Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data - Datasets

Includes raw and processed copies of the scRNA-seq datasets used for the paper: '<strong>How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data.'</strong> <strong>Real scRNA-seq.zip </strong>contains the Abundant (subset1) and Rare (subset 2) subsets generated to represent discretely structured datasets (sourced from<strong> </strong> Wegmann et al. 2019) and the continuously structured data (sourced from Popescu et al. 2019). <strong>Simulated scRNA-seq.zip</strong> contains the Abundant, Moderately-Rare and Ultra-Rare subsets for discretely and continuously structured datasets. All data was simulated using the PROSSTT package in Python 3.8, as well as the dataset containing the labels to re-produce Figure 3 of the manuscript. <strong>Results.zip </strong>contains the results for all datasets from the full analysis, in a pickled python dictionary. Code to read in and visualise results is available on the projects github The scripts for the dataset generation, processing and visualisation of results are available at our github for the scProcimitE package, and documentation is available here.本数据集包含用于论文**《数据结构如何影响细胞间相似性？评估结构属性对单细胞RNA测序（single-cell RNA-seq）数据中邻近指标性能的影响》**所使用的原始与处理后单细胞RNA测序（single-cell RNA-seq）数据集副本。**Real scRNA-seq.zip** 包含源自Wegmann等人2019年研究的、用于表示离散结构数据集的丰裕（子集1）与稀有（子集2）子集，以及源自Popescu等人2019年研究的连续结构数据集。**Simulated scRNA-seq.zip** 包含用于离散与连续结构数据集的丰裕、中度稀有及极度稀有子集。所有数据均通过Python 3.8环境下的PROSSTT包模拟生成，同时附带用于复现论文图3的带标签数据集。**Results.zip** 包含完整分析流程中所有数据集的结果，以Python序列化字典（pickled Python dictionary）格式存储。用于读取与可视化结果的代码可在本项目的GitHub仓库获取；用于数据集生成、处理及结果可视化的脚本可在scProcimitE包的GitHub仓库获取，相关文档可在此处获取。