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

Bioinformatics

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.

University of Connecticut researchers 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.

Compared scRNA-Seq clustering methods

rna-seq

Empirical experimental results show that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches.


Moussa M, Măndoiu II. (2018) Single cell RNA-seq data clustering using TF-IDF based methods. BMC Genomics 19(Suppl 6):569. [article]

Articles You May Like

These mysterious mounds in Brazil are 4,000 years old and visible from space
Rhythms of life: circadian disruption and brain disorders across the lifespan
Submarine that vanished a year ago with 44 sailors has been found near Argentina
We Are More Than Our DNA
An ‘Exceptionally Rare’ 2-Headed Snake Found In Virginia Has Died

Leave a Reply

Your email address will not be published. Required fields are marked *