Spacemake: processing and analysis of large-scale spatial transcriptomics data.
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IF: 7.658
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Cited by: 7
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Abstract

Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques quantify messenger RNA expression levels from tissue sections and at the same time register information about the original locations of the molecules in the tissue. The resulting data sets are processed and analyzed by accompanying software that, however, is incompatible across inputs from different technologies. Here, we present spacemake, a modular, robust, and scalable spatial transcriptomics pipeline built in Snakemake and Python. Spacemake is designed to handle all major spatial transcriptomics data sets and can be readily configured for other technologies. It can process and analyze several samples in parallel, even if they stem from different experimental methods. Spacemake's unified framework enables reproducible data processing from raw sequencing data to automatically generated downstream analysis reports. Spacemake is built with a modular design and offers additional functionality such as sample merging, saturation analysis, and analysis of long reads as separate modules. Moreover, spacemake employs novoSpaRc to integrate spatial and single-cell transcriptomics data, resulting in increased gene counts for the spatial data set. Spacemake is open source and extendable, and it can be seamlessly integrated with existing computational workflows.

Keywords

novoSpaRc
Spatial Transcriptomics
ProximID
bioinformatics
computational biology
computational pipeline
modularity
reproducibility
scalability
sequence analysis
single-cell transcriptomics
spatial transcriptomics
workflow

MeSH terms

Computational Biology
RNA, Messenger
Software
Transcriptome
Workflow

Authors

Sztanka-Toth, Tamas Ryszard
Jens, Marvin
Karaiskos, Nikos
Rajewsky, Nikolaus