000009207 001__ 9207
000009207 005__ 20240301092800.0
000009207 0247_ $$2DOI$$a10.6083/p5547s25x
000009207 037__ $$aIR
000009207 041__ $$aeng
000009207 245__ $$aCreating interpretable and informative low-dimensional representations of cell-state from sequencing data using visible neural network variational autoencoders (VNNVAE)
000009207 260__ $$bOregon Health and Science University
000009207 269__ $$a2021
000009207 336__ $$aAbstract
000009207 520__ $$aNext generation sequencing (NGS) and single-cell sequencing technologies have enabled unparalleled resolution of the cell's molecular machinery; however, gleaning accurate knowledge from sequencing data is often stymied by high-dimensionality, measurement noise and biological complexity. Numerous methods have been proposed to address this by mapping high-dimensional inputs to informative low-dimensional representations. One such dimensionality reduction method is the variational autoencoder (VAE), which attempts to learn the probability distribution of given data through a low-dimensional latent variable, and has been shown to competitively separate cell types and to characterize functional differences of cell state1. In this talk, we present our preliminary results implementing a novel VAE model that incorporates prior knowledge through a VNN to create low-dimensional cell-state representations using bulk and single-cell RNA expression features. We hypothesize that this will reduce model complexity (number of parameters) while maintaining or improving model performance and creating a biologically relevant low-dimensional representation of sequencing data. Successful execution of this research will provide an interpretable and informative deep learning dimensionality reduction algorithm. This work was originally motivated by challenges in precision oncology, but may have application in many biological domains.
000009207 540__ $$fCC BY
000009207 542__ $$fIn copyright - joint owners
000009207 650__ $$aDeep Learning$$012734
000009207 650__ $$aMultifactor Dimensionality Reduction$$039067
000009207 650__ $$aHigh-Throughput Nucleotide Sequencing$$039471
000009207 650__ $$aPrecision Medicine$$038927
000009207 650__ $$aMedical Oncology$$021943
000009207 6531_ $$aoncology
000009207 6531_ $$avariational autoencoder
000009207 6531_ $$apersonalized medicine
000009207 6531_ $$avisible neutral network model
000009207 691__ $$aSchool of Medicine$$041369
000009207 692__ $$aDepartment of Medical Informatics and Clinical Epidemiology$$041422
000009207 692__ $$aOHSU Knight Cancer Institute$$041488
000009207 7001_ $$aEvans, Nathaniel$$uOregon Health and Science University$$041354
000009207 7001_ $$aMcWeeney, Shannon$$uOregon Health and Science University$$041354
000009207 711__ $$aResearch Week$$uOregon Health and Science University$$d2021
000009207 8564_ $$9c55c2337-4b05-4c55-a8ac-52e44a4b523b$$s108490$$uhttps://digitalcollections.ohsu.edu/record/9207/files/Evans-Nathaniel-OHSU-ResearchWeek-2021.pdf
000009207 905__ $$a/rest/prod/p5/54/7s/25/p5547s25x
000009207 980__ $$aResearch Week