What is DESeq2-inspired Model?¶
Multi-Sample Session Specific
This feature is only available in multi-sample sessions.
DESeq2-inspired Model: an in-browser, client-side statistical engine in CartoScope designed for real-time differential expression analysis across multi-sample biological cohorts without requiring external R/Bioconductor server backends.
How does the DESeq2-inspired model work?
- Median-of-Ratios Normalization: Computes geometric means across sample expression profiles and median sample ratios to normalize for library size and sequencing depth differences across slides.
- Negative Binomial Model: Models sample-level count variances using a Negative Binomial distribution rather than a simple Poisson model.
- Closed-Form Parametric Dispersion: Uses analytical parametric dispersion estimation to model sample-to-sample variance in JavaScript/Web Worker threads.
- Wald Z-Score Testing: Calculates Wald test statistics (W = log2 FC / SE) and stable two-tailed p-values.
How is this different from actual R/Bioconductor DESeq2?
CartoScope's deseq2 option is not running the actual R DESeq2 package.
The key differences are:
| Feature | DESeq2 | CartoScope DESeq2-inspired Model |
|---|---|---|
| Requirements | R runtime environment / server backend. | Perform in CartoScope with simple click. No other dependencies. |
| Model Fitting | Fits Generalized Linear Models (GLMs) via iterative maximum likelihood. | Uses closed-form analytical equations for real-time calculations. |
| Dispersion Estimation | Fits a smooth trend curve across all genes and performs Empirical Bayes shrinkage. | Uses parametric dispersion estimation combined with sample-level variance. |
| Primary Purpose | Final publication-grade statistical modeling in R. | Instant interactive web screening, volcano plot exploration, and filtering. |
Why use the DESeq2-inspired model over the Chi-squared test?
While Chi-squared (chisq) is fast for screening pooled counts, the DESeq2-inspired model provides robust biological cohort comparisons:
- Accounts for Biological Variance: Operates at the sample level (pseudo-bulk) to model sample-to-sample variability, preventing false positives from single-slide outliers or batch effects.
- Robust Normalization: Uses Median-of-Ratios size factor normalization to prevent sequencing depth differences or dominant genes from distorting fold-change (log2 FC) values.
- Stable Fold Changes: Incorporates pseudocount scaling to avoid artificially inflated fold changes for low-count genes.
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