Scripts and data for the paper: Differential analysis of binarized single-cell RNA sequencing data captures biological variation

Scripts and data for the paper: Differential analysis of binarized single-cell RNA sequencing data captures biological variation<br>Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts.<br>

本论文配套脚本与数据集：《二值化单细胞RNA测序（single-cell RNA sequencing）数据的差异分析可捕获生物学变异》
单细胞RNA测序数据以大量零计数为典型特征，但越来越多的研究证据表明，这些零值实则反映生物学变异，而非技术伪影（technical artifacts）。本研究提出采用二值化表达谱（binarized expression profiles），以识别单细胞RNA测序数据中的生物学变异效应。借助16套公开可用数据集与模拟数据集，本研究证实，单细胞表达数据的二值化表征可精准呈现生物学变异，且相较于计数数据，能更稳健地揭示转录本（transcripts）的相对丰度。