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- 虽然现在已经是高通量测序的时代,大家基本都是从counts矩阵出发,使用DESeq2进行差异表达分析,但是GEO和ArrayExpress上的仍有海量且持续更新的芯片数据,有时候也不可避免遇到一些FPKM格式乃至已经进行了z-score转换的数据,对于这些数据的分析,我们可以认为其在适当变换下(log2FPKM),满足正态分布,那么仍可以使用limma直接进行分析。下面博主以E-MEXP-1422
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- conda配置环境 conda clean -y -all conda create -n geo -c conda-forge r-base=4.1.3 conda activate geo conda install -c conda-forge r-tidyverse=1.3.1 -y conda install -c conda-forge r-irkernel -y
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- 博主最近在看临床预测模型,里面涉及到一些神经网络的相关知识,这里记录一下博主的简单理解。 基本概念 X:一个nx维的输入张量,可以是nx=0的标量,nx=1的向量,nx=2的矩阵,或更高维的张量 Y:一个ny维的输出张量,可以是nx=0的标量,nx=1的向量,nx=2的矩阵,或更高维的张量 f:一个特定结构的神经网络,如简单的BP神经网络
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- TMM:The Trimmed Mean of M value by edgeR VST:The variance stabilizing transformation by DESeq2 RLOG:The regularized-logarithm transformation by DESeq2 Counts矩阵来源于STAR匹配得到的结果:df <- read.csv('G
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- 使用 rownames(df) <- rowNn 时经常会遇到rowNn中有重复值的情况,此时需要使用合适的策略来选择需要保留的那一列。下面这个函数默认保留IQR值(四分位距)最大的那一列。通过传入不同的select_func参数值,也可以改用其他的保留选择策略。如 mean 来保留算数平均值最大的一列,也可以传入自己定义的函数。 来源:Comprehensive Evaluation of
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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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- 组织/细胞的功能执行具有模块化的特点。权重基因共表达网络分析(Weighted gene co-expression network analysis,WGCNA)使用Pearson相关系数或bicor双权重中位相关系数来衡基因之间的共表达关系,将表达模式相似的基因聚类成模块。同一模块的基因可能参与同一生物学过程或通路,被称为功能模块。WGCNA通过分析功能模块与特定性状或表型之间的关联
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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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