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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 9, 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

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq

Qingyang Yin¹, Liang Chen¹

¹Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States.

Briefings in Bioinformatics

|December 7, 2023

Summary

This summary is machine-generated.

CellTICS, a new machine learning method, accurately identifies cell types by prioritizing marker genes and biological pathways. It offers biological insights into cell identity and heterogeneity, even revealing novel pathways and expression variability.

Keywords:

cell type identification explainable neural network pathways scRNA-seq data

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Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Accurate cell type identification is essential for understanding organismal function.
Current machine learning methods often lack biological interpretability, limiting their utility.
Understanding the biological basis of cell type definition is critical.

Purpose of the Study:

To develop a biologically interpretable machine learning method for cell type identification.
To uncover the underlying biological pathways that define cell types and their heterogeneity.
To improve prediction accuracy in cell type classification.

Main Methods:

CellTICS prioritizes cell-type-specific marker genes.
Utilizes a hierarchy of biological pathways for neural network construction.
Employs a multi-predictive-layer strategy for cell and sub-cell type prediction.

Main Results:

CellTICS generally outperforms existing methods in prediction accuracy.
Identifies key biological pathways defining cell types under various physiological conditions (e.g., disease, aging).
Reveals novel pathways and highlights the role of expression variability and stochasticity in cell type characterization.

Conclusions:

CellTICS provides a biologically interpretable approach to cell type identification and characterization.
The method sheds light on pathways driving cellular heterogeneity and organismal function.
Offers insights into the significance of expression stochasticity within pathways for cell type identity.