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Semi-structured, raw scRNA-seq data from public repositories are difficult to retrieve and integrate together for cell-type and cell-function annotation exercises. Each repository processes data differently and may lack the adequate metadata annotations which directly affects findability of these datasets.

There is a lack of standards for the deposition of cell-level metadata. Although guidelines have recently been proposed for single-cell data deposition, these guidelines have primarily focused on describing experimental aspects of the study. In most cases, even the cell types assigned by investigators for each cellular barcode are not mentioned.

Preliminary exploration or analysis of single cell data has extensive memory requirements. Also, researchers need to spend critical amounts of time downloading the data, packages, and libraries to a computational environment.

Analysis and insight generation from different pipelines written by different users is often counterproductive to reproducibility. More importantly, comparing and interpreting different datasets requires a standard processing pipeline.

 

These lead to major challenges in accessibility, harmonization, and standardization, hindering researchers and organizations from effectively utilizing the data.

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