Task 1: provide a unified representation of single-cell data

Challenges:

• Hundreds of scRNA-seq software tools

• https://github.com/seandavi/awesome-single-cell 
• http://www.scrna-tools.org 

• Most R and Bioconductor packages define their own class
• Some extend SummarizedExperiment, some ExpressionSet
• Most packages don’t fully exploit the potential of SummarizedExperiment (e.g., assay does not have to be a matrix)

Proposed solutions:

• Create a class for developers to extend: SingleCellExperiment

Useful Bioconductor packages and other resources:

• SingleCellExperiment

Task 2: scale-up of existing tools / implementation of tools to handle large-scale datasets

Challenges:

• Tools are scalable to thousands of cells.
• 10X Genomics released 1.3 Million cells dataset!
• Main problem: does not fit in memory!

Proposed solutions:

• HD5 files + "chunk operations"
• Simple algorithms + approximate, scalable methods
• Provide API to perform common operations independent of data representation (in memory vs. on disk)

Useful Bioconductor packages and other resources:

• TENxGenomics (Martin Morgan)
• beachmat (Aaron)
• DelayedMatrixStats (Pete)
• BigDataAlgorithms
• HDF5Array (Herve Pages)
• restfulSE (Vince Carey)

Interested in contributing? Join the slack channel:

https://community-bioc.slack.com

Discussion points

1. Benchmark (canonical datasets)
2. Splatter (simulations of scRNA-seq)
3. What to do next?
4. BigDataAlgorithms: define scope, what functionalities we want

1. Prior art in astronomy, etc?

5. Visualization?
6. Multi assay?

1. People are running single-cell assays that generate multiple types of data (e.g., RNA expression and methylation) from each single-cell.
2. Can store each assay in a SingleCellExperiment and then put inside a MultiAssayExperiment to link up the row and column metadata.

7. Multiple samples--list of SingleCellExperiments vs giant joined SingleCellExperiment.  Can we learn from flowSet?