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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction.

Shuchang Zhao^1,2, Li Zhang^1,3, Xuejun Liu^1,2

¹MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106 China.

Frontiers of Computer Science

|November 2, 2022

Summary

This study introduces AE-TPGG, a novel autoencoder model for single-cell RNA sequencing (scRNA-seq) data. It effectively addresses technical noise and dropout events, improving downstream analysis of cellular diversity.

Keywords:

TPGG autoencoder data imputation dimensionality reduction scRNA-seq

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Last Updated: Aug 23, 2025

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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
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) offers high-throughput transcriptomic insights, revealing cellular diversity beyond bulk RNA-seq.
scRNA-seq data often suffers from technical noise and high dropout events due to low starting material, hindering analysis.
Existing normalized scRNA-seq data exhibits bimodal and right-skewed distributions, posing challenges for standard analytical methods.

Purpose of the Study:

To develop an advanced computational model for analyzing single-cell RNA sequencing data.
To address the challenges of technical noise, dropout events, and complex data distributions in scRNA-seq.
To improve the accuracy and robustness of downstream analyses in single-cell transcriptomics.

Main Methods:

Proposed a customized autoencoder model named AE-TPGG (Autoencoder with Two-Part-Generalized-Gamma distribution).
Integrated a two-part model to handle mixed discrete-continuous random variables inherent in scRNA-seq data.
Utilized the generalized gamma (GG) distribution to fit positive and right-skewed continuous gene expression data.
Employed autoencoder architecture to capture inter-gene relationships and perform denoised imputation.

Main Results:

The AE-TPGG model effectively captures inherent relationships between genes within scRNA-seq data.
Demonstrated competitive performance compared to existing imputation methods on real scRNA-seq datasets.
Successfully reduced technical noise and imputed missing gene expression values, ameliorating downstream analyses.

Conclusions:

The AE-TPGG model provides a robust and effective approach for scRNA-seq data analysis, particularly for handling noise and dropouts.
The model's ability to handle complex data distributions and perform denoised imputation enhances the interpretation of cellular heterogeneity.
AE-TPGG offers a valuable tool for advancing transcriptomic studies and understanding cellular diversity.