A Bayesian model for single-cell transcript expression analysis with MERFISH data
Johannes Köster
2016
MERFISH
FISH
Fluorescence in-situ hybridization:
- label RNA with fluorescent probes
- see RNA molecules in single cells
Problem:
needs 1 color per transcript
MERFISH
...
MERFISH
Problem with raw counts:
20% loss and misidentification
MERFISH
Known error probabilities:
1→0 error: 10%
0→1 error: 4%
Goal:
- Bayesian model on top of error probabilities
- estimate gene/transcript expression
- "Bayesian"-style differential expression analysis
Approach
Urn model for
expression likelihoods
Bayesian model for differential expression
Example results
Results
Simulation
Pr(0→1) Pr(1→0)
simulated
hybridization
Bayesian model recovers biased counts
Bayesian model recovers biased counts
Application
Characterize batch effects in real data
Published dataset:
- ~200 fibroblast cells
- 7 batches
- same biological condition
Question:
Are expression profiles influenced by batch effects?
t-SNE analysis
cell size
cell position
batch
Approach
- calculate posterior estimate of coefficient of variation (CV) between means of batches
- null model: CV < 0.5
- control expected FDR at 5%
Differentially expressed genes
Gene ontology enrichment
Term | expected | observed | size |
---|---|---|---|
response to temperature stimulus | 0.36 | 3 | 6 |
cellular response to heat | 0.36 | 3 | 6 |
negative regulation of endopeptidase activity | 0.12 | 2 | 2 |
second-messenger-mediated signaling | 0.12 | 2 | 2 |
positive regulation of protein kinase B signaling | 0.12 | 2 | 2 |
regulation of peptidase activity | 0.12 | 2 | 2 |
pos. regulation of reactive oxygen species metabolic process | 0.12 | 2 | 2 |
controlled FDR at 5% with Benjamini-Yekuteli
Conclusion
Bayesian model for gene expression analysis on MERFISH data:
- provides estimates of expression, fold change and coefficent of variation
- credible intervals, expected FDR
Simulation:
can correct for biases in MERFISH data
Application:
applied to characterize batch effects
Acknowledgements
Shirley Liu
Myles Brown
Bo Li
Peng Jiang
Eric Severson
A Bayesian model for gene expression analysis with MERFISH data
By Johannes Köster
A Bayesian model for gene expression analysis with MERFISH data
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