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Fast and precise single-cell data analysis using a hierarchical autoencoder

Computer Science

Fast and precise single-cell data analysis using a hierarchical autoencoder

D. Tran, H. Nguyen, et al.

Discover scDHA, an innovative single-cell Decomposition using Hierarchical Autoencoder framework developed by Duc Tran, Hung Nguyen, Bang Tran, Carlo La Vecchia, and Hung N. Luu. This powerful tool enhances scRNA-seq data analysis by effectively filtering noise and projecting significant data into a lower-dimensional space, surpassing existing techniques in visualization and classification.

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Playback language: English
Abstract
This paper introduces scDHA, a single-cell Decomposition using Hierarchical Autoencoder framework, designed to efficiently analyze scRNA-seq data by extracting representative information from each cell while mitigating noise. scDHA comprises two modules: a non-negative kernel autoencoder for removing insignificant genes and a stacked Bayesian autoencoder for projecting data onto a lower-dimensional space. The framework outperforms state-of-the-art techniques in cell segregation, visualization, classification, and pseudo-time inference.
Publisher
Nature Communications
Published On
Feb 15, 2021
Authors
Duc Tran, Hung Nguyen, Bang Tran, Carlo La Vecchia, Hung N. Luu, Tin Nguyen
Tags
single-cell analysis
scRNA-seq
noise reduction
autoencoder
data visualization
classification
pseudo-time inference

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