Normalization of Single-Cell RNA-Seq Data
Methods in Molecular Biology, ISSN: 1940-6029, Vol: 2284, Page: 303-329
2021
- 2Citations
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations2
- Citation Indexes2
- Captures8
- Readers8
Book Chapter Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85104161269&origin=inward; http://dx.doi.org/10.1007/978-1-0716-1307-8_17; http://www.ncbi.nlm.nih.gov/pubmed/33835450; https://link.springer.com/10.1007/978-1-0716-1307-8_17; https://dx.doi.org/10.1007/978-1-0716-1307-8_17; https://link.springer.com/protocol/10.1007/978-1-0716-1307-8_17
Springer Science and Business Media LLC
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