Gene arrays can test for gene expression {gene expression, algorithms}.
array algorithms
Gene-expression-array algorithms automatically find background, errors, fiducials, normalization, signal values, spatial cross-talk, spots, replicate aggregates, and clustering.
gene expression analysis
Gene-expression analysis compares different tissues or conditions to find gene-expression patterns. Genes in the same biochemical pathways, genes with similar chemical reactions, and genes for similar cell processes have similar regulation and have similar gene-expression patterns.
gene expression visualization
To visualize expression patterns, expression spaces can have dimension number equal to experiment number. Experiments determine gene-expression ratios, which are expression-space points or vectors. Similar genes have points that cluster near each other in expression space.
Expression matrices have column number equal to experiment number. Gene-expression ratios are expression-matrix rows. Algorithms sort rows and columns to cluster up-regulated and down-regulated genes and/or experiments. Expression-matrix cells can have colors, such as red for up-regulated, green for down-regulated, and black for control levels.
cluster analysis
Using distance measures, genes and/or experiments can cluster. Clustering algorithms include hierarchical, self-organizing maps, K-means, fuzzy C-means, and expectation maximization. Statistical techniques identify gene classes and/or experiment classes and assign shared features. Hierarchical clustering makes hierarchies. Self-organizing maps group equal categories. Error-weighted gene-expression clustering retrofits clustering algorithm information to use error-propagation information.
assay multiplexing
Several assays can be simultaneous, or several samples can be in same wells {multiplexing, assays}.
Biological Sciences>Genetics>Algorithms
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Date Modified: 2022.0224