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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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Related Experiment Video

Updated: Oct 20, 2025

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

Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature

Wenjing Ma¹, Kenong Su¹, Hao Wu^2,3

¹Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, GA, 30322, USA.

|September 10, 2021

Summary

Supervised cell type identification in single-cell RNA sequencing (scRNA-seq) benefits from careful reference dataset construction. Combining datasets and using multi-layer perceptron (MLP) with F-test feature selection improves accuracy.

Keywords:

Reference dataset construction Supervised cell typing scRNA-seq

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Last Updated: Oct 20, 2025

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

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Published on: May 22, 2017

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Cell type identification is crucial for single-cell RNA sequencing (scRNA-seq) data analysis.
Supervised methods are increasingly popular due to their accuracy and performance.
Method performance depends on feature selection, prediction method, and reference dataset choice.

Purpose of the Study:

To systematically evaluate strategies for supervised cell identification in scRNA-seq data.
To provide guidelines for optimizing supervised cell typing methods.
To assess the impact of reference dataset characteristics and preprocessing on prediction accuracy.

Main Methods:

Benchmarking nine classifiers and six feature selection strategies.
Analyzing the effect of reference data size and cell type number.
Investigating the impact of dataset discrepancies, imputation, and batch correction.
Evaluating pooling and purifying reference data strategies.

Main Results:

Identified key factors influencing supervised cell identification accuracy.
Determined optimal strategies for reference dataset construction and classifier selection.
Quantified the effects of data preprocessing and discrepancies on prediction performance.

Conclusions:

Recommend pooling all available individuals for reference dataset construction.
Suggest using multi-layer perceptron (MLP) as the classifier.
Advise employing F-test for feature selection in supervised cell typing.