Working group description and goals:
Single-cell RNA sequencing (scRNA-seq) data is extremely complex, being both high-throughput as well as highly heterogeneous in methodological procedures, raw data processing/analysis, and further computational analysis for biological interpretation. For scRNAseq data type, many biological states need to be sampled to annotate the cell type most accurately. As these data come from multiple sources and methods, a coordinated meta-analysis will be quite useful and more accurate. In addition, cell type definitions are inherently subjective and may be suboptimal. All cell types are not known and defined; therefore, it is important that our tools can handle undefined cell categories. There are many different formats for storing this data and metadata; since different tools use different data types, converting between them is a common task.
This working group will:
- address these challenges of scRNAseq metadata in plants and animals and establish a set of recommendations for member databases on the management of sc datasets.
- create a cohesive community that will provide standards, resources, tools, and standardized scRNAseq datasets for FAIR data meta-analysis, leading to initial cell-type transcriptome descriptions.
Champions: Christopher Tuggle, Peter Harrison
Members: This working group will be established in January '24.
Meeting Schedule: more information soon!
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