The Single-cell Pediatric Cancer Atlas: Open-source data and tools for single-cell transcriptomics o
Updated: Sep 29, 2022
Allegra G. Hawkins, PhD. Alex's Lemonade Stand Foundation. Joshua A. Shapiro, PhD. Alex's Lemonade Stand Foundation. Chante Bethell, BS. Alex's Lemonade Stand Foundation. David S. Mejia. HSD. Alex's Lemonade Stand Foundation. Deepa Prasad, MS. Alex's Lemonade Stand Foundation. Nozomi Ichihara, AS. Alex's Lemonade Stand Foundation. Arkadii Yakovets, MS. Alex's Lemonade Stand Foundation. Kurt Wheeler, BS. Alex's Lemonade Stand Foundation (former); Reify Health (current). Steven Foltz PhD. Alex's Lemonade Stand Foundation; University of Pennsylvania. Jennifer O'Malley, MA. Alex's Lemonade Stand Foundation. Stephanie J. Spielman, PhD. Alex's Lemonade Stand Foundation. Jaclyn N. Taroni, PhD. Alex's Lemonade Stand Foundation.
In 2019, Alex's Lemonade Stand Foundation funded 10 projects to characterize the transcriptomic landscape of pediatric cancers at single-cell resolution. We introduce the Single-cell Pediatric Cancer Atlas (ScPCA), an open-source data and code resource for single-cell and single-nuclei RNA sequencing data of pediatric tumors. To facilitate widespread usage of the data produced by these projects by the research community, the Childhood Cancer Data Lab (CCDL) developed an open-source pipeline to uniformly process raw sequencing data and release results in the accompanying ScPCA Portal (https://scpca.alexslemonade.org/). As of August 2022, the portal contains 260 samples from a diverse set of over 27 types of cancers. All data were processed using a Nextflow pipeline based around alevin-fry, which allows for fast and efficient processing of single-cell RNA-seq data, as well as the associated CITE-seq and cellhash data used by some ScPCA projects. Quantification of spatial transcriptomics data is also supported. The workflow is available as an open-source resource at https://github.com/AlexsLemonade/scpca-nf, allowing researchers to leverage this pipeline for their own datasets. Finally, we are currently developing an additional open-source workflow (https://github.com/AlexsLemonade/scpca-downstream-analyses) to support downstream analyses, including filtering, normalization, dimensionality reduction, and clustering on single-cell and/or single-nuclei quantified transcriptomic data. All of these ScPCA resources have comprehensive documentation to support new users.