Single cell RNA-seq data clustering using TF-IDF based methods

Abstract

Background: Single cell transcriptomics is critical for understanding cellular heterogeneity and identification of novel cell types. Leveraging the recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel unsupervised clustering algorithms that are robust to high levels of technical and biological noise and scale to datasets of millions of cells. Results: We present novel computational approaches for clustering scRNA-seq data based on the Term Frequency - Inverse Document Frequency (TF-IDF) transformation that has been successfully used in the field of text analysis. Conclusions: Empirical experimental results show that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches.