Year-end Sale % OFF 
Abstract
Elaborate downstream methods are required to analyze large microarray data-sets. At times, where the end goal is to look for relationships between (or patterns within) different subgroups or even just individual samples, large data-sets must first be filtered using statistical thresholds in order to reduce their overall volume. As an example, in anthropological microarray studies, such ‘dimension reduction’ techniques are essential to elucidate any links between polymorphisms and phenotypes for given populations. In such large data-sets, a subset can first be taken to represent the larger data-set. For example, polling results taken during elections are used to infer the opinions of the population at large. However, what is the best and easiest method of capturing a sub-set of variation in a data-set that can represent the overall portrait of variation? In this article, principal components analysis (PCA) is discussed in detail, including its history, the mathematics behind the process, and in which ways it can be applied to modern large-scale biological datasets. New methods of analysis using PCA are also suggested, with tentative results outlined.
Highlights
Principal components analysis and other multivariate tools are used to analyze large volumes of data in order to tease out the differences/relationships between the logical entities being analyzed [1]
On the Affymetrix single nucleotide polymorphisms (SNPs) 6.0, 762,463 markers target known genes that are listed in the RefSeq gene database
The data generated through principal components analysis (PCA) was channelled through the haplotype-tagging copy number variants (htCNVs) pipeline, which was capable of reducing them further to 4,594
Summary
Principal components analysis and other multivariate tools are used to analyze large volumes of data in order to tease out the differences/relationships between the logical entities being analyzed (for example, a data-set consisting of a large number of samples, each with their own data points/varia-bles) [1] It extracts the fundamental structure of the data without the need to build any model to represent it [2]. Examples include craniofacial recognition [5], analysis of water quality[3], and to derive a set of highly confident genes [6] or single nucleotide polymorphisms (SNPs) [7, 8] for classification purposes It has been used in subject areas such as climatology, geology, meteorology, psychology, quality control [4], forensics and population genetics ( in relation to SNPs), medical genetics [2], and bacteriology [9]. Du [11] successfully adapted and applied PCA to protein data in the form of Amino Acid PCA (AAPCA), where the aim was to classify proteins into structural classes; Li [12] combined
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
