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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Jan 9, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Orchestrating Spatial Transcriptomics Analysis with Bioconductor.

Helena L Crowell¹, Yixing Dong^2,3, Ilaria Billato⁴

¹National Center for Genomic Analysis, Barcelona, Spain.

Biorxiv : the Preprint Server for Biology

|December 3, 2025

Summary

This summary is machine-generated.

This study offers an open-source online book for analyzing spatial omics data. It provides reproducible R code examples for spatial transcriptomics workflows, enhancing data analysis accessibility.

Keywords:

Bioconductor R computational biology data analysis gene expression high-dimensional data interoperability open-source reproducible research spatial omics spatial transcriptomics spatially-resolved transcriptomics workflow

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Last Updated: Jan 9, 2026

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics technologies offer spatially-resolved gene expression data.
These technologies generate complex and variable data, requiring sophisticated computational analysis.
Existing analysis workflows often involve multiple computational methods and software packages.

Purpose of the Study:

To provide a comprehensive, accessible, and reproducible resource for spatial omics data analysis.
To demonstrate the application of Bioconductor in R for spatial transcriptomics.
To facilitate the integration of spatial omics data analysis with Python.

Main Methods:

Development of a freely accessible, open-source online book.
Inclusion of reproducible code examples and datasets.
Focus on multi-step workflows for spatial omics data analysis using Bioconductor in R.

Main Results:

A continuously updated and tested online resource is now available.
The book offers practical guidance on analyzing diverse spatial omics data.
Interoperability between R and Python for spatial omics analysis is demonstrated.

Conclusions:

The online book serves as a valuable, up-to-date resource for researchers in spatial omics.
It simplifies complex data analysis workflows, promoting reproducibility.
Enhanced accessibility to spatial transcriptomics data analysis is achieved through this resource.