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- 数据清洗123library(WGCNA) #加载WGCNA包enableWGCNAThreads() #开启多线程options(stringsAsFactors = FALSE) #避免某些错误匹配 1234567#Read in the female liver data setfemData = read.csv("LiverFemale3600.csv");#
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- 官网下载zipped data sets和Male data,unzip解压,基本概念见此。 数据输入与清洗123456789library(WGCNA) #加载WGCNA包enableWGCNAThreads() #开启多线程femData = read.csv("LiverFemale3600.csv") #载入基因表达量数据femData datExpr0 = as.
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- GSEA分析123456789101112131415161718192021222324252627282930313233343536require(clusterProfiler)require(enrichplot)rres <- readRDS('DEGs_X1.OE.DMSO_X2.OE.DMSO_vs._X1.control.DMSO_X2.control.DMSO_
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- 通过比对,我们得到了counts矩阵,接下来可以进行DEGs分析。此时如果我们有多组之间的对比,则可以使用RRA算法来聚合我们的结果。RRA的安装过程见此。 第一步,多组差异基因分析1234567891011121314151617181920212223242526272829303132333435library(DESeq2)count_all <- read.csv("~/
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- WikiPathways是一个开放协作平台,旨在促进生物学界对通路信息的贡献和维护。它提供了一种新的模型,可以增强和补充KEGG、Reactome和Pathway Commons等正在进行的工作。 安装相应的R包 conda activate clusterprofiler conda install -c bioconda bioconductor-rwikipathways -y 初步建立数
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- clusterProfiler中提供了enricher和GSEA两个函数,enricher包装了**Over-representation analysis**,GSEA包装了Gene Set Enrichment Analysis。这两个函数与其他的enrichKEGG、gseKEGG的唯一不同点是提供的不是KEGG数据库,而是TERM2GENE和TERM2NAME两个参数。 从gson格式初探
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- 更新包 conda activate clusterprofile ~/dev/xray/xray -c ~/etc/xui2.json & wget -e “https_proxy=http://127.0.0.1:20809“ https://github.com/YuLab-SMU/clusterProfiler/archi
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