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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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Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Related Experiment Video

Updated: Jul 12, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Single-cell and Spatial Transcriptomics Clustering with an Optimized Adaptive K-Nearest Neighbor Graph.

Jia Li^1,2, Yu Shyr^1,2, Qi Liu^1,2

¹Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.

Biorxiv : the Preprint Server for Biology

|October 31, 2023

Summary

This summary is machine-generated.

aKNNO accurately identifies both abundant and rare cell types in single-cell transcriptomics data. This novel method improves cell type discovery without compromising performance on common cell populations.

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Related Experiment Videos

Last Updated: Jul 12, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Published on: January 16, 2019

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

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Published on: December 1, 2023

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

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell and spatial transcriptomics are crucial for understanding cellular heterogeneity in complex tissues.
Unsupervised clustering is essential for defining cell types but struggles with identifying rare populations.
Existing methods for rare cell type detection often compromise performance on abundant cell types.

Conclusions:

aKNNO offers a robust solution for comprehensive cell type identification in single-cell and spatial transcriptomics.
The method effectively addresses the challenge of simultaneously discovering abundant and rare cell populations.
aKNNO enhances the precision of cellular landscape and anatomical structure characterization from transcriptomic data.