基于 MERFISH 数据的单细胞转录表达分析的贝叶斯模型。
A Bayesian model for single cell transcript expression analysis on MERFISH data.
机构信息
Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
出版信息
Bioinformatics. 2019 Mar 15;35(6):995-1001. doi: 10.1093/bioinformatics/bty718.
MOTIVATION
Multiplexed error-robust fluorescence in-situ hybridization (MERFISH) is a recent technology to obtain spatially resolved gene or transcript expression profiles in single cells for hundreds to thousands of genes in parallel. So far, no statistical framework to analyze MERFISH data is available.
RESULTS
We present a Bayesian model for single cell transcript expression analysis on MERFISH data. We show that the model successfully captures uncertainty in MERFISH data and eliminates systematic biases that can occur in raw RNA molecule counts obtained with MERFISH. Our model accurately estimates transcript expression and additionally provides the full probability distribution and credible intervals for each transcript. We further show how this enables MERFISH to scale towards the whole genome while being able to control the uncertainty in obtained results.
AVAILABILITY AND IMPLEMENTATION
The presented model is implemented on top of Rust-Bio (Köster, 2016) and available open-source as MERFISHtools (https://merfishtools.github.io). It can be easily installed via Bioconda (Grüning et al., 2018). The entire analysis performed in this paper is provided as a fully reproducible Snakemake (Köster and Rahmann, 2012) workflow via Zenodo (https://doi.org/10.5281/zenodo.752340).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
多重稳健荧光原位杂交（MERFISH）是一种最新的技术，可以在单细胞中同时获得数百到数千个基因的空间分辨基因或转录物表达谱。到目前为止，还没有用于分析 MERFISH 数据的统计框架。
结果
我们提出了一种用于 MERFISH 数据的单细胞转录物表达分析的贝叶斯模型。我们表明，该模型成功地捕获了 MERFISH 数据中的不确定性，并消除了 MERFISH 获得的原始 RNA 分子计数中可能出现的系统偏差。我们的模型准确地估计了转录物的表达，并且还提供了每个转录物的完整概率分布和可信区间。我们进一步展示了这如何使 MERFISH 能够扩展到整个基因组，同时能够控制获得结果的不确定性。
可用性和实现
所提出的模型是在 Rust-Bio（Köster，2016）之上实现的，并作为 MERFISHtools（https://merfishtools.github.io）开源提供。它可以通过 Bioconda（Grüning 等人，2018）轻松安装。本文中执行的整个分析都通过 Zenodo（https://doi.org/10.5281/zenodo.752340）以完全可重复的 Snakemake（Köster 和 Rahmann，2012）工作流程提供。
补充信息
补充数据可在《生物信息学》在线获得。
Zotero 插件