glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data.
glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data.
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DOI：
10.1093/bioinformatics/btaa1009
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发表时间：
2021-04-05
期刊：
影响因子：
--
通讯作者：
Huber W
中科院分区：
文献类型：
--
作者：
Ahlmann-Eltze C;Huber W
Gamma-Poisson分布是单细胞RNA测序计数的采样变异性的理论和经验激励模型，也是包括差异表达分析、主成分分析和因子分析在内的分析方法的基本构建块。从数据中推断其参数的现有实现通常难以处理单个细胞数据集的大小，其可以包括数百万个细胞;同时，它们没有充分利用零和其他小数字在数据中频繁出现的事实。这些限制阻碍了模型的采用，为统计上较差的方法（如对数（类）变换）留下了空间。我们提出了一个新的R包，用于将Gamma-Poisson分布拟合到具有现代单细胞数据集特征的数据，比现有方法更快，更准确。该软件可以处理磁盘上的数据，而不必同时将它们加载到RAM中。软件包glmGamPoi可从Bioconductor获得，适用于Windows、macOS和Linux，源代码可在github.com/const-ae/glmGamPoi上根据GPL-3许可证获得。复制本文结果的脚本可在github.com/const-ae/glmGamPoi-Paper上获得。 补充数据可在Bioinformatics在线获得。
The Gamma-Poisson distribution is a theoretically and empirically motivated model for the sampling variability of single cell RNA-sequencing counts and an essential building block for analysis approaches including differential expression analysis, principal component analysis and factor analysis. Existing implementations for inferring its parameters from data often struggle with the size of single cell datasets, which can comprise millions of cells; at the same time, they do not take full advantage of the fact that zero and other small numbers are frequent in the data. These limitations have hampered uptake of the model, leaving room for statistically inferior approaches such as logarithm(-like) transformation. We present a new R package for fitting the Gamma-Poisson distribution to data with the characteristics of modern single cell datasets more quickly and more accurately than existing methods. The software can work with data on disk without having to load them into RAM simultaneously. The package glmGamPoi is available from Bioconductor for Windows, macOS and Linux, and source code is available on github.com/const-ae/glmGamPoi under a GPL-3 license. The scripts to reproduce the results of this paper are available on github.com/const-ae/glmGamPoi-Paper. Supplementary data are available at Bioinformatics online.
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