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/
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 ...
Droplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack...
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...
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...
The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the proces
Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction ...
Briefly, for scmap, we used the scmap-cluster projection strategy that map the experimental dataset to “pbmcsca” reference dataset from SeuratData package. For SingleR, the HumanPrimaryCellAtlasData was used as the reference, and “label.main” was specified as the prediction level. For ...
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...
cell identification. results here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. the performance of the methods is evaluated using 27 publicly available single-cell rna sequencing datasets of different sizes,...