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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).

6.2 Seurat Tutorial Redo. For this tutorial, we will be analyzing the a dataset of Peripheral Blood Mononuclear Cells (PBMC) freely available from 10X Genomics. There are 2,700 single cells that were sequenced on the Illumina NextSeq 500.

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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.

Note: sce is a seurat object of pbmc dataset. What I am unable to understand is that if FindClusters is working on the reduced dimensions (i.e. the 2D cell embeddings) or on the whole dataset, since the...

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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 ...

Detect clusters within the data. Find genes which define the clusters. Seurat (for general single cell loading and processing). Sleepwalk (for data projection visualisation...

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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...

如果我们查看Seurat对象的元数据( [email protected])，则计算出的每种不同分辨率都有单独的列。 # Explore resolutions [email protected] %>% View() 一开始选择分辨率，我们通常选择介于0.6或0.8之间的分辨率。

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Integrated Analysis in Seurat. Math et al. sce = computeSumFactors (sce, cluster = clusters) #. normalize, don't return log2 sce = normalize (sce, return_log = FALSE).

Cells were clustered with the FindClusters function (resolution = 0.2). Clusters in which at least 25% of cells were double-positive for CD1A and CD207 ("positive list"; normalized UMI greater 1), and no...

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# 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 ...

def findClusters_DBSCAN(data): ''' Cluster data using DBSCAN algorithm ''' #. create the classifier object dbscan = cl.DBSCAN(eps=1.2, min_samples=200) #.

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