seurat基础

• nFeature_RNA is the number of genes detected in each cell.
  nFeature_RNA是每个细胞中检测到的基因数

• nCount_RNA is the total number of molecules detected within a cell.
  nCount_RNA是一个细胞中检测到的分子总数

• Low nFeature_RNA for a cell indicates that it may be dead/dying or an empty droplet.
  nFeature_RNA低表明这可能是一个已死亡或即将死亡的细胞，或者是个空液滴

• High nCount_RNA and/or nFeature_RNA indicates that the "cell" may in fact be a doublet (or multiplet).
  nFeature_RNA或者nCount_RNA高则说明这个“细胞”可能实际上这个液滴包含两个及以上细胞

• In combination with %mitochondrial reads, removing outliers from these groups removes most doublets/dead cells/empty droplets, hence why filtering is a common pre-processing step.
  和线粒体reads综合来看，从这些组中去除异常值可以剔除多数双峰、死细胞、空液滴。因此过滤是一个常见的预处理步骤

• The NormalizeData step is basically just ensuring expression values across cells are on a comparable scale. By default, it will divide counts for each gene by the total counts in the cell, multiply that value for each gene by the scale.factor (10,000 by default), and then natural log-transform them.
  默认情况下，NormalizeData将每个基因的计数除以细胞中的总计数，将每个基因的值乘以 scale.factor（默认为 10,000），然后对它们进行自然对数变换

参考问答