snare <- NormalizeData(snare) snare <- ScaleData(snare) snare <- RunPCA(snare, npcs = 30) snare <- RunUMAP(snare, dims = 1:30, reduction.name = "umap.rna") snare <- FindNeighbors(snare, dims = 1:30) snare <- FindClusters(snare, resolution = 0.5, algorithm = 3) ## Modularity...
片段文件:https://signac-objects.s3.amazonaws.com/snareseq/fragments.so... 片段文件的索引文件:https://signac-objects.s3.amazonaws.com/snareseq/fragments.so... 用于从原始数据生成片段文件的代码:https://github.com/timoast/SNARE-seq 数据加载 首先构建了一个Seurat对象,它包含了两种不同的检测类型:...
library(Signac)library(Seurat)library(ggplot2)library(EnsDb.Mmusculus.v79)# load processed data matrices for each assayrna<-Read10X("../vignette_data/snare-seq/GSE126074_AdBrainCortex_rna/",gene.column=1)atac<-Read10X("../vignette_data/snare-seq/GSE126074_AdBrainCortex_atac/",gene.column=1...
Here, we describe a single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-seq2) assay, implemented with cellular combinatorial indexing. This method involves tagmentation within permeabilized and fixed single-nucleus isolates to capture accessible chromatin (AC) regions, followed ...
用SNARE的RNA-seq表达数据(SNARE-seq expression assay)和可及性数据(SNARE-seq accessibility assay)分别做细胞聚类,发现二者的profile有很好的一致性:那些在特定淋巴细胞中高表达的marker gene同样也具有对应的开放染色质谱(高表达的TF有可及的motif)。
与scRNA-seq 整合 接下来,可以通过成人小鼠大脑的单细胞RNA测序(scRNA-seq)数据集的标签,来对当前数据集中的细胞类型进行分类标注。 # label transfer from Allen brainallen<-readRDS("../vignette_data/allen_brain.rds")allen<-UpdateSeuratObject(allen)# use the RNA assay in the SNARE-seq data for integ...