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Updated: Jun 2, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Discovering biological progression underlying microarray samples.

Peng Qiu¹, Andrew J Gentles, Sylvia K Plevritis

¹Department of Radiology, Stanford University, Stanford, California, USA. pqiu@mdanderson.org

Plos Computational Biology

|May 3, 2011

Summary

This summary is machine-generated.

Sample Progression Discovery (SPD) reveals biological patterns in gene expression data. This computational method organizes samples to uncover underlying processes like cell differentiation and disease progression, identifying key genes involved.

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Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Biological processes like differentiation and development involve sequential changes.
Understanding these progressions requires analyzing complex gene expression data.
Existing methods may not fully capture the dynamic nature of these processes.

Purpose of the Study:

To introduce Sample Progression Discovery (SPD), a novel computational approach.
To discover underlying biological progression patterns in microarray gene expression data.
To identify gene subsets responsible for observed biological progressions.

Main Methods:

SPD assumes samples represent points along an unknown biological progression.
It organizes samples to reveal the progression and identifies responsible gene subsets.
The method was tested on various microarray datasets without prior process information.

Main Results:

SPD accurately recovered the time order for cell cycle data and identified relevant genes.
It correctly ordered B-cell differentiation stages and linked tumor cells to their origin.
The approach uncovered differentiation landscapes in stem cells and identified disease-progression-related gene modules in cancer data.

Conclusions:

SPD is a powerful tool for uncovering hidden biological progressions in gene expression data.
It aids in generating hypotheses by revealing likely biological pathways and regulatory genes.
The method demonstrates versatility across diverse biological processes and datasets.