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.

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
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.

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.