Ding L, Zentner GE, McDonald DJ. Sufficient principal component regression for pattern discovery in transcriptomic data.
BIOINFORMATICS ADVANCES 2022;
2:vbac033. [PMID:
35722206 PMCID:
PMC9194947 DOI:
10.1093/bioadv/vbac033]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 03/16/2022] [Accepted: 05/04/2022] [Indexed: 01/27/2023]
Abstract
Motivation
Methods for the global measurement of transcript abundance such as microarrays and RNA-Seq generate datasets in which the number of measured features far exceeds the number of observations. Extracting biologically meaningful and experimentally tractable insights from such data therefore requires high-dimensional prediction. Existing sparse linear approaches to this challenge have been stunningly successful, but some important issues remain. These methods can fail to select the correct features, predict poorly relative to non-sparse alternatives or ignore any unknown grouping structures for the features.
Results
We propose a method called SuffPCR that yields improved predictions in high-dimensional tasks including regression and classification, especially in the typical context of omics with correlated features. SuffPCR first estimates sparse principal components and then estimates a linear model on the recovered subspace. Because the estimated subspace is sparse in the features, the resulting predictions will depend on only a small subset of genes. SuffPCR works well on a variety of simulated and experimental transcriptomic data, performing nearly optimally when the model assumptions are satisfied. We also demonstrate near-optimal theoretical guarantees.
Availability and implementation
Code and raw data are freely available at https://github.com/dajmcdon/suffpcr. Package documentation may be viewed at https://dajmcdon.github.io/suffpcr.
Contact
daniel@stat.ubc.ca.
Supplementary information
Supplementary data are available at Bioinformatics Advances online.
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