Qiao, Mu and Meister, Markus (2020) Factorized linear discriminant analysis and its application in computational biology. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210105-133427535
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Abstract
A fundamental problem in computational biology is to find a suitable representation of the high-dimensional gene expression data that is consistent with the structural and functional properties of cell types, collectively called their phenotypes. This representation is often sought from a linear transformation of the original data, for the reasons of model interpretability and computational simplicity. Here we propose a novel method of linear dimensionality reduction to address this problem. This method, which we call factorized linear discriminant analysis (FLDA), seeks a linear transformation of gene expressions that varies highly with only one phenotypic feature and minimally with others. We further leverage our approach with a sparsity-based regularization algorithm, which selects a few genes important to a specific phenotypic feature or feature combination. We illustrated this approach by applying it to a single-cell transcriptome dataset of Drosophila T4/T5 neurons. A representation from FLDA captured structures in the data aligned with phenotypic features and revealed critical genes for each phenotype.
Item Type: | Report or Paper (Discussion Paper) | ||||||
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Alternate Title: | Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data | ||||||
DOI: | 10.48550/arXiv.2010.02171 | ||||||
Record Number: | CaltechAUTHORS:20210105-133427535 | ||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20210105-133427535 | ||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
ID Code: | 107327 | ||||||
Collection: | CaltechAUTHORS | ||||||
Deposited By: | George Porter | ||||||
Deposited On: | 06 Jan 2021 18:17 | ||||||
Last Modified: | 02 Jun 2023 01:11 |
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