Unsupervised single-cell clustering with Asymmetric Within-Sample Transformation and per cluster supervised features selection

Abstract This chapter shows applying the Asymmetric Within-Sample Transformation [14] to single-cell RNA-Seq data matched with a previous dropout imputation. The asymmetric transformation is a special winsorization that flattens low-expressed intensities and preserves highly expressed gene levels. Before a standard hierarchical clustering algorithm, an intermediate step removes non-informative genes according to a threshold applied to a per-gene entropy estimate. Following the clustering, a time-intensive algorithm is shown to uncover the molecular features associated with each cluster. This step implements a resampling algorithm to generate a random baseline to measure up/down-regulated significant genes. To this aim, we adopt a GLM model [10] as implemented in DESeq2 [9] package. We render the results in graphical mode. While the tools are standard heat maps, we introduce some data scaling so that the results’ reliability is crystal clear.