OmniClust: A versatile clustering toolkit for single-cell and spatial transcriptomics data

Abstract

In recent years, RNA transcriptome sequencing technology has been continuously evolving, ranging from single-cell transcriptomics to spatial transcriptomics. Although these technologies are all based on RNA sequencing, each sequencing technology has its own unique characteristics, and there is an urgent need to develop an algorithmic toolkit that integrates both sequencing techniques. To address this, we have developed OmniClust, a toolkit based on single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data. OmniClust employs deep learning algorithms for feature learning and clustering of spatial transcriptomics data, while utilizing machine learning algorithms for clustering scRNA-seq data. OmniClust was tested on 12 spatial transcriptomics benchmark datasets, demonstrating high clustering accuracy across multiple clustering evaluation metrics. It was also evaluated on four scRNA-seq benchmark datasets, achieving high clustering accuracy based on various clustering evaluation metrics. Furthermore, we applied OmniClust to downstream analyses of spatial transcriptomics and single-cell RNA breast cancer data, showcasing its potential to uncover and interpret the biological significance of cancer transcriptome data. In summary, OmniClust is a clustering tool designed for both single-cell transcriptomics and spatial transcriptomics data, demonstrating outstanding performance.