Case-control analysis of single-cell RNA-seq studies

Petukhov V, Rydbirk R, Igolkina A, Mei S, Kharchenko P, Khodosevich K

viktor.petukhov@pm.me

Description of the problem

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

Case samples

Control samples

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

What can we possibly do?

Gene expression analysis

Compositional analysis

Cluster-based

Cluster-free

control

epilepsy

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

Composition analysis, cluster-based

Composition analysis, cluster-based

Problem: changes are not independent

Composition analysis, cluster-based

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

Old

New

Composition analysis, cluster-free

Control

Multiple sclerosis

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

Composition analysis, cluster-free

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

Control

MS

Improved z-scores

P-value adjustment

Expression analysis, cluster-based

What cell types are affected the most?

Expression analysis, cluster-based

Visualization of sample structure

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

Expression analysis, cluster-based

Visualization of sample structure

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

Expression analysis, cluster-based

Visualization of sample structure

*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

Differential expression stability

*Done by Anna and Rasmus

Code improvement

  • Updated cluster-free DE computations
  • Lots of performance, usability and code improvements
  • It works on arbitrary objects now

Thank you!

Konstantin Khodosevich lab

Peter Kharchenko

  • Rasmus Rydbirk
  • Anna Igolkina
  • Shenglin Mei

Co-authors

viktor.petukhov@pm.me

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