Sampling time-dependent artifacts in single-cell genomics studies: scRNA-seq data

Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention. This repository contains the expression matrices and Seurat objects associated with the scRNA-seq data of the manuscript: "Sampling time-dependent artifacts in single-cell genomics studies" published in Genome Biology in 2020. The purpose of this repo is to share processed files and metadata for immediate access and reproducibility. The code to analyze it is thoroughly documented at the associated Github repository (https://github.com/massonix/sampling_artifacts).成熟稳健的实验方案与自动化技术，现已支撑大规模单细胞RNA测序与转座酶染色质可及性测序（ATAC sequencing）实验，并可将其应用于生物样本库与临床队列研究。然而，样本获取过程中引入的技术偏差可能会阻碍可靠且可复现的实验结果产出，因此在启动大规模数据生产前，需开展系统性基准测试。本研究证实了采样过程中引入的基因表达与染色质可及性伪影的存在及其影响范围，并提出了对应的实验与计算层面的防控方案。本数据集仓库收录了2020年发表于《Genome Biology》的论文《Sampling time-dependent artifacts in single-cell genomics studies》中所涉及的单细胞RNA测序表达矩阵与Seurat对象。本仓库的搭建目的在于共享已预处理的数据文件与元数据，以便研究者快速获取并实现实验结果复现。相关的数据分析代码已在关联的GitHub仓库（https://github.com/massonix/sampling_artifacts）中完成了详尽的文档说明。