Viktor Petukhov PRO
PhD student at the University of Copenhagen
Case-control analysis of single-cell RNA-seq studies
Petukhov Viktor
Khodosevich Lab, BRIC
viktor.petukhov@pm.me
Computational methods for single-cell analysis of brain disorders
Co-supervisor
- Computational methods for single-cell biology
- Harvard Medical School
Peter Kharchenko
Background
Main supervisor
- Biology of neurodevelopmental disorders
- scRNA-seq
- University of Copenhagen
Konstantin Khodosevich
Structure
Overview of projects
Biological introduction
Projects: Conos
Projects: Epilepsy
Projects: Cacoa
Future directions
Slide complexity
Overview of projects
in prep.
preprint
package
published
authorship
Biology
Schizophrenia
Neuron Maturation
Macaque Vis Region
brain
Conos
Epilepsy
Cacoa
2019
2020
co-
co-
case-control
Baysor
SpaceTx
2022
spatial
Future work
Cacoa
Epilepsy
Conos
Overview
Introduction
Our data: single-cell RNA-sequencing (2015)
DNA:
RNA molecules
Genes
}
Expression
levels
(3;
1;
5)
Future work
Cacoa
Epilepsy
Conos
Overview
Introduction
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?
Conos
Epilepsy
Cacoa
Conos
Epilepsy
Cacoa
Future work
Cacoa
Epilepsy
Conos
Overview
Introduction
Problem: batch-effect on multiple samples
Problem: batch-effect on multiple samples
- Individuals
- Tissues
- Species
Potential sources of differences:
- Treatment
- Environment
- Disease
- Protocol
- Lab
- ...
Conos: clustering of networks of samples
Goal: develop a framework to analyze heterogeneous collections of samples without suffering from batch-effect
State of the art (Seurat, mnn): create a joint 'batch-corrected' expression space
Conos: clustering of networks of samples
Goal: develop a framework to analyze heterogeneous collections of samples without suffering from batch-effect
State of the art (Seurat, mnn): create a joint 'batch-corrected' expression space
Problem: joint expression space removes important variation and distorts distributions
Conos: clustering of networks of samples
Problem: joint expression space removes important variation and distorts distributions
Solution: work with a joint topology (graph), not expression space
Conos: clustering of networks of samples
Pairwise alignment
Conos: clustering of networks of samples
Joint graph
Conos: clustering of networks of samples
Graph analysis
- Clustering
- 2D embedding
- Propagating information between samples
- Other graph processing
Conos: results
Annotation
Samples
Clusters
Conos: benchmarking
Projects
Conos
Epilepsy
Cacoa
Projects
Conos
Epilepsy
Cacoa
Future work
Cacoa
Epilepsy
Conos
Overview
Introduction
Our data: multiple samples (2018)
Problem: multiple conditions (2020)
Healthy patients
Epilepsy patients
scRNA-seq to study Temporal Lobe Epilepsy
- Temporal Cortex
- 9 control vs 10 epilepsy samples
- 117k nuclei
scRNA-seq to study Temporal Lobe Epilepsy
Goal: investigate molecular mechanisms of Temporal Lobe Epilepsy using single-cell data
State of the art: no scRNA-seq studies were done on Epilepsy data
Problem: how to measure changes between conditions?
Step 1: align and annotate
Step 2: identification of changed types
Gene expression analysis
Compositional analysis
%of nuclei
Step 2.1: compositional analysis
%of nuclei
Step 2.2: expression similarity
Step 2.2: expression similarity
Step 2.2: expression similarity
Step 2.2: expression similarity
Z = \frac{\overline{d}_{control} - d_{between}}{\overline{d}_{control}}
Step 2.2: expression similarity
Step 2.3: cell type ranking
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
Projects
Conos
Epilepsy
Cacoa
Projects
Conos
Epilepsy
Cacoa
Future work
Cacoa
Epilepsy
Conos
Overview
Introduction
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
Case-control analysis of single-cell RNA-seq studies
Compositional analysis
Gene expression analysis
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
Compositional analysis: cluster-based
Compositional analysis: cluster-based
*: CoDA significance
*: proportion significance
Compositional analysis: cluster-based
Compositional analysis: cluster-based
Control
Multiple sclerosis
Compositional analysis: cluster-based
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
Expression analysis: cluster-free
Expression analysis: cluster-free
control
epilepsy
Expression analysis: cluster-free
control
epilepsy
Compare
Expression analysis: cluster-free shifts
Expression analysis: cluster-free DE
Expression analysis: cluster-free DE
Gene programs:
Expression analysis: cluster-free DE
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
Future directions
- Improving precision and power of methods
- Including additional modalities
- Including multiple conditions
- Better handling covariates
Future work
Cacoa
Epilepsy
Conos
Overview
Introduction
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
Acknowledgements
Thank you for your attention!
Petukhov Viktor
Khodosevich Lab, BRIC
Case-control analysis of single-cell RNA-seq studies
PhD Defence
By Viktor Petukhov
