DNA microarray: Difference between revisions

Revision as of 00:15, 11 April 2021

Revision as of 06:57, 4 June 2021

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<!-- {{Citation needed|date=July 2008}}as in many other cases where authorities disagree, a sound conservative approach is to directly compare different normalization methods to determine the effects of these different methods on the results obtained. This can be done, for example, by investigating the performance of various methods on data from "spike-in" experiments. {{Citation needed|date=July 2008}} -->

* Dimensional reduction: Analysts often reduce the number of dimensions (genes) prior to data analysis.<ref name="Peterson"/> This may involve linear approaches such as principal components analysis (PCA), or non-linear manifold learning (distance metric learning) using kernel PCA, diffusion maps, Laplacian eigenmaps, local linear embedding, locally preserving projections, and Sammon's mapping.

* Network-based methods: Statistical methods that take the underlying structure of gene networks into account, representing either associative or causative interactions or dependencies among gene products.<ref name="Emmert">{{cite book |author=Emmert-Streib, F.|author2=Dehmer, M.|name-list-style=amp |date=2008 |title=Analysis of Microarray Data A Network-Based Approach |publisher=Wiley-VCH |isbn=978-3-527-31822-3}}</ref> [[Weighted Correlationcorrelation Networknetwork Analysisanalysis|Weighted gene co-expression network analysis]] is widely used for identifying co-expression modules and intramodular hub genes. Modules may corresponds to cell types or pathways. Highly connected intramodular hubs best represent their respective modules.

Microarray data may require further processing aimed at reducing the dimensionality of the data to aid comprehension and more focused analysis.<ref>{{cite journal