: 9
• Open Issues: 14
• Metadata Files:
  • Readme: README.md

Lists

• awesome_single_cell - SCnorm - [R] - A quantile regression based approach for robust normalization of single cell RNA-seq data. (Software packages / RNA-seq)
• awesome-single-cell - SCnorm - [R] - A quantile regression based approach for robust normalization of single cell RNA-seq data. (Software packages / Count modelling and normalization)
• awesome-single-cell - SCnorm - [R] - A quantile regression based approach for robust normalization of single cell RNA-seq data. (Software packages / RNA-seq)

README

        
# SCnorm: robust normalization of single-cell RNA-seq data

SCnorm is an R package which can be used to normalize single-cell RNA-seq data. SCnorm is on Bioconductor and instructions for download may be found at [https://bioconductor.org/packages/release/bioc/html/SCnorm.html](https://bioconductor.org/packages/release/bioc/html/SCnorm.html).

#### Can you use SCnorm on your 10X (or very sparse) dataset?

SCnorm is not intended for datasets with more than ~80% zero counts, often K will not converge in these situations. 

Setting the FilterExpression parameter to 1 or 2 may help, but is not a guarantee. 

It may also be helpful to use the ditherCounts = TRUE parameter for sparse UMI based data which may contain numerous tied counts (counts of 1 and 2 for example).

Additional details and a FAQ for SCnorm are described in the vignette: [SCnorm Vignette](https://bioconductor.org/packages/release/bioc/vignettes/SCnorm/inst/doc/SCnorm.pdf)

## Installation

Make sure to use the latest version of SCnorm by installing it from BioConductor or GitHub. 

```R

if (!requireNamespace("BiocManager", quietly=TRUE))

    install.packages("BiocManager")

BiocManager::install("SCnorm")

```

or

```R

library(devtools)

devtools::install_github("rhondabacher/SCnorm")

```

To access a version of SCnorm that does not require R version >= 3.4 you can 

download SCnorm from the development branch here: 

[https://github.com/rhondabacher/SCnorm/tree/devel](https://github.com/rhondabacher/SCnorm/tree/devel)

## Author

Rhonda Bacher 

## Cite

Please cite SCnorm if you use it in your research:

> Bacher R, Chu LF, Leng N, Gasch AP, Thomson JA, Stewart RM, Newton M, 

Kendziorski C. SCnorm: robust normalization of single-cell

RNA-seq data. Nature Methods. 2017 Jun 1;14(6):584-6.

[https://www.nature.com/nmeth/journal/v14/n6/full/nmeth.4263.html](https://www.nature.com/nmeth/journal/v14/n6/full/nmeth.4263.html)

## Latest Update: v1.5.7

Added additional warnings when using the useSpikes option and SCnorm now supports input data in the sparse matrix format.

#### Previous updates: v1.5.4

Allow separate normalizations for different Conditions for very large dataset. See vignette for example.