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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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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference

Yi-Xuan Xiong^1,2, Xiao-Fei Zhang^1,2

¹School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China.

Briefings in Bioinformatics

|March 4, 2024

Summary

Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT) accurately annotates cell types using multiple references and identifies novel cell types. This method enhances understanding of complex biological tissues.

Keywords:

cell-type annotation distance metric learning novel cell-type identification optimal transport single-cell RNA sequencing

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Published on: March 12, 2021

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

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) generates vast amounts of data for tissue analysis.
Accurate cell-type annotation is challenging, especially with multiple references and novel cell types.

Purpose of the Study:

Introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT).
Develop a method for integrating multiple reference datasets and identifying novel cell types.
Enhance the precision of cell-type annotation in scRNA-seq data.

Main Methods:

scDOT utilizes distance metric learning and optimal transport for a novel optimization framework.
Learns predictive power of reference datasets and establishes probabilistic cell-type mapping.
Develops an interpretable scoring system for identifying novel cell types.

Main Results:

scDOT demonstrates superior performance in cell-type annotation across diverse datasets.
Successfully identifies previously unseen cell types in benchmark datasets.
Experimental results validate scDOT's effectiveness in various tissues and technologies.

Conclusions:

scDOT offers a powerful tool for precise cell-type annotation in scRNA-seq data.
Facilitates the discovery of novel cell types, advancing biological understanding.
Improves the integration and analysis of multiple reference datasets.