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Optimal Dimensionality Selection for Independent Component Analysis of Transcriptomic Data by John Luke McConn; Cameron R. Lamoureux; Saugat Poudel; Bernhard O. Palsson; Anand V. Sastry is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Optimal Dimensionality Selection for Independent Component Analysis of Transcriptomic Data about?

Abstract Background Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. Methods We computed independent components across a range of dimensionalities for four gene expression datasets with varying dimensions (both in terms of number of genes and number of samples). We computed the correlation between independent components across different dimensionalities to understand how the overall structure evolves

Who reads Optimal Dimensionality Selection for Independent Component Analysis of Transcriptomic Data?

Author

John Luke McConn; Cameron R. Lamoureux; Saugat Poudel; Bernhard O. Palsson; Anand V. Sastry

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)