Last updated: 2023-12-04
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Knit directory: DEanalysis/
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In this project, we compare a few methods performing the DE analysis results. Each of them might use different input data ,different statistical model and different FDR control on p-values.
| Poisson-glmm | Binomial-glmm | Pb-DESeq2 | Pb-edgeR | MAST | Wilcox | MMvst | MMpoisson | |
|---|---|---|---|---|---|---|---|---|
| Package | Our method | Our method | Muscat | Muscat | MAST | Seurat | Muscat | Muscat |
| Input | UMI | zero proportion | UMI | CPM | CPM | Integrated/Log normalized | VST | UMI |
| Model base | Poisson glmm | Binomial glmm | Negative binomial model | Negative binomial model | Zero-inflated model | Rank-sum test | Linear mixed model | Poisson glmm |
| Normalization | X | X | V | V | V | V | V | V |
| Normalization method | Median of ratio size factor, variance stabilizing transformation in model | In data, trimmed mean of M values (TMM) in model | In data | In data | In data | Library size factor as offset |
Only genes that pass the threshold for gene expression frequency will be considered as inputs.
For each gene, we run a poisson glmm method (glmmPQL) on the raw counts with cellgroups as fixed effect and repgroups as random effect \[ \begin{aligned} X_{cgk}|\lambda_{cgk} & \sim Poisson(\lambda_{cgk})\\ \log \lambda_{cgk} & = \mu_g + X_c{\beta_g} + \epsilon_{gk}\\ \end{aligned} \]
If the algorithm doesn’t converge, the gene will be excluded.
Only genes that pass the threshold for gene expression frequency will be considered as inputs.
We take the zero proportion of each gene as the response in the binomial model. (\(1\) if the read count is zero; otherwise \(0\).)
For each gene, we run a binomial glmm method (glmmPQL) on the zero proportion with cellgroups as fixed effect and repgroups as random effect \[ \begin{aligned} \mathbb{1}_{X_{cgk}=0}|p_{cgk} & \sim Bernoulli(p_{cgk})\\ \log \frac{p_{cgk}}{1-p_{cgk}} & = \mu_g + X_c\beta_{g} + \epsilon_{gk}\\ \end{aligned} \]
If the algorithm doesn’t converge, the gene will be excluded.