Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Using ANOVA to analyze microarray data.

BioTechniques - 1 Aug 2004

Churchill Gary A

Abstract excerpt

ANOVA provides a general approach to the analysis of single and multiple factor experiments on both one- and two-color microarray platforms. Mixed model ANOVA is important because in many microarray experiments there are multiple sources of variation that must be taken into consideration when constructing tests for differential expression of a gene. The genome is large, and the signals of expression change can be...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Analysis of Variance
Share to Gene Expression Profiling

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Expression Regulation

Share to Models, Statistical

Share to Oligonucleotide Array Sequence Analysis

Share to Sample Size

Share to Software

Source

Identifiers

PMID: 15335204
DOI: 10.2144/04372TE01

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Using ANOVA to analyze microarray data.PMID 15335204Read ↗

Split-plot microarray experiments: issues of design, power and sample size.Semantic proximity Read ↗

Microarray data analysis: a hierarchical T-test to handle heteroscedasticity.Semantic proximity Read ↗

Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays.Semantic proximity Read ↗

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes.Semantic proximity Read ↗

GeneANOVA--gene expression analysis of variance.Semantic proximity Read ↗

Normalization of microarray data using a spatial mixed model analysis which includes splines.Semantic proximity Read ↗

Analysis of variance components in gene expression data.Semantic proximity Read ↗

On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression.Semantic proximity Read ↗

The PowerAtlas: a power and sample size atlas for microarray experimental design and research.Semantic proximity Read ↗

Replication, variation and normalisation in microarray experiments.Semantic proximity Read ↗

A random variance model for detection of differential gene expression in small microarray experiments.Semantic proximity Read ↗

Statistics: detecting differences among groups.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.