A joint analysis of single cell transcriptomics and proteomics using transformer

A joint analysis of single cell transcriptomics and proteomics using transformer

Blog Article

Abstract CITE-seq provides a powerful method chervo jacke herren for simultaneously measuring RNA and protein expression at the single-cell level.The integrated analysis of RNA and protein expression in identical cells is crucial for revealing cellular heterogeneity.However, the high experimental costs associated with CITE-seq limit its widespread application.

In this paper, we propose scTEL, a deep learning framework based on Transformer encoder layers, to establish a mapping from sequenced RNA expression to unobserved protein expression in the same cells.This computation-based approach significantly reduces the experimental costs of protein expression sequencing.We are now able to predict protein expression using single-cell RNA sequencing (scRNA-seq) data, which is well-established and available at napoleon concealer a lower cost.

Moreover, our scTEL model offers a unified framework for integrating multiple CITE-seq datasets, addressing the challenge posed by the partial overlap of protein panels across different datasets.Empirical validation on public CITE-seq datasets demonstrates scTEL significantly outperforms existing methods.