[HTML][HTML] Single cell RNA-seq data clustering using TF-IDF based methods
M Moussa, II Măndoiu - BMC genomics, 2018 - Springer
BMC genomics, 2018•Springer
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 …
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 …
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.
Springer
