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Single cell RNA-seq data clustering using TF-IDF based methods
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DOI：10.1186/s12864-018-4922-4.png)
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Abstract
En 中文
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.
Keywords:
Single cell RNA-Seq
Clustering
TF-IDF
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