Pretty interesting @PeerJPreprints article about normalizing microbiome sequencing count data. In addition to the content, I think the article is well written/organized and I appreciated their discussion points. :) https://t.co/nB4mCXteKE
GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data https://t.co/GsqeRdCO87
Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current R…
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Cite this article
Chen L, Reeve J, Zhang L, Huang S, Wang X, Chen J.2018. GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ Preprints6:e3417v3https://doi.org/10.7287/peerj.preprints.3417v3
Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero inflation remain largely undeveloped. Here we propose GMPR - a simple but effective normalization method - for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.
We have performed more simulations, re-analyzed the data, and added more discussions.