New tutorial for data integration & automated cell type annotation

[Zethson]

Data integration & automated cell type annotation Data integration methods have now been tested extensively and there is (as always) no clear winner. However, some methods are better at certain tasks or problem sizes and we should emphasize that and discuss that. Dataset: Haber et al 2018 + new mouse dataset from Malte

• Benchmarking atlas-level data integration in single-cell genomics
• Building the Mega Single Cell Transcriptome Ocular Meta-Atlas
• Computational principles and challenges in single-cell data integration
• Computational methods for the integrative analysis of single-cell data
• A benchmark of batch-effect correction methods for single-cell RNA sequencing data
• Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

Cell type annotation can be conducted in an either automated or expert driven fashion. Automated cell type annotation is improving over time as the number of annotated datasets and marker genes grows. Especially, the mapping against reference atlas datasets is becoming more and more interesting.

• A comparison of automatic cell identification methods for single-cell RNA sequencing data
• Automated methods for cell type annotation on scRNA-seq data <- overview of available methods
• Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods
• Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data <- R only eww
• Mapping single-cell data to reference atlases by transfer learning <- not sure about this one here; LH will revisit
• Lisa's atlas paper?

[LuckyMD]

I would remove scmap from the list (more of a label projection method)... and the datasets we need to rethink as well. Those two are not multi-lab datasets, so integration is trivial. We should probably go with the immune cell human task from scIB.

[Zethson]

Closing in favor of many other issues.