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An introduction to representation learning for single-cell data analysis.

Ihuan Gunawan^1,2, Fatemeh Vafaee^3,4, Erik Meijering²

¹School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.

Cell Reports Methods

|September 6, 2023

Summary

This summary is machine-generated.

Representation learning methods help analyze complex single-cell data by reducing dimensions. This guide aids researchers in selecting and optimizing these powerful tools for cell heterogeneity studies.

Keywords:

deep learning dimension reduction hyperparameter manifold learning omics systems microscopy

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Area of Science:

Single-cell biology
Systems biology
Computational biology

Background:

Single-cell omics and imaging generate high-dimensional data, revealing cell population heterogeneity.
Representation learning methods are crucial for analyzing and interpreting this complex data.
Numerous representation learning approaches exist, necessitating guidance for effective application.

Purpose of the Study:

To provide an overview and contrast of representation learning methods for single-cell data.
To guide researchers in selecting, applying, and optimizing these methods.
To highlight key steps and interdependencies in the representation learning workflow.

Main Methods:

Contrasting statistical, manifold learning, and neural network-based representation learning approaches.
Examining critical steps: data pre-processing, hyperparameter optimization, downstream analysis, and biological validation.
Discussing interdependencies within the representation learning workflow.

Main Results:

Representation learning facilitates the interpretation of cell heterogeneity structures, dynamics, and regulation.
A variety of methods exist, each with specific strengths for different single-cell data types.
Understanding the workflow is key to successful application and optimization.

Conclusions:

Representation learning is a vital tool for dissecting single-cell heterogeneity.
This overview serves as a guide for researchers navigating representation learning strategies.
Effective application of these methods is essential for advancing single-cell research.