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bioRxiv · 10.1101/2023.11.18.567507

scfetch: an R package to access and format single-cell RNA sequencing datasets from public repositories

Song, Y.·Gao, J.·Wang, J.
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Abstract

BackgroundDownloading and reanalyzing the existing single-cell RNA sequencing (scRNA-seq) data provides an efficient choice to gain clues and new insights. However, no tool can fetch the diverse scRNA-seq data types (raw data, count matrix, and processed object) distributed in various repositories, process and load the downloaded data to R, convert formats between scRNA-seq objects, and benchmark the format conversion tools. FindingsHere, we present GEfetch2R, an R package with Docker image to (i) download diverse scRNA-seq data types, including raw data (SRA and ENA), count matrices (GEO, UCSC Cell Browser, and PanglaoDB), and processed objects (Zenodo, CELLxGENE, and HCA); (ii) process the downloaded data, load output/downloaded count matrices and annotations to R (SeuratObject/DESeqDataSet), filter the SeuratObject based on cell metadata and genes, and merge multiple SeuratObjects if applicable; (iii) convert formats between the widely used scRNA-seq objects, including SeuratObject, AnnData, SingleCellExperiment, CellDataSet/cell_data_set, and loom, and benchmark format conversion tools in terms of information kept, usability, running time, and scalability to guide the tool selection. Furthermore, GEfetch2R can also download, process, and load bulk RNA-seq raw data (SRA and ENA) and count matrices (GEO) to R (DESeqDataSet). ConclusionsGEfetch2R is an R package dedicated to facilitating researchers to access and explore the existing gene expression data from various public repositories. It can function as a data downloader (supports all three scRNA-seq and two bulk RNA-seq data types), a data processor (processes and loads the output/downloaded count matrices and annotations to R), and an object format converter (between the widely used scRNA-seq objects).

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Song, Y., Gao, J., Wang, J.. 2023-11-20. scfetch: an R package to access and format single-cell RNA sequencing datasets from public repositories. https://doi.org/10.1101/2023.11.18.567507

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