Unsupervised single-cell clustering with Asymmetric Within-Sample Transformation and per cluster supervised features selection
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.
Pagnotta Stefano Maria
Dept. of Science and Technology, Universit¨¤ degli Studi del Sannio
分子生物学生物科学研究方法、生物科学研究技术细胞生物学
Pagnotta Stefano Maria.Unsupervised single-cell clustering with Asymmetric Within-Sample Transformation and per cluster supervised features selection[EB/OL].(2025-03-28)[2025-05-17].https://www.biorxiv.org/content/10.1101/2023.05.17.541148.点此复制
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