# Microarray analysis techniques

**microarraySignificance analysis of microarraysmicroarray analysisPathway Analysisarrayarray analysesmicroarray approachmicroarray chipmicroarraysnormalization methods**

Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire genome - in a single experiment.wikipedia

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### Microarray

**microarraysmicroarray analysismicroarray technology**

Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire genome - in a single experiment.

### Robert Tibshirani

**Robert J. TibshiraniTibshirani, RobertRob Tibshirani**

Significance analysis of microarrays (SAM) is a statistical technique, established in 2001 by Virginia Tusher, Robert Tibshirani and Gilbert Chu, for determining whether changes in gene expression are statistically significant.

Lasso method, which proposed the use of L 1 penalization in regression and related problems, and Significance Analysis of Microarrays.

### Microarray databases

**Microarray DatabaseMicroarray data**

### Genome

**genomesgenetic materialgenomic**

Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire genome - in a single experiment.

### Affymetrix

**Affymetrix GeneChip Operating SoftwareAffyAffymetrix, Inc**

Most microarray manufacturers, such as Affymetrix and Agilent, provide commercial data analysis software alongside their microarray products.

### Agilent Technologies

**AgilentAgilent Technologies IncAgilent Technologies Inc.**

Most microarray manufacturers, such as Affymetrix and Agilent, provide commercial data analysis software alongside their microarray products.

### Local regression

**LOESSLowess curveLoess curve**

Dye normalization for two color arrays is often achieved by local regression.

### MA plot

A common method for evaluating how well normalized an array is, is to plot an MA plot of the data.

### Median polish

Robust Multi-array Average (RMA) is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through median polish.

### Hierarchical clustering

**agglomerative hierarchical clusteringhierarchical cluster analysisdivisive clustering**

Hierarchical clustering, and k-means clustering are widely used techniques in microarray analysis.

### Homogeneity and heterogeneity

**heterogeneoushomogeneousheterogeneity**

Hierarchical clustering is a statistical method for finding relatively homogeneous clusters.

### Distance matrix

**distance matricesdissimilarity matrixdistance (or cost) matrix**

Initially, a distance matrix containing all the pairwise distances between the genes is calculated.

### Pearson correlation coefficient

**correlation coefficientPearson product-moment correlation coefficientPearson correlation**

Pearson’s correlation and Spearman’s correlation are often used as dissimilarity estimates, but other methods, like Manhattan distance or Euclidean distance, can also be applied.

### Spearman's rank correlation coefficient

**Spearman's rank correlationrank correlation coefficientSpearman**

Pearson’s correlation and Spearman’s correlation are often used as dissimilarity estimates, but other methods, like Manhattan distance or Euclidean distance, can also be applied.

### Taxicab geometry

**Manhattan distanceL1 normtaxicab metric**

Pearson’s correlation and Spearman’s correlation are often used as dissimilarity estimates, but other methods, like Manhattan distance or Euclidean distance, can also be applied.

### Euclidean distance

**Euclidean metricEuclideandistance**

### UPGMA

**Unweighted Pair Group Method with Arithmetic Meanaverage linkage**

### Centroid

**centroidsgeographic centerbarycenter**

Grouping is done by minimizing the sum of the squares of distances between the data and the corresponding cluster centroid.

### K-medoids

**k''-medoidsk-medoidK-medoids clustering (PAM)**

K-means clustering algorithm and some of its variants (including k-medoids) have been shown to produce good results for gene expression data (at least better than hierarchical clustering methods).

### K-means clustering

**k-meansk''-means clusteringk-means algorithm**

Hierarchical clustering, and k-means clustering are widely used techniques in microarray analysis.

### GenMAPP

Non-commercial tools such as FunRich, GenMAPP and Moksiskaan also aid in organizing and visualizing gene network data procured from one or several microarray experiments.

### Anduril (workflow engine)

**AndurilMoksiskaan**

Non-commercial tools such as FunRich, GenMAPP and Moksiskaan also aid in organizing and visualizing gene network data procured from one or several microarray experiments.

### Bioconductor

A wide variety of microarray analysis tools are available through Bioconductor written in the R programming language.

### Phenotype

**phenotypicphenotypesphenotypically**

Specialized software tools for statistical analysis to determine the extent of over- or under-expression of a gene in a microarray experiment relative to a reference state have also been developed to aid in identifying genes or gene sets associated with particular phenotypes.

### Gene set enrichment analysis

**gene set enrichmentenrichment analysesDAVID**

One such method of analysis, known as Gene Set Enrichment Analysis (GSEA), uses a Kolmogorov-Smirnov-style statistic to identify groups of genes that are regulated together.