With the recent advance of sequencing technology, the collection of RNA expression (RNA-seq) data has been growing rapidly. RNA-seq data are statistically count-type measurements. Poisson distribution is a basic probability distribution for modeling count-type data. With Poisson regression models, various experimental factors, GC content as well as alternative splicing isoforms can be flexibly considered in RNA-seq data analysis. Due to the biochemical and technical limitations of sequencing technology, the biases among RNA-seq data have been recognized.
In this study, an artificial censoring approach has been proposed to an isoform-specific Poisson regression model for analyzing RNA-seq data. Low expression values can be grouped (censored) into one probability category, and high expression values can also be grouped (censored) into another probability category. We have implemented the related Newton-Raphson numeric computing procedure to achieve the maximum likelihood estimation for our censored-Poisson regression model. The related mathematical simplifications have been derived for the consideration of stable and convenient numerical computing.
The advantages of our artificial censoring approach have been demonstrated in both simulation studies and application analysis of experimental data.
Our proposed artificial censoring approach allows us to focus on the majority of data. As the extreme values (tails) of data are artificially censored, more efficient analysis results can be obtained, even from relatively simple Poisson regression models. Our proposed artificial censoring approach can certainly be considered for other well-developed models or methods for RNA-seq data analysis.
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RNA sequencing (RNA-seq) expression data have been increasingly collected for various biomedical studies. Due to the biochemical and technical limitations of sequencing technology, the biases among RNA-seq data have been recognized. We have developed an artificial censoring approach to the analysis of isoform-specific RNA-seq expression data. Low and high expression values can be grouped (censored) into the related probability categories. This approach allows us to focus on the majority of data and to obtain more efficient analysis results. Our proposed artificial censoring approach can also be considered in other RNA-seq data analysis scenarios.
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Chen, X., Lai, Y. A censored-Poisson model based approach to the analysis of RNA-seq data. Quant Biol (2020). https://doi.org/10.1007/s40484-020-0208-3
- Poisson models
- censored distribution