Seurat 4.0 | 单细胞转录组数据整合(scRNA-seq integration) 对于两个或多个单细胞数据集的整合问题,Seurat 自带一系列方法用于跨数据集匹配(match) (或“对齐” ,align)共享的细胞群。这些方法首先识别处于匹配生物状态的交叉数据集细胞(“锚”,anchors),可以用于校正数据集之间的技术差异(如,批次效应校正),也可以...
对于两个或多个单细胞数据集的整合问题,Seurat 自带一系列方法用于跨数据集匹配(match) (或“对齐” ,align)共享的细胞群。这些方法首先识别处于匹配生物状态的交叉数据集细胞(“锚”,anchors),可以用于校正数据集之间的技术差异(如,批次效应校正),也可以用于不同实验条件下的scRNA-seq的比较分析。 作者使用了两种不...
Here we performlatent semantic indexingto reduce the dimensionality of the scATAC-seq data. This procedurelearns an ‘internal’ structurefor the scRNA-seq data, and is important when determining the appropriate weights for the anchors when transferring information. We utilizeLatent Semantic Indexing (L...
Seurat v4包含一组方法,用于跨数据集匹配(或“对齐”)共享的细胞群。这些方法首先识别处于匹配生物状态的交叉数据集细胞(“锚”),可以用于纠正数据集之间的技术差异(即批效应校正),并在不同实验条件下执行比较scRNA-seq分析。 下面,我们使用了两种不同state(a resting or interferon-stimulated state)的人类PBMC细胞进...
scRNA-seq dataspatial transcriptomics sequencing dataHVGsNumerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA...
datasets. These methods first identify cross-dataset pairs of cells that are in a matched biological state (‘anchors’), can be used both to correct for technical differences between datasets (i.e. batch effect correction), and to perform comparative scRNA-seq analysis of across experimental ...
如何确定gene.to.label的基因?可以通过寻找std最大的方式吗? library(dplyr) avg.t.cells avg.t.cells %>% select(CTRL,STIM) %>% unlist () %>% sd() ''' As you can see, many of the same genes are upregulated in both of these cell types and likely represent a conserved interferon respo...
Here, we develop a strategy to “anchor” diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq...
这些方法首先识别处于匹配生物状态的交叉数据集细胞(“锚”)，可以用于纠正数据集之间的技术差异(即批效应校正)，并在不同实验条件下执行比较scRNA-seq分析。下面，我们使用了两种不同state（ a resting or interferon-stimulated state ）的人类PBMC细胞进行了比较分析。方法详细描述见： Stuart , Butler ...
I am trying to integrate the gene activity matrix of scATAC-seq with scRNA-seq. The gene activity matrix is not obtained from original scATAC-seq data. Rather, it is obtained from imputed scATAC-seq data by a recent method. In integratio...