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ScaiDigest Volume 6: Variational autoencoders (VAEs) in biology

ScaiDigest Volume 6: Variational autoencoders (VAEs) in biology

ScaiDigest Volume 6: Variational autoencoders (VAEs) in biology

ScaiDigest Volume 6: Variational autoencoders (VAEs) in biology

Unlocking Insights with Variational Autoencoders (VAEs)

 

Welcome to the 6th edition of ScaiDigest, where we explore cutting-edge research in single-cell analysis. In this edition, Sukalp Muzumdar, Senior Data Scientist at Scailyte, delves into the transformative role of Variational Autoencoders (VAEs) in biology. VAEs are revolutionizing single-cell data analysis, powering tools that enable comprehensive multi-omic data integration, data augmentation, and much more.

 

Advancing Single-Cell Analysis

Discover how VAE-based tools are expanding their reach to cover various single-cell biological data types, including flow cytometry, single-cell Hi-C, and spatial omics. These tools offer nuanced insights into cellular heterogeneity while accounting for technical effects. Explore Sukalp’s insights in this edition of ScaiDigest and stay at the forefront of single-cell analysis.

 

Variational autoencoders (VAEs) are driving a mini-revolution of sorts in the field of single-cell data analysis. Today, they are employed in a variety of applications in the field, ranging from the integration of multimodal data (as seen in tools like multiVI and totalVI), to data augmentation for training neural networks (as in CeLEry).

Generally, these tools build upon the foundations laid by methods like scVI which uses deep neural networks within a Bayesian framework to enable the probabilistic modeling of single-cell transcriptomics data. Developed by the same group behind scVI, totalVI is a framework for the end-to-end joint analysis of multi-omic CITE-seq data, using a probabilistic model to represent the data as a composite of biological and technical (e.g. batch) factors. It is able to handle a diverse array of tasks common in single-cell analysis such as dimensionality reduction, dataset integration, and differential expression testing, thereby offering a comprehensive solution for analyzing single-cell multi-omic data.

A recent example of a novel tool leveraging the unique capabilities of variational autoencoders is scPoli, a semi-supervised conditional VAE that can learn sample and cell representations of single-cell data, for use in data integration, label transfer, and atlas mapping. scPoli can also perform multi-scale analysis, offering insights into both cell-level and sample-level data representations – crucial for going beyond single-cell integration, and actually performing sample annotation.

Tools such as flowVI (flow cytometry), scVI-3D (single-cell Hi-C), and SIMVI (spatial omics) have extended the application of these techniques to cover other common types of single-cell biological data as well. flowVI in particular, allows for both the imputation of missing data, but also for the integration of multiple datasets.

VAE-based tools are thus playing a major role in advancing the field of single-cell analysis by enabling comprehensive and nuanced interpretations of cellular heterogeneity and function, while simultaneously accounting for technical effects, putting them in a position to help significantly advance our understanding of complex biological systems. 

Articles mentioned in this ScaiDigest:

Deep generative modeling for single-cell transcriptomics

Joint probabilistic modeling of single-cell multi-omic data with totalVI

MultiVI: deep generative model for the integration of multimodal data

Population-level integration of single-cell datasets enables multi-scale analysis across samples

Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry

flowVI: Flow Cytometry Variational Inference

Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D

SIMVI reveals intrinsic and spatial-induced states in spatial omics data

 

About Scailyte

Scailyte is an ETH Zürich spin-off with a best-in-class artificial intelligence platform for the discovery of complex disease patterns from single-cell data. Our solution provides unprecedented insight into the disease and patients’ biology and enables the discovery of new clinically-relevant biomarker signatures by uncovering human’s hidden “single-cell” secrets. 

Scailyte’s proprietary best-in-class data analysis platform ScaiVision™ associates multimodal single-cell datasets (RNA-/TCR-/BCR-seq, proteomics, etc.) with clinical endpoints, such as disease diagnosis, progression, severity, treatment response, and toxicity response to identify ultra-sensitive biomarker signatures and cell functionality states. The performance and clinically-relevant applications of Scailyte’s platform ScaiVision have been demonstrated in well established CAR-T cell therapies and various clinical projects in Oncology and Immunology.

For more information, visit www.scailyte.com and connect on social media @LinkedIn and @Twitter.

ScailyteTM and ScaiVisionTM are registered trademarks proprietary to Scailyte AG.

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