Fig. 3

A modified regularization procedure improves the robustness of parameter estimates. A Left: Estimated parameter estimates for θ on the Fetal sci-RNA-seq3 dataset [76], using the original regularization procedure from [9] (v1 regularization). Regularized estimates were learned using all cells (purple line), or downsampled cell subsets. Right: Same as (A), but using a modified procedure where the GLM slope was fixed, and genes where σ²≤μ and μ<0.001 were excluded from regularization (v2 regularization) which improves robustness, and enables us to learn parameter estimates from a subsample of 2,000 cells. B Correlation of Pearson residual variance after applying a NB GLM with v2 regularization where parameters were estimated from all 377,456 cells (x-axis), and a subsample of 2000 cells (y-axis). C Green curve: total sctransform run time as a function of dataset size, using all cells to estimate parameters. Orange curve: total runtime when using a subsample of 2000 cells, which increases computational efficiency for large datasets