Seurat:https://satijalab.org/seurat/get_started.html; Scanpy:Tutorials - Scanpy 1.6.2.dev0+g099be48b.d20210114 documentation). 基于以上问题,作者综述了目前scRNA-seq分析流程、步骤和方法(Figure 1),提出了一套目前最佳的实践分析流程,详见theislab/single-cell-tutorial. 这套分析流程在Jupyter–Ipython...
常用的归一化Normalize处理目的是将离散程度很大的数据集中化,对数据转换能够让同一基因在不同样本具有可比性(RPKM/TPM),在Seurat中LogNormalize便是利用log1p(value/colSums[cell-idx] *scale_factor) 常用的标准化Scale是基于之前归一化结果,再添加z-score计算,考虑到了不同样本对表达量的影响,消除了表达的平均水平...
guide to scRNA-seq analysis in neuroscience.We provide a step-by-step Seurat tutorial for comparing gene expression between conditions.The workflow highlights QC, integration, clustering, and biological interpretation steps.We use Docker to offer an automated and reproducible single-cell analysis ...
Detailed workflow tutorial 35:53 9 The Beginner's guide to bulk RNA sequencing vs single-cell RNA Sequencing 12:24 10 Single-cell Trajectory analysis using Monocle3 and Seurat _ Step-by-step tuto 48:43 11 Automatic cell-annotation for single-cell RNA-Seq data using a reference data 34:12...
For cell type enumeration, we employed Spatial Seurat, which showed strong concordance with known global proportions in simulated ST datasets (Extended Data Fig. 3a). To approximate the number of cells per spot, we implemented a simple approach based on RNA abundance estimation (Methods). This ...
The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so
Seurat, Seurat_SNN To test Seurat, we followed the guided clustering workflow recommended in the tutorial at [11] by first applying the recommended cell quality filtering based on the number of detected genes, minimum 200 per cell, and percentage of reads from mitochondrial genes. Then, as reco...
Seurat v3 is the recommended method for batch integration [11]; Scran is widely known for normalization of the non-full-length dataset [12]; Monocle is expert at the reconstruction of the cell fate trajectory [3, 9]; Scanpy is scalable in dealing with large datasets of more than one ...
Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering...
‘zebrafish single-cell atlas of perturbed embryos’: single-cell transcriptomic data from 1,812 individually resolved developing zebrafish embryos, encompassing 19 timepoints, 23 genetic perturbations and a total of 3.2 million cells. The high degree of replication in our study (eight or more ...