A Tool for Visualization and Analysis of Single-Cell RNA-Seq Data Based on Text Mining

• Published in: Frontiers in genetics 2019-08, Vol.10, p.734-734
• Main Authors: Gambardella, Gennaro, di Bernardo, Diego
• Format: Article
• Language: English
• Subjects: cell type; enrichment analysis; feature extraction; Genetics; single-cell transcriptomics; term frequency–inverse document frequency
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2019.00734

Gene expression in individual cells can now be measured for thousands of cells in a single experiment thanks to innovative sample-preparation and sequencing technologies. State-of-the-art computational pipelines for single-cell RNA-sequencing data, however, still employ computational methods that were developed for traditional bulk RNA-sequencing data, thus not accounting for the peculiarities of single-cell data, such as sparseness and zero-inflated counts. Here, we present a ready-to-use pipeline named (gene frequency-inverse cell frequency) for normalization of raw counts, feature selection, and dimensionality reduction of scRNA-seq data for their visualization and subsequent analyses. Our work is based on a data transformation model named term frequency-inverse document frequency (TF-IDF), which has been extensively used in the field of text mining where extremely sparse and zero-inflated data are common. Using benchmark scRNA-seq datasets, we show that the pipeline outperforms existing state-of-the-art methods in terms of improved visualization and ability to separate and distinguish different cell types.