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- W4377242511 abstract "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." @default.
- W4377242511 created "2023-05-23" @default.
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- W4377242511 date "2023-05-21" @default.
- W4377242511 modified "2023-09-27" @default.
- W4377242511 title "Unsupervised single-cell clustering with Asymmetric Within-Sample Transformation and per cluster supervised features selection" @default.
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- W4377242511 doi "https://doi.org/10.1101/2023.05.17.541148" @default.
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