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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 25, 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

SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor.

Dario Righelli¹, Lukas M Weber², Helena L Crowell^3,4

¹Department of Statistical Sciences, University of Padova, 35121 Padova, Italy.

Bioinformatics (Oxford, England)

|April 28, 2022

Summary

This summary is machine-generated.

SpatialExperiment is a new R/Bioconductor data infrastructure for spatial transcriptomics. It offers modularity and interoperability, with example data and tools for Visium and seqFISH platforms.

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Last Updated: Sep 25, 2025

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Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Spatially-resolved transcriptomics generates complex data.
Existing infrastructure may lack standardization and interoperability.

Purpose of the Study:

Introduce SpatialExperiment, a novel data infrastructure for spatial transcriptomics.
Demonstrate its utility with 10x Visium and seqFISH data.
Provide accessible example datasets and visualization tools.

Main Methods:

Implementation within the R/Bioconductor framework.
Demonstration using 10x Genomics Visium and seqFISH platforms.
Utilizing STexampleData, TENxVisiumData, and ggspavis packages.

Main Results:

SpatialExperiment provides a modular and interoperable framework.
Standardized operations and comprehensive documentation are key advantages.
Example datasets and visualization tools are readily available.

Conclusions:

SpatialExperiment offers a robust infrastructure for spatial transcriptomics data analysis.
The associated packages enhance accessibility and usability for researchers.
Facilitates standardized and reproducible research in the field.