在上一篇帖子中,我介绍了CellPhoneDB的原理.实际操作,以及一些值得注意的地方.这一篇继续细胞通讯分析的可视化. 公众号后台回复20210723获取本次演示的测试数据,以及主要的可视化代码. 所有的数据和结果文件均已打包,下载后直接就能跑下面的代码画图. 下面的代码可以绘制对称热图 (如果你不清楚为啥热图要沿着对角线对称,可以看一下之前的推文) library(tidyverse) library(RColorBrewer) library(scales) pvalues=read.ta

原题: Description While swimming at the beach, Mike has accidentally dropped his cellphone into the water. There was no worry as he bought a cheap replacement phone with an old-fashioned keyboard. The keyboard has only ten digital equal-sized keys, loc

Cellphone Typing Time Limit: 5000ms Memory Limit: 131072KB   This problem will be judged on UVA. Original ID: 1252664-bit integer IO format: %lld      Java class name: Main Prev Submit Status Statistics Discuss Next Font Size: + - Type:   None Graph

Mike and Cellphone 题目链接: http://acm.hust.edu.cn/vjudge/contest/121333#problem/E Description While swimming at the beach, Mike has accidentally dropped his cellphone into the water. There was no worry as he bought a cheap replacement phone with an old

写一半去开程序的时候不小心关了网页,LOFTER我都不奢望加入代码高亮,最起码我关闭的时候弹个对话框,再不济也给我定时保存一下草稿吧. A. Mike and Cellphone time limit per test 1 second memory limit per test 256 megabytes input standard input output standard output While swimming at the beach, Mike has accidentally

许多分析软件 : https://github.com/seandavi/awesome-single-cell#software-packages Smart-seq.CEL-seq.SCRB-seq和Drop-seq.Smart-seqSMART(Switching mechanism at 5’ end of the RNA transcript)是一个具有里程碑意义的重要技术.实际上,能够从单细胞生成全长cDNA的测序方案并不多,Smart-seq就是其中之一.对于等位基因特异性表达或者

Seurat是一个老牌的单细胞分析工具了(satija的力作),我之前测试过,但是没怎么用. 最近发现这个工具又publish在了NBT上,所以很有必要看一下这篇文章. Integrating single-cell transcriptomic data across different conditions, technologies, and species 主要目的:identifying subpopulations of cells that are present across m

这么多工具和基因组版本,选择困难症犯了,到底用哪个好呢? 2018 nature - Developmental diversification of cortical inhibitory interneurons : ENSEMBL release 84 Mus musculus genome 2017 Molecular Cell - Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dyn

个人的一些碎碎念: 聚类,直觉就能想到kmeans聚类,另外还有一个hierarchical clustering,但是单细胞里面都用得不多,为什么?印象中只有一个scoring model是用kmean进行粗聚类.(10x就是先做PCA,再用kmeans聚类的) 鉴于单细胞的教程很多,也有不下于10种针对单细胞的聚类方法了. 降维往往是和聚类在一起的,所以似乎有点难以区分. PCA到底是降维.聚类还是可视化的方法,t-SNE呢? 其实稍微思考一下,PCA.t-SNE还有下面的diffusion

Quantitative single-cell rna-seq with unique molecular identifers 这篇文章论证了 scRNA-seq 使用UMI来计算基因表达量的合理性和优势. 这里主要研究如何分析 scRNA-seq 的数据,如何处理ERCC和UMI. 背景: however, losses in cdna synthesis and bias in cdna amplifcation lead to severe quantitative errors. 单

Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells 本文作者的官网:Ventral midbrain 顺便找到了:Download all the data and Python Notebooks from GitHub to reproduce the main figures. GitHub:linnarsson-lab/ipynb-lamanno2016 教程:scRNA-Seq Data

单细胞RNA测序技术之入门指南 [字体: 大 中 小 ] 时间:2018年09月12日 来源:生物通   编辑推荐: 在这个飞速发展的测序时代,DNA和RNA测序已经逐渐成为“实验室中的家常菜”.若要评选出目前最受欢迎的一道菜,那恐怕非单细胞RNA测序莫属. 在这个飞速发展的测序时代,DNA和RNA测序已经逐渐成为“实验室中的家常菜”.若要评选出目前最受欢迎的一道菜,那恐怕非单细胞RNA测序莫属. 以往,研究人员通常利用RNA测序(RNA-seq)来检测样本中的所有RNA转录本,以发现新型RNA

单细胞测序技术(single cell sequencing) 2018-03-02 11:02   来源: 一呼百诺  点击次数:6587关键词:   前言 单细胞生物学最近几年是非常热门的研究方向.在这一领域中,最前沿的则是单细胞测序技术.传统测序方法一次处理成千上万个细胞,得到的变异水平也是成千上万个细胞的平均后水平.但是,就如同世界上没有完全相同的两片树叶一样,没有两个细胞是完全相同的.所以,单细胞测序对于研究单个细胞就显得至关重要. 单细胞测序可以揭示出每个细胞独特的微妙变化,甚至可以

业界良心啊,开源的单细胞课程. 随便看了几章,课程写得非常用心,非常适合新手. 课程地址:Analysis of single cell RNA-seq data 源码地址:hemberg-lab/scRNA.seq.course

本文总结自一篇综述: Computational approaches for interpreting scRNA-seq data 单细胞分析分为两个层次: cell level gene level Tools for the visualization and clustering of cells. Tools for the ordering of cells & bifurcation/branch identification Tools for gene-level analy

单细胞流程跑了不少,但依旧看不懂结果,是该好好补补了. 有些人可能会误会,觉得单细胞的RNA-seq数据很好分析,跟分析常规的RNA-seq应该没什么区别.今天的这篇文章2015年3月发表在Nature Genetics Review上,专门说明了一下单细胞RNA测序数据在数据分析和计算上的挑战(虽然已经过去1年多了,这里指出的问题和挑战仍然是不过时的,至于这些问题和挑战现在是不是完美解决了,这里就暂且先不讨论了.). 主要说了以下问题: 1. 单细胞RNA测序 (single cell RNA

Cell-phone Emissions can change Brain Activity 手机辐射有可能改变大脑活动 So many people use the cell phone so frequently every day.But surprisingly little is certain about the health effects of its use.Manufacturers maintain that cell phones meet government stan

A. Mike and Cellphone 题目连接: http://www.codeforces.com/contest/689/problem/A Description While swimming at the beach, Mike has accidentally dropped his cellphone into the water. There was no worry as he bought a cheap replacement phone with an old-fas

Cellphone Typing Time Limit: 5000ms Memory Limit: 131072KB This problem will be judged on UVA. Original ID: 1252664-bit integer IO format: %lld      Java class name: Main Prev Submit Status Statistics Discuss Next Type: None   None Graph Theory 2-SAT

灵敏度高 == 假阴性率低,即漏检率低,即有病人却没有发现出来的概率低. 用于判断:有一部分人患有一种疾病,某种检验方法可以在人群中检出多少个病人来. 特异性高 == 假阳性率低,即错把健康判定为病人的概率低. 用于:被某种试验判定为患病的人中,又有多少是真的患了这种病的. 好的检测方法:有高的灵敏度(低的假阴性率).同时又有高的特异性(低的假阳性率). ROC 曲线: 横轴:100 — 特异性..即100减去特异性,特异性高,100减去特异性就低,故越小越好. 纵轴:灵敏度值. ROC分析图的

While swimming at the beach, Mike has accidentally dropped his cellphone into the water. There was no worry as he bought a cheap replacement phone with an old-fashioned keyboard. The keyboard has only ten digital equal-sized keys, located in the foll