Case-control analysis of single-cell RNA-seq studies
Petukhov V, Igolkina A, Rydbirk R, Mei S, Christoffersen L, Kharchenko P, Khodosevich K
viktor.petukhov@pm.me
Structure
Overview of the problem
State of the field
Cacoa: case-control analysis
Epilepsy data analysis
Slide complexity
Papers we analyzed
Epilepsy
Cacoa
Overview
State
Papers we analyzed
Epilepsy
Cacoa
Overview
State
scRNA-seq to study Temporal Lobe Epilepsy
- Temporal Cortex
- 9 control vs 10 epilepsy samples
- 117k nuclei
Our data: single-cell RNA-sequencing (2015)
DNA:
RNA molecules
Genes
}
Expression
levels
(3;
1;
5)
Genes
DNA:
(3;
1;
5)
}
Expression
levels
t-SNE 1
t-SNE 2
Our data: single-cell RNA-sequencing (2015)
~10k cells x 20k genes
Genes
DNA:
(3;
1;
5)
}
Expression
levels
t-SNE 1
t-SNE 2
Our data: single-cell RNA-sequencing (2015)
Our data: multiple samples (2018)
Problem: multiple conditions (2020)
Control patients
Case patients
Problem: multiple conditions (2020)
Control patients
Case patients
Cells
Cell
types
Problem: multiple conditions (2020)
Control patients
Case patients
Cell
types
Cells
Cells
Genes
Cells
Genes
Cells
Genes
Cells
Genes
Cells
Genes
Cells
Genes
Cells
Genes
Cells
Genes
How to compare?
Goal: develop a comprehensive set of methods for analysis of scRNA-seq case-control experiments
State of the art: no methods were published when we started, several competitors exist now
Problem: how to measure changes between conditions?
Case-control analysis of single-cell RNA-seq studies
Existing solutions
Epilepsy
Cacoa
Overview
State
Existing solutions
Align samples
scVI, Conos, ..., Seurat
See the review from the Theis lab
Existing solutions
Align samples
Perform joint annotation
Existing solutions
Align samples
Perform joint annotation
Run differential expression
Existing solutions
Align samples
Perform joint annotation
Run differential expression
Run Gene Ontology analysis
Existing solutions
Align samples
Perform joint annotation
Run differential expression
Run Gene Ontology analysis
Compare cell type proportions
Can we do better?
There are up to hundreds significant GO terms per type
There are up to 1000 significant DE genes per type
Case-control analysis of single-cell RNA-seq studies
Compositional analysis
Gene expression analysis
What can we possibly do?
Epilepsy
Cacoa
Overview
State
Case-control analysis of single-cell RNA-seq studies
Case-control analysis of single-cell RNA-seq studies
Gene expression analysis
Compositional analysis
Cluster-based
Cluster-free
Control
Multiple sclerosis
Composition analysis, cluster-based
Composition analysis, cluster-based
Problem: changes are not independent
*Credit to Anna Igolkina
Composition analysis, cluster-based
Problem: changes are not independent
*Credit to Anna Igolkina
Compositional analysis: cluster-based
*: CoDA significance
*: proportion significance
Compositional analysis: cluster-based
Neurons
Glia
Compositional analysis: cluster-free
Compositional analysis: cluster-free
Control
Multiple sclerosis
Compositional analysis: cluster-free
Effect size
Significance
Expression analysis: cluster-based
EN L2-L3
Expression analysis: sample structure
EN L2-L3
EN L2-L3
Expression analysis: sample structure
Color by batch
Aggregated across all cell types
Expression analysis: sample structure
Expression analysis: expression distance
separation
EN L2-L3
Expression analysis, cluster-free
Differential expression on single cells
Expression analysis, cluster-free
Differential expression on single cells
Expression analysis, cluster-free
Differential expression on single cells
control
epilepsy
Expression analysis, cluster-free
Differential expression on single cells
control
epilepsy
Differential expression
Expression analysis, cluster-free
Differential expression on single cells
PDE10A
MS
Control
NCKAP5
MS
Control
Expression analysis, cluster-free
Gene programs
Science reproducibility
>70% of publications had
major flows, compromising main results
Science reproducibility
>70% of publications had
major flows, compromising main results
Science reproducibility
- Publish your code
- Double-check your results
- Don't get too excited
Gene expression analysis
Compositional analysis
Cluster-based
Cluster-free
Control
Multiple sclerosis
Summary
Full analysis of the Epilepsy dataset
- Temporal Cortex
- 9 control vs 10 epilepsy samples
- 117k nuclei
Epilepsy
Cacoa
Overview
State
Full analysis of the Epilepsy dataset
Compositional analysis
No significance!
Full analysis of the Epilepsy dataset
Compositional analysis, cluster-free
Effect size
Significance
Full analysis of the Epilepsy dataset
Expression analysis: sample embeddings
Condition
Full analysis of the Epilepsy dataset
Expression analysis: sample embeddings
Condition
Sex
Full analysis of the Epilepsy dataset
Expression analysis: sample embeddings
Condition
Sex
Protocol
Full analysis of the Epilepsy dataset
Expression analysis: shift magnitudes
Full analysis of the Epilepsy dataset
Expression analysis: shift magnitudes
Full analysis of the Epilepsy dataset
Expression analysis: cluster-free DE
Full analysis of the Epilepsy dataset
Programs for excitatory neurons
Full analysis of the Epilepsy dataset
Global excitatory program
Full analysis of the Epilepsy dataset
Programs for inhibitory neurons
Full analysis of the Epilepsy dataset
Joint program
Full analysis of the Epilepsy dataset
Expression analysis: local cluster-free DE
L2_Cux2_Lamp5 programs
Full analysis of the Epilepsy dataset
Expression analysis: local cluster-free DE
L2_Cux2_Lamp5 programs
Full analysis of the Epilepsy dataset
Expression analysis: local cluster-free DE
Step 3.1: global structure of changes
developmental processes, neural circuit re-organization and neurotransmission
ion transport and glutamate signaling
protein transport to axons/dendrites
cell adhesion, ion transport and synaptic plasticity
regulation of neuronal morphogenesis
Step 3.2: local structure of changes
Step 3.2: local structure of changes
Control
Epilepsy
Step 3.2: local structure of changes
Step 3.2: local structure of changes
Epilepsy
Cacoa
Overview
State
Acknowledgements
Khodosevich Lab
Jonathan Mitchel
Ruslan Soldatov
Shenglin Mei
Evan Biederstedt
Navneet Vasistha
Konstantin Khodosevich
Rasmus Rydbirk
Irina Korshunova
Diego González
Katarina Dragicevic
Mykhailo Batiuk
Anna Igolkina
Peter Kharchenko
Thank you for your attention!
Petukhov Viktor
viktor.petukhov@pm.me
Petukhov Viktor
viktor.petukhov@pm.me
Let's collaborate!
- Bioinformatics & statistics
- Analysis of scRNA-seq data
- Anti-ageing & cell rejuvenation
- Case-control analysis of multi-omics data