ArticleBioinformatics (Oxford, England)2019
A Bayesian model for single cell transcript expression analysis on MERFISH data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Recovering Spatially-Varying Cell-Specific Gene Co-expression Networks for Single-Cell Spatial Expression Data.Frontiers in genetics · 2021Article
- Clustering and classification methods for single-cell RNA-sequencing data.Briefings in bioinformatics · 2020Review
- Eleven grand challenges in single-cell data science.Genome biology · 2020Review
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
motivationMultiplexed 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.
resultsWe 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.
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