Optimal dimensionality selection for independent component analysis of transcriptomic data

Optimal dimensionality selection for independent component analysis of transcriptomic data

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...

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Bibliographic Details
Main Authors: Lamoureux, C.R (Author), McConn, J.L (Author), Palsson, B.O (Author), Poudel, S. (Author), Sastry, A.V (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
article
Databases, Factual
decomposition
factual database
gene cluster
gene expression
Gene expression
Gene expression datasets
gene expression profiling
Gene Expression Profiling
gene regulatory network
Gene Regulatory Networks
Gene sets
High quality
high throughput technology
independent component analysis
Independent component analysis
Independent components
Independent components analysis
Learning algorithms
Machine learning
Machine learning algorithms
Optimal dimensionality
Quality control
Source signals
transcriptome
Transcriptome
Transcriptomics
Unsupervised machine learning
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