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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: Jan 2, 2026

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

scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data.

Jose Alquicira-Hernandez^1,2, Anuja Sathe^3,4, Hanlee P Ji^3,4

¹Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia. j.alquicira@garvan.org.au.

|December 13, 2019

Summary

This summary is machine-generated.

scPred accurately classifies single cells using transcriptional profiles. This machine learning method enhances cell type identification from single-cell RNA sequencing data across various tissues.

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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:

Single-cell RNA sequencing (scRNA-seq) allows detailed cell type characterization.
Identifying cell-specific transcriptional signatures is crucial for cell classification.
Current methods may lack generalizability or accuracy in classifying individual cells.

Purpose of the Study:

To introduce scPred, a novel and generalizable computational method for accurate single-cell classification.
To leverage machine learning for predicting cell identity based on transcriptional profiles.
To provide a robust tool for analyzing scRNA-seq data.

Main Methods:

scPred employs unbiased feature selection within a reduced-dimension space.
It utilizes a machine-learning-based, probability prediction approach.
The method was applied to diverse scRNA-seq datasets, including pancreatic tissue, mononuclear cells, colorectal tumors, and dendritic cells.

Main Results:

scPred demonstrated high accuracy in classifying individual cells across multiple tissue types.
The method proved generalizable to various biological contexts.
Successful application to pancreatic, mononuclear, colorectal tumor, and dendritic cell datasets was shown.

Conclusions:

scPred offers a highly accurate and generalizable solution for single-cell classification.
The method effectively identifies cell types and states from scRNA-seq data.
The scPred tool is publicly available for broader research application.