背景:因子分析是分子生物学中高通量数据集的无监督降维的广泛使用的工具,最近提出的专门为空间转录组学数据设计的扩展。然而,这些方法期望(计数)矩阵作为数据输入,因此不能直接适用于单分子分辨率数据,它们是用基因注释的坐标列表的形式,并提供对亚细胞空间表达模式的洞察。为了解决这个问题,我们在这里提出FISHFactor,结合了空间优势的概率因子模型,具有泊松点过程似然性的非负面因素分析,以明确建模并解释单分子分辨率数据的性质。此外,FISHFactor在公共权重矩阵中的潜在大量单元格中共享信息,允许对跨细胞的因素进行一致的解释,并产生改进的潜在变量估计。
结果:我们将FISHFactor与现有的方法进行了比较,这些方法依赖于通过空间分级来聚合信息,并且无法将来自多个细胞的信息组合起来,并表明我们的方法可以在模拟数据上获得更准确的结果。我们证明了我们的方法是可扩展的,可以很容易地应用于大型数据集。最后,我们在真实数据集上证明,FISHOfactor能够以数据驱动的方式识别主要的亚细胞表达模式和空间基因簇。
背景:模型实现,数据模拟和实验脚本可在https://www下获得。github.com/bioFAM/FISHFactor.
背景:补充数据可在Bioinformatics在线获得。
Factor analysis is a widely used tool for unsupervised dimensionality reduction of high-throughput datasets in molecular biology, with recently proposed extensions designed specifically for spatial transcriptomics data. However, these methods expect (count) matrices as data input and are therefore not directly applicable to single molecule resolution data, which are in the form of coordinate lists annotated with genes and provide insight into subcellular spatial expression patterns. To address this, we here propose FISHFactor, a probabilistic factor model that combines the benefits of spatial, non-negative factor analysis with a Poisson point process likelihood to explicitly model and account for the nature of single molecule resolution data. In addition, FISHFactor shares information across a potentially large number of cells in a common weight matrix, allowing consistent interpretation of factors across cells and yielding improved latent variable estimates.
We compare FISHFactor to existing methods that rely on aggregating information through spatial binning and cannot combine information from multiple cells and show that our method leads to more accurate results on simulated data. We show that our method is scalable and can be readily applied to large datasets. Finally, we demonstrate on a real dataset that FISHFactor is able to identify major subcellular expression patterns and spatial gene clusters in a data-driven manner.
The model implementation, data simulation and experiment scripts are available under https://www.github.com/bioFAM/FISHFactor.