MCA DGE Data

MCA single cell DGE data (Cells with &gt;500UMI ) for the following manuscript:Mapping the Mouse Cell Atlas by Microwell-seq<br>MCA_DGE.rar: The raw digital expression matrix (dge) of more than 400,000 single cells sorted by tissues. For each cell, more than 500 transcripts are expressed. The batch effects are not been removed.<br>MCA_Figure2-batch-removed.txt.tar.gz: The batch removed dge of approximately 60,000 cells of high quality. 1500 cells were sampled from 43 tissues respectively. This sampled data is used for Figure 2.<br>MCA_cell-assignments.xlsx: The annotation of cells, which includes the cell names, cluster ID, belonged tissues, experimental batches and cell barcodes.<br>MCA_All-batch-removed.zip: The batch effect removed dge of more than 400,000 single cells sorted by tissues. This dataset can be used to make global tissue tSNE plot and do cross-tissue analysis.<br>Batch effect removalFor cross tissue comparison, we removed the batch gene background to improve presentation. We assume that for each batch of experiment, the cell barcodes with less than 500UMI correspond to the empty beads exposed free RNA during the cell lysis, RNA capture and washing steps. The batch gene background value is defined as the average gene detection for all cellular barcodes with less than 500 UMI, multiplied by a coefficient of 2, and then rounded to the nearest integer. Genes detected in less 25% of all cells are removed from the batch gene background list. We subtract the batch gene background for each cell from the digital expression matrix before making the cross tissue comparison figures.

本数据集为配套论文《基于Microwell-seq技术绘制小鼠细胞图谱》（Mapping the Mouse Cell Atlas by Microwell-seq）的单细胞数字基因表达谱（Digital Gene Expression Profile, DGE）数据，筛选标准为细胞的唯一分子标识符（Unique Molecular Identifier, UMI）数量大于500。
MCA_DGE.rar：按组织分类的40余万个单细胞原始数字表达矩阵文件。该数据中每个细胞的转录本表达量均超过500，且未去除批次效应（batch effect）。
MCA_Figure2-batch-removed.txt.tar.gz：经过批次效应去除处理的高质量单细胞数字表达谱数据，共包含约6万个细胞。该数据从43种组织中各采样1500个细胞，用于绘制论文中的图2。
MCA_cell-assignments.xlsx：细胞注释信息文件，包含细胞名称、聚类ID（cluster ID）、所属组织、实验批次以及细胞条形码（cell barcode）等信息。
MCA_All-batch-removed.zip：按组织分类的40余万个单细胞经过批次效应去除处理后的数字表达矩阵文件。该数据集可用于绘制全局组织tSNE（t-distributed Stochastic Neighbor Embedding）图并开展跨组织分析。
批次效应去除方法
为开展跨组织比较，我们通过去除批次基因背景以优化可视化效果。我们假设：在每一轮实验中，UMI数量低于500的细胞条形码对应细胞裂解、RNA捕获及清洗步骤中暴露于游离RNA的空白磁珠。批次基因背景值的计算方式为：对所有UMI数量低于500的细胞条形码的基因检测均值乘以系数2，随后取最接近的整数。在所有细胞中检出率低于25%的基因将被从批次基因背景列表中移除。在绘制跨组织比较图表前，我们将从每个细胞的数字表达矩阵中减去对应的批次基因背景值。