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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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Updated: Jun 18, 2025

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

Inferring Novel Cells in Single-Cell RNA-Sequencing Data.

Ziyi Li¹, Peng Yang²

¹Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. zli16@mdanderson.org.

Methods in Molecular Biology (Clifton, N.J.)

|July 27, 2024

Summary

Single-cell RNA-sequencing (scRNA-seq) enables gene expression analysis. This study presents three novel methods for inferring new cell types from scRNA-seq data, aiding biological discovery.

Keywords:

Copy number variation Novel cell detection Single-cell RNA-seq Supervised annotation Unsupervised clustering

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Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

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Last Updated: Jun 18, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

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Published on: October 12, 2018

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

Area of Science:

Computational Biology
Genomics
Molecular Biology

Background:

Single-cell RNA-sequencing (scRNA-seq) reveals cellular heterogeneity.
Identifying novel cell types is crucial but challenging with current methods.
Bulk RNA-sequencing often misses rare or novel cell populations.

Purpose of the Study:

To present and categorize methods for inferring novel cell types from scRNA-seq data.
To address the limitations of existing cell type identification techniques.
To provide practical guidance and code for applying these inference methods.

Main Methods:

Unsupervised and outlier-detection-based approaches for novel cell inference.
Supervised and semi-supervised learning strategies for cell type identification.
Copy number variation (CNV)-based methods for detecting cellular novelty.

Main Results:

Categorization of three distinct lines of methods for novel cell inference.
Discussion of the applicability of each method in different analytical scenarios.
Provision of implementation code and usage examples for practical application.

Conclusions:

The described methods offer a framework for robust novel cell inference in scRNA-seq analysis.
These approaches enhance the discovery of previously uncharacterized cell types and subpopulations.
The provided resources facilitate the routine application of advanced cell inference techniques.