Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.
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DOI：
10.1093/bioinformatics/btab486
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发表时间：
2021-11-05
期刊：
影响因子：
5.8
通讯作者：
Rattray, Magnus
中科院分区：
文献类型：
--
作者：
BinTayyash, Nuha;Georgaka, Sokratia;John, S. T.;Ahmed, Sumon;Boukouvalas, Alexis;Hensman, James;Rattray, Magnus
负二项分布已被证明是大量和单细胞rna测序（RNA-seq）计数数据的良好模型。高斯过程（GP）回归为模拟基因表达的时间或空间变化提供了一种有用的非参数方法。然而，目前可用的GP回归方法实现负二项似然模型不能扩展到单细胞和空间转录组学产生的越来越大的数据集。GPcounts包实现了GP回归方法，用于使用负二项似然函数对计数数据进行建模。计算效率是通过使用变分贝叶斯推理实现的。GP函数通过对数联系函数来模拟负二项似然均值的变化，并通过极大似然来拟合离散参数。我们在模拟时间过程数据上验证了该方法，显示出比基于高斯或泊松似然的方法更好的性能来识别过度分散计数数据的变化。为了证明时间推理，我们将GPcounts应用于伪时间和分支推理后的单细胞RNA-seq数据集。为了证明空间推理，我们将gpcount应用于来自小鼠嗅球的数据，以识别空间可变基因，并比较两种已发表的GP方法。我们还提供了使用零膨胀负二项对额外辍学进行建模的选项。我们的研究结果表明，在简单的高斯和泊松似然不现实的情况下，GPcounts可以用来模拟时间和空间计数数据。GPcounts是使用Python中的GPflow库实现的，可以在https://github.com/ManchesterBioinference/GPcounts上获得再现本文所示结果所需的数据、代码和笔记本。本文使用的版本存档于https://doi.org/10.5281/zenodo.5027066。补充数据可在生物信息学网站获得。
The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics. The GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic. GPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. The version used for this paper is archived at https://doi.org/10.5281/zenodo.5027066. Supplementary data are available at Bioinformatics online.
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