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

Thank you for your attention!

BNSMA2023: Cacoa + Epilepsy

By Viktor Petukhov

BNSMA2023: Cacoa + Epilepsy

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