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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH
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A Bayesian model for single cell transcript expression analysis on MERFISH data.

Johannes Köster1,2,3, Myles Brown2,4,5, X Shirley Liu4,6,7

  • 1Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.

Bioinformatics (Oxford, England)
|March 16, 2019
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Summary
This summary is machine-generated.

A new Bayesian model analyzes Multiplexed Error-Robust Fluorescence In-Situ Hybridization (MERFISH) data, improving transcript expression accuracy and enabling whole-genome analysis with controlled uncertainty.

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Area of Science:

  • Single-cell genomics
  • Spatial transcriptomics
  • Bioinformatics

Background:

  • Multiplexed Error-Robust Fluorescence In-Situ Hybridization (MERFISH) enables high-throughput, spatially resolved transcriptomics.
  • Existing MERFISH analysis lacks a robust statistical framework to handle data uncertainty and biases.

Purpose of the Study:

  • To develop and present a Bayesian statistical model for analyzing MERFISH data.
  • To address the need for accurate transcript expression quantification and uncertainty estimation in MERFISH.

Main Methods:

  • Development of a Bayesian model tailored for MERFISH data.
  • Implementation of the model using Rust-Bio and open-source MERFISHtools.
  • Utilizing Snakemake for reproducible analysis workflows.

Main Results:

  • The Bayesian model effectively captures uncertainty inherent in MERFISH data.
  • Systematic biases in raw RNA molecule counts are successfully eliminated.
  • Accurate transcript expression estimation, including full probability distributions and credible intervals, is achieved.
  • The model facilitates scaling MERFISH analysis towards whole-genome studies with controlled uncertainty.

Conclusions:

  • The presented Bayesian model provides a crucial statistical framework for MERFISH data analysis.
  • This framework enhances the reliability and scalability of MERFISH for comprehensive transcriptomic studies.
  • MERFISHtools offers an accessible and reproducible solution for researchers in spatial transcriptomics.