Abstract：
This chapter shows applying the Asymmetric Within-Sample Transformation 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 noninformative 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/downregulated significant genes. To this aim, we adopt a GLM model as implemented in DESeq2 package. We render the results in graphical mode. While the tools are standard heat maps, we introduce some data scaling to clarify the results\' reliability.
摘要：
本章显示了将不对称样品内转化应用于与先前的缺失填补匹配的单细胞RNA-Seq数据。不对称转化是一种特殊的winsorization，可以平坦低表达强度并保留高表达基因水平。在标准的分层聚类算法之前，中间步骤根据应用于每个基因熵估计的阈值来去除非信息性基因。在聚类之后，一个时间密集的算法被证明可以揭示与每个簇相关的分子特征。该步骤实施重采样算法以生成随机基线来测量上调/下调的显著基因。为了这个目标,我们采用在DESeq2包中实现的GLM模型。我们以图形模式呈现结果。虽然这些工具是标准的热图，我们引入了一些数据缩放来阐明结果的可靠性。
