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          之前通过tcpa下载过蛋白数据，而TCGAbiolinks也有下载蛋白质组学数据的示例，后者看上去更全面一点。 下载数据123456789library(TCGAbiolinks)query.rppa &lt;- GDCquery(    project = &quot;TCGA-PRAD&quot;,     data.category = &quot;Proteome Profiling&qu
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          使用MICE包对数据缺失值进行插补
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          在分析数据集时，常常会碰到一些缺失值，如果缺失值的数量相对总体来说非常小，那么直接删除缺失值就是一种可行的方法。但某些情况下，直接删除缺失值可能会损失一些有用信息，此时就需要寻找方法来补全缺失值。（庄闪闪） 安装包 conda create -n MICE -c conda-forge r-mice -y conda activate MICE conda install -c conda-for
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          TCGAbiolinks下载甲基化数据（续）
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          之前TCGAbiolinks下载甲基化数据的hg19的数据下载好了，现在继续进行分析 安装包 conda create -n rGADEM -c conda-forge r-base&#x3D;3.6 -y conda activate rGADEM conda install -c bioconda bioconductor-motiv -y conda install -c conda-for
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          绘制富集系统聚类图（简化版）
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          之前用GOplot绘制了富集系统聚类图，感觉非常好看也非常实用，因此简化一下调用流程 12345678910f_GOplot &lt;- function(GOplotIn, genedata, filePath, Category=&#x27;enricher&#x27;, width = 12, height = 8)&#123;    names(GOplotIn) &lt;- c(&#x2
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          火山图美化
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          用DESeq2计算的结果，有时候绘图时，会发现有些差异基因的P值为0，这时候可以通过使用其他方法来计算P值，代替为0的p值，在不改变结果的前提下让火山图更好看。 读入数据123456789101112DEG &lt;- readRDS(&#x27;../../../DEG/CELL/C42vsLNCaP_EtOH.rds&#x27;)DEG &lt;- subset(DEG, !grepl(&#x
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          糖糖家的老张的教程使用记录 安装补充包 conda activate clusterprofiler BiocManager::install(“GOplot”)  计算差异基因f_DESeq2、f_dedup_IQR 1234567891011r1 &lt;- f_DESeq2(cts_b[keep,], geneInfo[keep,], Ct1, Tt1)DEG &lt;- subset(r
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          清洗数据123456789101112vsted &lt;- readRDS(&#x27;rininiang.rds&#x27;)group &lt;- readRDS(&#x27;tcga.predict.rds&#x27;)incSample &lt;- rownames(group)[group$group == &#x27;High Risk&#x27;]pwayGSE  &lt;- re
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          TCGAbiolinks下载甲基化数据
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          安装补充包 conda activate tcga # conda install -c bioconda bioconductor-sesamedata -y conda install -c conda-forge r-magick -y BiocManager::install(“sesameData”) BiocManager::install(“sesame”) BiocManager:
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          TCGAbiolinks下载CNV数据
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          下载Gene水平的数据12345678910library(TCGAbiolinks)query &lt;- GDCquery(    project = &quot;TCGA-PRAD&quot;,    data.category = &quot;Copy Number Variation&quot;,    data.type = &quot;Gene Level Copy Number&q
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          TCGAbiolinks下载maf数据
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          下载数据 ~&#x2F;dev&#x2F;xray&#x2F;xray -c ~&#x2F;etc&#x2F;xui2.json &amp;  123456789101112library(TCGAbiolinks)Sys.setenv(&quot;http_proxy&quot;=&quot;http://127.0.0.1:20809&quot;)Sys.setenv(&quot;https_
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