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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: Nov 1, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

SOMDE: a scalable method for identifying spatially variable genes with self-organizing map.

Minsheng Hao¹, Kui Hua¹, Xuegong Zhang^1,2

¹MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.

Bioinformatics (Oxford, England)

|June 24, 2021

Summary

This summary is machine-generated.

We developed SOMDE, an efficient computational method for identifying spatially variable genes (SVGs) in large spatial transcriptomic datasets. SOMDE significantly accelerates analysis while maintaining accuracy, enabling faster insights into tissue microenvironments.

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Published on: April 13, 2012

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomic sequencing technologies offer insights into cellular functions within tissue microenvironments.
Identifying spatially variable genes (SVGs) is crucial for spatial gene expression analysis.
Existing computational methods for SVG identification face scalability challenges with large datasets.

Purpose of the Study:

To present SOMDE, an efficient computational method for identifying SVGs in large-scale spatial expression data.
To address the limitations of high computational complexity in current SVG identification tools.
To provide a scalable solution for analyzing emerging large-scale spatial transcriptomic datasets.

Main Methods:

SOMDE utilizes a self-organizing map to cluster neighboring cells into nodes.
A Gaussian process is employed to fit node-level spatial gene expression data.
This approach facilitates the identification of spatially variable genes.

Main Results:

SOMDE achieves comparable results to existing methods while being 5-50 times faster.
Its adjustable resolution allows for rapid analysis (∼5 min) of datasets exceeding 20,000 sites.
SOMDE demonstrates significant computational efficiency for large spatial expression data.

Conclusions:

SOMDE provides an efficient and scalable solution for identifying spatially variable genes.
The method overcomes computational bottlenecks, making it suitable for large-scale spatial transcriptomic data analysis.
SOMDE is readily available as a Python package for academic use.