A central goal in neurobiology is to relate the expression of genes to the structural and functional properties of neuronal types, collectively called their phenotypes. Single-cell RNA sequencing can measure the expression of thousands of genes in thousands of neurons. How to interpret the data in the context of neuronal phenotypes? We propose a supervised learning approach that factorizes the gene expression data into components corresponding to individual phenotypic characteristics and their interactions. This new method, which we call factorized linear discriminant analysis (FLDA), seeks a linear transformation of gene expressions that varies highly with only one phenotypic factor and minimally with the 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 applied this approach to a single-cell RNA-Seq dataset of Drosophila T4/T5 neurons , focusing on their dendritic and axonal phenotypes. The analysis confirms results obtained by conventional methods but also points to new genes related to the phenotypes and an intriguing hierarchy in the URLanization of these cells .
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 .
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central goal in neurobiology is to relate the expression of genes to
fundamental problem in computational biology is to find a suitable representation of the high-dimensional gene expression data that is consistent with
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neuronal
cell
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Single-cell RNA sequencing can measure the expression of thousands of genes in thousands of neurons. How to interpret the data in the context of neuronal phenotypes? We propose a supervised learning approach that factorizes the gene expression data into components corresponding to individual phenotypic characteristics and their interactions. This new
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
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factor
feature
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the
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applied this approach
illustrated this approach by applying it
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RNA-Seq
transcriptome
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, focusing on their dendritic and axonal phenotypes. The analysis confirms results obtained by conventional methods but also points to new genes related to the phenotypes and an intriguing hierarchy in the URLanization of these cells
. A representation from FLDA captured structures in the data aligned with phenotypic features and revealed critical genes for each phenotype