Learning Meaningful Representations of Cells

Abstract

Batch effects are a significant concern in single-cell RNA sequencing (scRNA-Seq) data analysis, where variations in the data can be attributed to factors unrelated to cell types. This can make downstream analysis a challenging task. In this study, a neural network model is designed utilizing contrastive learning and a novel loss func tion for learning an generalizable embedding space from scRNA-Seq data. When benchmarked against multiple established methods for scRNA-Seq integration, the model outperforms existing methods in learning a generalizable embedding space on multiple datasets. A downstream application that was investigated for the embedding space was cell type annotation. When compared against multiple well established cell type classifiers, the model in this study displayed a performance competitive with top performing methods across multiple metrics, such as accuracy, balanced accuracy, and F1 score. These findings aim to quantify the “meaningfulness” of the embedding space learned by the model, and highlight the potential applications of these learned cellular representations. The model is currently being structured into an open-source Python package, simplifying and streamlining its usage.