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Factorized linear discriminant analysis and its application in computational biology

Qiao, Mu and Meister, Markus (2020) Factorized linear discriminant analysis and its application in computational biology. . (Unpublished)

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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)
Related URLs:
URLURL TypeDescription Paper
Qiao, Mu0000-0001-7309-4237
Meister, Markus0000-0003-2136-6506
Alternate Title:Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data
Record Number:CaltechAUTHORS:20210105-133427535
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107327
Deposited By: George Porter
Deposited On:06 Jan 2021 18:17
Last Modified:02 Jun 2023 01:11

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