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Related Experiment Video

Updated: Jun 14, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Rarity: discovering rare cell populations from single-cell imaging data.

Kaspar Märtens¹, Michele Bortolomeazzi^2,3, Lucia Montorsi^2,3

¹The Alan Turing Institute, London NW1 2DB, United Kingdom.

Bioinformatics (Oxford, England)

|December 13, 2023

Summary

This summary is machine-generated.

Rarity, a new unsupervised clustering framework, enhances the discovery of rare cell types in single-cell data. It uses a Bayesian model to improve sensitivity and interpretability, overcoming limitations of typical methods.

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Last Updated: Jun 14, 2026

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Cell type identification is crucial for single-cell data analysis.
Unsupervised clustering aids in discovering known and unknown cell populations.
Rare cell types pose challenges due to weak expression signatures.

Purpose of the Study:

To develop a robust framework for identifying rare cell types in single-cell data.
To improve the consistency and interpretability of unsupervised clustering for rare cell discovery.
To address the limitations of typical unsupervised methods in detecting rare subpopulations.

Main Methods:

Developed a novel statistical framework named Rarity for unsupervised clustering.
Employed a Bayesian latent variable model for cell clustering.
Assigned cells to inferred latent binary on/off expression profiles.

Main Results:

Rarity enables more robust and consistent discovery of rare cell types.
The framework offers increased sensitivity to rare cell populations.
Rarity allows for control and interpretation of potential false positive discoveries.

Conclusions:

Rarity provides a powerful tool for identifying rare cell types in single-cell datasets.
The Bayesian approach enhances the detection and understanding of cellular heterogeneity.
The method is demonstrated on various imaging mass cytometry (IMC) datasets.