HCL DGE Data

Single-cell analysis is a valuable tool to dissect cellular heterogeneity in complex systems. Yet, a systematic single-cell atlas has not been achieved for human beings. We used single-cell RNA sequencing to determine cell type composition of all major human organs and construct a basic scheme for the human cell landscape (HCL). We reveal a single-cell hierarchy for many tissues that has not been well characterized previously. We present a ‘‘single-cell HCL analysis’’ pipeline that accurately defines human cell types; and exemplify its utility in stem cell biology. Finally, we perform single-cell comparative analysis for human and mouse cell atlas to reveal the conserved genetic networks in the mammalian system.<b></b><br><b>File Extension Specification</b><b></b><b></b><br><b>HCL_Fig1_adata.h5ad</b>: Use scanpy.api.read_h5ad to load AnnData. This AnnData stores the data used for HCL Figure 1.<b></b><br><b>HCL_Fig1_cell_Info</b>: The information, which includes the cell names, samples, clusters, stages, batches, donors and cell types for cells of data used for HCL Figure 1.<br><b>cluster_markers_HCL&amp;MCA1.1</b>: The cell type annotation and marker genes for 102 HCL clusters of Fig1 and 104 MCA1.1 clusters of SFig. <br><b><br></b><b>dge_raw_data.tar</b>: The raw digital expression matrix (dge) of more than 720,000 single cells sorted by tissues. The batch genes were not removed.<br><b>dge_rmbatch_data.tar</b>: The batch gene removed dge of more than 700,000 primary single cells sorted by tissues. Some tissues are not included due to relatively strong batch effects. This dataset can be used to make global tissue tSNE plot and do cross-tissue analysis.<br><b>annotation_rmbatch_data.tar</b>: The cell annotations, which include cluster ID, belonged tissues, age<b><i> (gestational age for fetal tissue)</i></b>, clusters and cell types for each rmbatch dge data.<br><b>annotation_cluster_info: <i>Modified</i></b> cell type annotation of each cluster in accord with ClusterID in annotation_rmbatch_data.zip<br><b>MCA1.1_adata.h5ad</b>: Use scanpy.api.read_h5ad to load AnnData. This AnnData stores the MCA1.1 data. <br><b>MCA1.1_cell_Info</b>: The information, which includes the cell names, samples, clusters, stages, batches, donors and ce;; types for cells of MCA1.1 data.<br><br>

单细胞分析是解析复杂系统中细胞异质性的重要工具。然而，目前尚未构建出系统性的人类单细胞图谱。我们通过单细胞RNA测序，明确了人体所有主要器官的细胞类型组成，并为人类细胞全景图（Human Cell Landscape，HCL）搭建了基础框架。我们揭示了多种此前未被充分表征的组织的单细胞层级结构。我们提出了一套可精准定义人类细胞类型的“单细胞HCL分析”流程，并以干细胞生物学研究为例展示了该流程的应用价值。最终，我们通过对人类与小鼠细胞图谱开展单细胞比较分析，揭示了哺乳动物系统中保守的遗传调控网络。

**文件说明**
**HCL_Fig1_adata.h5ad**：需使用scanpy.api.read_h5ad加载AnnData对象，该对象存储了HCL图1所用的数据集。
**HCL_Fig1_cell_Info**：该文件包含HCL图1所用数据集的细胞相关信息，具体包括细胞名称、样本、聚类簇、发育阶段、批次、供体及细胞类型。
**cluster_markers_HCL&MCA1.1**：该文件包含图1的102个HCL聚类簇以及补充图SFig.的104个MCA1.1聚类簇的细胞类型注释与标记基因信息。
**dge_raw_data.tar**：该压缩包包含按组织分类的72万余个单细胞的原始数字表达矩阵（digital expression matrix，DGE），未去除受批次效应影响的基因。
**dge_rmbatch_data.tar**：该压缩包包含按组织分类的70万余个原代单细胞的已去除批次效应的数字表达矩阵。由于部分组织的批次效应较强，该数据集未包含这些组织。该数据集可用于绘制全局组织tSNE图及开展跨组织分析。
**annotation_rmbatch_data.tar**：该压缩包包含每个已去除批次效应的数字表达矩阵数据的细胞注释信息，具体包括聚类簇ID、所属组织、年龄（胎儿组织则为胎龄）、聚类簇及细胞类型。
**annotation_cluster_info（已修改）**：该文件包含与annotation_rmbatch_data.zip中聚类簇ID相对应的各聚类簇的细胞类型注释信息。
**MCA1.1_adata.h5ad**：需使用scanpy.api.read_h5ad加载AnnData对象，该对象存储了MCA1.1数据集。
**MCA1.1_cell_Info**：该文件包含MCA1.1数据集的细胞相关信息，具体包括细胞名称、样本、聚类簇、发育阶段、批次、供体及细胞类型。

搜集汇总

背景与挑战

背景概述

HCL DGE Data是一个人类单细胞RNA测序数据集，包含超过70万个单细胞的原始和处理后的数字表达矩阵，以及详细的细胞注释和聚类信息。该数据集用于构建人类细胞景观(HCL)图谱，支持跨组织分析和比较人类与小鼠细胞图谱的研究。