Single cell RNA-seq data clustering using TF-IDF based methods
By Marmar Moussa, Ion I. Mandoiu. 2018
38 citations recorded by the source corpus.
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. 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. Empirical experimental results show that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches.
Subfields: Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Michael Heinzinger
- Christian Dallago
- Ahmed Elnaggar
- Yu Wang
- Florian Matthes
- Dmitrii Nechaev
- Burkhard Rost
- Yana Bromberg
Citations in the corpus
- Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “This concept resembles that of using k-mers for database searches [33], clustering [81], motifs [82, 83], and prediction methods [72, 76, 84 – 88].”