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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: Jul 2, 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

scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data.

Xianxian Cai¹, Wei Zhang², Xiaoying Zheng³

¹School of Sciences, East China Jiaotong University, Nanchang, 330013, China.

Interdisciplinary Sciences, Computational Life Sciences

|February 18, 2024

Summary

This summary is machine-generated.

This study evaluates 20 unsupervised cell type identification methods for single-cell RNA sequencing (scRNA-seq) data. A new ensemble method, scEM, demonstrates superior performance in predicting cellular composition.

Keywords:

Clustering Comparative analysis Ensemble method scRNA-seq data

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Last Updated: Jul 2, 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

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

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

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

Published on: May 22, 2017

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) provides insights into cellular heterogeneity.
Numerous computational methods exist for cell type identification, but lack standardized comparison.
This hinders understanding of method strengths and weaknesses.

Purpose of the Study:

To comprehensively review and evaluate existing unsupervised cell type identification methods.
To propose and validate a novel ensemble method for improved cell type prediction.

Main Methods:

Reviewed 20 unsupervised cell type identification algorithms.
Evaluated methods on 24 diverse scRNA-seq datasets.
Developed scEM, an ensemble method using entropy weighting and the Louvain algorithm.

Main Results:

Conducted a comprehensive comparison of 20 methods across 24 datasets.
Demonstrated scEM's effectiveness compared to 11 other similarity-based methods.
scEM accurately predicts cellular type composition in scRNA-seq data.

Conclusions:

A standardized evaluation framework is crucial for scRNA-seq analysis tools.
The proposed scEM method offers a robust approach for cell type identification.
scEM advances the analysis of cellular composition and heterogeneity in scRNA-seq studies.