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      <h2 class="index-header">
        
        <a href="/shi-yong-limma-bao-jin-hang-cha-yi-ji-yin-fen-xi" target="_self">
          【迁移】使用limma包进行差异基因分析
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      </h2>

      
      <a class="index-excerpt " href="/shi-yong-limma-bao-jin-hang-cha-yi-ji-yin-fen-xi" target="_self">
        <div>
          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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            <time datetime="2022-07-30 19:13" pubdate>
              2022-07-30
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              <a href="/tags/DEG/">#DEG</a>
            
              <a href="/tags/limma/">#limma</a>
            
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          <img src="https://api.limour.top/randomImg?d=2022-07-29 19:00:11" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="【迁移】oligo：GEO/ArrayExpress芯片数据处理">
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          【迁移】oligo：GEO/ArrayExpress芯片数据处理
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      <a class="index-excerpt " href="/oligo-GEO-ArrayExpress-xin-pian-shu-ju-chu-li" target="_self">
        <div>
          安装依赖 12345678910111213141516conda clean -y -allconda create -n geo -c conda-forge r-base=4.1.3conda activate geoconda install -c conda-forge r-tidyverse=1.3.1 -yconda install -c conda-forge r-irkernel
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              2022-07-29
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              <a href="/tags/oligo/">#oligo</a>
            
              <a href="/tags/GEO/">#GEO</a>
            
              <a href="/tags/ArrayExpress/">#ArrayExpress</a>
            
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          【迁移】菜鸟博主对神经网络的简单理解
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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、复杂的深度神经网络DNN等，用于将X映射到Y W：一个n
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              2022-07-28
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              <a href="/tags/llama/">#llama</a>
            
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          <img src="https://api.limour.top/randomImg?d=2022-07-27 20:10:38" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="【迁移】Counts矩阵的标准化方法：TMM和VST、RLOG">
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        <a href="/Counts-ju-zhen-de-biao-zhun-hua-fang-fa--TMM-he-VST-RLOG" target="_self">
          【迁移】Counts矩阵的标准化方法：TMM和VST、RLOG
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      <a class="index-excerpt " href="/Counts-ju-zhen-de-biao-zhun-hua-fang-fa--TMM-he-VST-RLOG" target="_self">
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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 &lt;- read.csv('GSE12
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              2022-07-27
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              <a href="/tags/TMM/">#TMM</a>
            
              <a href="/tags/VST/">#VST</a>
            
              <a href="/tags/RLOG/">#RLOG</a>
            
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