Clusters of cells were determined using the Seurat FindClusters function with 10 PCs and resolution=0.15; a total of 8 clusters were identified and cell type of each was determined using previously identified marker genes (Seurat FindMarkers function).
Apr 06, 2020 · Clusters were identified using the FindClusters function from Seurat, using the first 20 dimensions in the CCA space. Expression of known marker genes was assessed to assign cell types to each cluster, resulting in the identification of six major cell types.
Punjab text book class 9
简介  由于单细胞数据的高维度，基因长度差异，覆盖度差异及实验过程中的偏好性等因素，对前期数据进行有质量的标准化，对后续分析结果解读至关重要。标准化的分析比较多，对于常规的群体RNA分析而言，各种软件包也有相应的标准化方式，基因的定量也有RPKM ，TPM等标准化指标，单 ...
Benq zowie software
find all markers distinguishing cluster 5 from clusters 0 and 3 cluster5.markers <- FindMarkers Seurat provides the StashIdent() function for keeping cluster IDs; this is...
Access point configuration step by step
Integrated Analysis in Seurat. Math et al. sce = computeSumFactors (sce, cluster = clusters) #. normalize, don't return log2 sce = normalize (sce, return_log = FALSE).
What is the basis of gifted property
# 确定k-近邻图 seurat_integrated <- FindNeighbors(object = seurat_integrated, dims = 1:40) # 确定聚类的不同分辨率 seurat_integrated <- FindClusters(object = seurat_integrated, resolution = c(0.4, 0.6, 0.8, 1.0, 1.4)) # 如果我们看一下Seurat对象的元数据([email protected])，每一个不同的分辨率都有 ...