From 30803c7321595a67cbeb733f6489d021a0bdef88 Mon Sep 17 00:00:00 2001 From: John Kerl Date: Thu, 16 Feb 2023 15:58:03 -0500 Subject: [PATCH] code-review feedback --- abstract_specification.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abstract_specification.md b/abstract_specification.md index 5f7e9815..fc8bf6ef 100644 --- a/abstract_specification.md +++ b/abstract_specification.md @@ -12,7 +12,7 @@ The goal of SOMA (“stack of matrices, annotated”) is a flexible, extensible, - support access to persistent, cloud-resident datasets - enable use within popular data-science environments (e.g., R, Python), using the tools of that environment (e.g., Python Pandas integration) - enable "out-of-core" access to data aggregations much larger than single-host main memory -- enable distributed computation over datasets, and +- enable distributed computation over datasets - provide a building block for higher-level API that may embody domain-specific conventions or schema around annotated 2D matrices (e.g., a cell "atlas"). The SOMA data model is centered on annotated 2-D matrices, conceptually similar to commonly used single-cell 'omics data structures including Seurat Assay, Bioconductor SingleCellExperiment, and Scanpy AnnData. Where possible, the SOMA API attempts to be general-purpose and agnostic to the specifics of any given environment, or to the specific conventions of the Single Cell scientific ecosystem.