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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: Sep 28, 2025

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

Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data.

Wei Liu^1,2, Xu Liao², Yi Yang²

¹Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, 200062, China.

Nucleic Acids Research

|March 29, 2022

Summary

We developed Dimension-Reduction Spatial-Clustering (DR-SC), a unified framework for simultaneous dimension reduction and spatial clustering. DR-SC enhances biological feature extraction and improves spatial clustering accuracy in transcriptomics.

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Area of Science:

Computational biology
Bioinformatics
Spatial transcriptomics

Background:

Sequential dimension reduction and clustering may yield suboptimal results.
Low-dimensional embeddings might not align with inferred cluster labels.
Existing methods lack integrated analysis for spatial transcriptomics data.

Purpose of the Study:

To develop a novel computational method for simultaneous dimension reduction and spatial clustering.
To improve the accuracy and biological relevance of feature extraction in spatial transcriptomics.
To provide a unified framework for analyzing tissue organization and spatial relationships.

Main Methods:

Developed Dimension-Reduction Spatial-Clustering (DR-SC), a unified framework.
Integrated dimension reduction and spatial clustering using a latent hidden Markov random field model.
Employed an expectation-maximization algorithm with iterative conditional mode for efficient computation.

Main Results:

DR-SC achieved accurate spatial clustering and effective extraction of biologically informative features.
The method demonstrated superior performance compared to existing clustering and spatial clustering approaches.
DR-SC improved downstream trajectory inference and visualization.

Conclusions:

DR-SC offers a powerful, integrated approach for spatial transcriptomics analysis.
The method enhances the understanding of tissue spatial organization and cellular heterogeneity.
DR-SC provides a scalable and data-driven solution for complex biological data analysis.