Work

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

Year: 2018

Type: article

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.

Source: BMC Genomics

DOI: 10.1186/s12864-018-4922-4

Authors: Marmar Moussa, Ion I. Măndoiu

Institution: University of Connecticut

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,090

Funders: National Science Foundation, National Institutes of Health, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke