Description of the problem

Two conditions, multiple samples per condition, multiple cells per sample

Description of the problem

What cell types are affected and how?

Questions to existing data:

• Do some cell types changed their expression in a similar way?
• Which genes changed their expression in a similar way?
• All other patterns in expression changes we can think of

Existing solutions

*Integrating single-cell transcriptomic data across different conditions, technologies, and species, Butler A, et al. Nature Biotechnology, 2018

Existing solutions

Align samples

scVI, Conos, ..., Seurat

See the review from the Theis lab

*Integrating single-cell transcriptomic data across different conditions, technologies, and species, Butler A, et al. Nature Biotechnology, 2018

Existing solutions

Align samples

Perform joint annotation

*Integrating single-cell transcriptomic data across different conditions, technologies, and species, Butler A, et al. Nature Biotechnology, 2018

Existing solutions

Align samples

Perform joint annotation

Run differential expression 

*Single-cell genomics identifies cell type–specific molecular changes in autism, Velmeshev D, et al. Science, 2019

Existing solutions

Align samples

Perform joint annotation

Run differential expression 

Run Gene Ontology analysis

*Single-cell genomics identifies cell type–specific molecular changes in autism, Velmeshev D, et al. Science, 2019

Existing solutions

Align samples

Perform joint annotation

Run differential expression 

Run Gene Ontology analysis

Compare cell type proportions

*A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation, Grubman A, Nature Neuroscience, 2019

Existing solutions

Align samples

Perform joint annotation

Run differential expression 

Run Gene Ontology analysis

Compare cell type proportions

• A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation, Grubman A, Nature Neuroscience (2019).

• Single-cell genomics identifies cell type–specific molecular changes in autism, Velmeshev D, et al. Science (2019).

• Mathys, H., Davila-Velderrain, J., Peng, Z. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

• Schirmer, L., Velmeshev, D., Holmqvist, S. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019).

• Wheeler, M.A., Clark, I.C., Tjon, E.C. et al. MAFG-driven astrocytes promote CNS inflammation. Nature 578, 593–599 (2020).

Case-control analysis of single-cell studies: a fresh approach

What can we possibly do?

What can we possibly do?

Compositional analysis

Cluster-based

Cluster-free

Case-control analysis of single-cell studies: a fresh approach

Compositional analysis

Gene expression analysis

Cluster-based

Cluster-free

To be improved

To be improved

Ready

Proof of concept

What is done?

Case-control analysis of single-cell studies: a fresh approach

Composition analysis, cluster-based

Ideally, replace this plot with 1 increase vs 2 decrease

Problem: changes are not independent

Composition analysis, cluster-based

Expression analysis, cluster-based

What cell types are affected the most?

Expression analysis, cluster-based

What cell types are affected the most?

Expression analysis, cluster-free

Differential expression on single cells

Expression analysis, cluster-free

Differential expression on single cells

Expression analysis, cluster-free

Gene programs on single cells

Program 1

Expression analysis, cluster-free

Gene programs on single cells

Program 2

Expression analysis, cluster-free

Expression distances