Single cell RNA-seq data clustering using TF-IDF based methods
Related Experiment Video
Updated: Feb 3, 2026

Nuclei Isolation from Fresh Frozen Brain Tumors for Single-Nucleus RNA-seq and ATAC-seq
Published on: August 25, 2020
Marmar Moussa1, Ion I Măndoiu2
1University of Connecticut, Storrs, 06269, CT, USA.
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.
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