我们可以通过绘制伪时间坐标的直方图来找到这些亚稳态。 3.3 细胞水平的联合分析 - Cell-level analysis unification 聚类和轨迹推断代表单细胞数据的两个不同视角,并可以在粗粒度(coarse‐grained)的图形展示中进行统一。用点代表单细胞簇,轨迹作为簇之间相连的边,可以同时展示数据的静态和动态属性。这一联合分析在PAGA...
《Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data》 Introduction 目前基于单细胞测序主要有两种建库技术,一种是主打细胞数量的10X platform,该技术的特点是提出了unique molecular identifiers(UMI),即每一个细胞对应唯一的barcode作为区分于其他细胞的标识符;简而言之就是测到的...
cell-level downstream analysis -细胞水平的下游分析 gene-level downstream analysis -基因水平的下游分析 1. Pre-processing and visualization experimental workflow single-cell dissociation, single-cell isolation, library construction, and sequencing 分别为以上四步走 As a first step, asingle‐cell suspension ...
文献阅读-《Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data》 文献名称:教程:单细胞 RNA 测序数据的计算分析指南 文献期刊:nature protocols 发表时间:2021-1-9 摘要 单细胞 RNA 测序技术 (scRNA-seq) 是一种流行且功能强大的技术,可以分析大量单个细胞的整个转录表达谱。...
However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce ...
Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requ
final_counts folder. This tutorial will useDESeq2to normalize and perform the statistical analysis ...
However, it is challenging to create broadly applicable experimental designs because each experiment requires the user to make informed decisions about sample preparation, RNA sequencing and data analysis. To facilitate this decision-making process, in this tutorial we summarize current methodological and ...
【6】Andrews, T.S., Kiselev, V.Y., McCarthy, D., and Hemberg, M. (2021). Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat. Protoc. 16. 在学习分析之前,先看看,尤其是标红的文献,了解下分析流程,分析过程中使用的软件,各种软件的优缺点等等背景知识。
Doing differential gene expression analysis requires less sequencing depth than transcript reconstruction so when pooling samples it is critical to keep the objective of the experiment in mind. In this activity we will use subsets of experimentally generated datasets. The dataset used here was generated...