Many existing pipelines for scRNA-seq data apply pre-processing steps such as normalization or imputation to account for excessive zeros or “drop-outs." Here, we extensively analyze diverse UMI data sets to show that clustering should be the foremost step of the workflow. We observe that most drop-outs disappear once cell-type heterogeneity is resolved, while imputing or normalizing heterogeneous data can introduce unwanted noise. We propose a novel framework HIPPO (Heterogeneity-Inspired Pre-Processing tOol) that leverages zero proportions to explain cellular heterogeneity and integrates feature selection with iterative clustering. HIPPO leads to downstream analysis with greater flexibility and interpretability compared to alternatives.

中文翻译：

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揭秘单细胞 UMI 数据中的“辍学”

许多现有的 scRNA-seq 数据管道应用标准化或插补等预处理步骤来解释过多的零或“丢失”。在这里，我们广泛分析了不同的 UMI 数据集，以表明聚类应该是最重要的步骤工作流程。我们观察到，一旦细胞类型异质性得到解决，大多数辍学就会消失，而对异质数据进行插补或归一化可能会引入不需要的噪音。我们提出了一个新的框架 HIPPO（异质性启发的预处理工具），它利用零比例来解释细胞异质性并将特征选择与迭代聚类相结合。与替代方案相比，HIPPO 导致下游分析具有更大的灵活性和可解释性。