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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: Apr 17, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Microarray enriched gene rank.

Eugene Demidenko¹

¹Department of Biomedical Data Science, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, 03755 NH USA.

|February 5, 2015

Summary

This summary is machine-generated.

We developed gene rank (GR), a novel method combining gene ontology and microarray data to measure gene connectivity. GR offers insights into gene pathways and biological expectations, aiding hypothesis generation.

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Introduced a novel concept for gene connectivity analysis using microarray data.
Developed gene rank (GR), integrating prior gene ontology (GO) knowledge with expression data.
Modeled GR recursively, analogous to Google PageRank, using stochastic matrices from expression data.

Purpose of the Study:

To develop and validate a new method for quantifying gene connectivity.
To explore the relationship between gene connectivity and biological phenomena such as cancer survival and tumor subtypes.
To investigate variations in gene connectivity across different organs and organisms.

Main Methods:

Utilized the coefficient of determination (squared Pearson correlation) for gene connectivity.
Computed GR as the left maximum eigenvector of a derived stochastic matrix.
Developed an efficient algorithm for rapid GR computation on large datasets using R.

Main Results:

Demonstrated GR's ability to confirm biological expectations across various microarray datasets.
Applied GR to analyze gene connectivity in relation to cancer patient survival.
Associated gene connectivity with tumor subtypes in ovarian cancer and compared it across organs and organisms.

Conclusions:

GR findings align with and support existing biological knowledge.
GR serves as a valuable tool for generating hypotheses regarding gene pathways.
GR facilitates comparative analyses of gene connectivity in different biological contexts, including case-control and longitudinal studies.