Classification of gene expression in scRNA-seq data

Abstract

Single cell RNA sequencing (scRNA-seq) presents some important informationin terms of activity levels for each gene given a specific kind of a cell. Thisinformation is organized into a profile and can be used to predict of the type ofthe cell when the cell type is unknown. This project presents an analysis of alarge dataset containing approximately 24000 gene expression profiles. As the titleindicates, we train several machine learning algorithms to perform classification topredict the cell type given the gene expression profile and summarize our results.Additionally, we also reduce the dimensionality of the data to aid in visualizing thedataset provided. We report results on various machine learning algorithms for thetask, motivated by our analysis of the dataset.

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