Next, we compared the performance of different normalization procedures, i.e. interarray median centering over all spots without QC flagging, quantile normalization20 and RSE. With increasing effect size of experimental treatment, the AUC of all normalization methods increases. RSE corrects gradients and filters out the induced spots and elicits the best performance (Fig. 4a). Upon increasing the intraslide gradient strength, the necessity of gradient correction becomes more and more clear.
When uncorrected raw data are used as an input for subsequent interslide normalization, classification performance is substantially lower than after intra-array normalization in which the intraslide gradient correction was performed, but without RSE (Fig. 4b). When the percentage of induced spots increases, these spots have an increasing influence on the overall distribution of spot intensities, leading to a decreasing performance of median-centering and quantile normalization. Contrarily, RSE effectively filters out most of the effect of induced spots on normalization and shows a steady performance over a range of induced spots (Fig. 4c). Intraslide gradient correction and RSE normalization are both median-centerings that are performed locally. Large numbers of off-spots lead to an increase of the area that is used to find the N nearest spots used for normalization, which intuitively could affect the success of local normalization methods. The normalization methods we propose perform robustly over a large range of spots present on the array, i.e. even a large fraction of off-spots does not have a negative influence on classification performance (Fig. 4d).
Yeah, however, I think it's mostly not about generic methods, but aboutoverloaded [[Get]] / [[Put]]. And in ES3 and ES5 overloaded [[Put]] /[[DefineOwnProperty]] is not generic but special for arrays. The same Iassume this feature is special for arrays. Really, I don't see any bigissues with backward compats. They are so minimal that may be ignored.
POWSC Determining the sample size for adequatepower to detect statistical significance is a crucial step at thedesign stage for high-throughput experiments. Even though a numberof methods and tools are available for sample size calculation formicroarray and RNA-seq in the context of differential expression(DE), this topic in the field of single-cell RNA sequencing isunderstudied. Moreover, the unique data characteristics present inscRNA-seq such as sparsity and heterogeneity increase thechallenge. We propose POWSC, a simulation-based method, to providepower evaluation and sample size recommendation for single-cell RNAsequencing DE analysis. POWSC consists of a data simulator thatcreates realistic expression data, and a power assessor thatprovides a comprehensive evaluation and visualization of the powerand sample size relationship. 076b4e4f54