Introduction to statistical methods to analyze large data sets: Principal components analysis

Neil R. Clark; Avi Ma'ayan

doi:10.1126/scisignal.2001967

Introduction to statistical methods to analyze large data sets: Principal components analysis

Neil R. Clark, Avi Ma'ayan

Research output: Contribution to journal › Review article › peer-review

29 Scopus citations

Abstract

This Teaching Resource provides lecture notes, slides, and a problem set for a series of lectures from a course entitled "Systems Biology: Biomedical Modeling." The materials are a lecture introducing the mathematical concepts behind principal components analysis (PCA). The lecture describes how to handle large data sets with correlation methods and unsupervised clustering with this popular method of analysis, PCA.

Original language	English
Article number	tr3
Journal	Science Signaling
Volume	4
Issue number	190
DOIs	https://doi.org/10.1126/scisignal.2001967
State	Published - 13 Sep 2011

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title = "Introduction to statistical methods to analyze large data sets: Principal components analysis",

abstract = "This Teaching Resource provides lecture notes, slides, and a problem set for a series of lectures from a course entitled {"}Systems Biology: Biomedical Modeling.{"} The materials are a lecture introducing the mathematical concepts behind principal components analysis (PCA). The lecture describes how to handle large data sets with correlation methods and unsupervised clustering with this popular method of analysis, PCA.",

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AB - This Teaching Resource provides lecture notes, slides, and a problem set for a series of lectures from a course entitled "Systems Biology: Biomedical Modeling." The materials are a lecture introducing the mathematical concepts behind principal components analysis (PCA). The lecture describes how to handle large data sets with correlation methods and unsupervised clustering with this popular method of analysis, PCA.

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JO - Science Signaling

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Introduction to statistical methods to analyze large data sets: Principal components analysis

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