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
Description of the problem
What cell types are affected and how?
Prepare for further experiments:
- Which subtypes should we focus on?
- Which genes per subtype should we investigate further?
Questions to existing data:
- Did some cell types change 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
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
Existing solutions
Align samples
Perform joint annotation
Run differential expression
Run Gene Ontology analysis
Compare cell type proportions
Why it is not enough
- What cell types are the most affected?
- How exactly are they affected?
The main questions:
Why it is not enough
Number of DE genes is not an answer
- What cell types are the most affected?
- How exactly they are affected?
The main questions:
Why it is not enough
There are up to 1000 significant DE genes per type and 2795 unique DE genes in total
- What cell types are the most affected?
- How exactly they are affected?
The main questions:
Why it is not enough
There are up to 1000 significant DE genes per type and 2795 unique DE genes in total
- What cell types are the most affected?
- How exactly they are affected?
The main questions:
There are up to 300 significant GO terms per type and 796 unique terms in total
Why it is not enough
DE depends on the depth and quality of the annotation
Compositional analysis
Gene expression analysis
Case-control analysis of single-cell studies: a fresh approach
What can we possibly do?
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
Composition analysis, cluster-free
Control
Multiple sclerosis
Composition analysis, cluster-free
Control
MS
Z-scores
Embedding
Graph
Composition analysis, cluster-free
Multiple-comparison adjustment
- Measurements are highly-correlated
- Large difference in one cell is good
- Large difference in many cell is even better
- Increasing number of cells should not decrease p-values
Composition analysis, cluster-free
Multiple-comparison adjustment
- Permute condition labels per sample ~200 times and estimate some difference statistic under null
- Apply graph smoothing
- Winsorize (1%) the scores
- Estimate min and max of the scores per permutation
- Approximate the null distributions (for minimums and maximums) with KDE
- P-value for each observed score is the tail probability of the null distribution for the observed statistic values
Composition analysis, cluster-free
Control
MS
Embedding
Graph
Adjusted z-scores
Z-scores
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-based
What cell types are affected the most?
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-based
Visualization of sample structure
Expression analysis, cluster-based
Visualization of sample structure
Expression analysis, cluster-based
Visualization of sample structure
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
Aggregate by samples
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
Probably can be improved by estimating adjusted z-scores
Differential expression stability
*Done by Anna and Rasmus
Gene expression analysis
Compositional analysis
Cluster-based
Cluster-free
Summary
