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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 17, 2026

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

Knowledge-Guided Gene Panel Selection for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective.

Meng Xiao, Weiliang Zhang, Xiaohan Huang

IEEE Transactions on Computational Biology and Bioinformatics

|September 15, 2025

Summary

This summary is machine-generated.

This study introduces a novel gene panel selection method using ensemble knowledge and reinforcement learning (RL) to improve genomic biomarker discovery in label-free datasets. The approach enhances precision and efficiency, advancing single-cell genomics analysis.

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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
Biomarker Discovery
Machine Learning

Background:

Gene panel selection is crucial for identifying genomic biomarkers in label-free data.
Traditional methods often suffer from bias and inefficiency, hindering biological signal detection.

Purpose of the Study:

To develop an improved iterative gene panel selection strategy.
To mitigate biases and enhance efficiency in biomarker discovery using label-free genomic datasets.

Main Methods:

Harnessing ensemble knowledge from existing gene selection algorithms for initial search space guidance.
Integrating reinforcement learning (RL) with expert-shaped reward functions for dynamic refinement.
Applying the method to label-free genomic datasets for biomarker identification.

Main Results:

Demonstrated improved precision and efficiency in gene panel selection.
Successfully identified informative genomic biomarkers.
Validated through comparative experiments, case studies, and downstream analyses.

Conclusions:

The proposed iterative strategy effectively addresses limitations of traditional gene panel selection methods.
This approach holds significant potential for advancing single-cell genomics data analysis.
Offers a more robust and adaptable solution for label-free biomarker discovery.