Research map / Probabilistic and scientific modeling / Molecules and proteins
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add cell-specific scalings (alpha_j, beta_j) to the HDPMM so normalization and clustering are done jointly | BISCUIT vs naive HDPMM · Synthetic data | F-score | Helped |
| Replace joint model with global normalization (GLMnorm or MeanNorm) before HDPMM clustering | HDPMM pipeline · Synthetic data | F-score | Hurt |
| Normalize with BASiCS before clustering versus clustering without normalization | PG, SpecClust, HDPMM on Zeisel data · Zeisel et al. 2015 | clustering quality | Hurt |
| Infer cell-specific scalings and impute/normalize, versus library-size normalization | Down-sampled Zeisel data (500 cells) · Dropout recovery | imputation of down-sampled zeros | Helped |
| Use Student-t or negative binomial data instead of Gaussian (model mismatch) | BISCUIT · Synthetic data | F-score | No clear effect |
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