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          安装包  conda activate clusterprofiler conda install -c conda-forge r-sysfonts -y conda install -c conda-forge r-showtext -y  绘图 1234567sysfonts::font_add(&quot;Arial Narrow&quot;, &quot;~/font/Arial Nar
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          【迁移】使用MICE包对数据缺失值进行插补
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      <a class="index-excerpt " href="/shi-yong-MICE-bao-dui-shu-ju-que-shi-zhi-jin-hang-cha-bu" target="_self">
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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。 安装包 12conda create -n mice conda-forge::r-tidyverse conda-forge::r-irkernel conda-forge::r-mice conda
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              2022-09-10
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              <a href="/tags/mice/">#MICE</a>
            
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          <img src="https://api.limour.top/randomImg?d=2022-07-30 19:13:36" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="【迁移】使用limma包进行差异基因分析">
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          【迁移】使用limma包进行差异基因分析
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      <a class="index-excerpt " href="/shi-yong-limma-bao-jin-hang-cha-yi-ji-yin-fen-xi" target="_self">
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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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              2022-07-30
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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">
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          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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        <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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          <img src="https://api.limour.top/randomImg?d=2022-07-22 18:46:32" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="【迁移】从差异基因到RRA聚合">
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    <article class="col-12 col-md-8 mx-auto index-info">
      <h2 class="index-header">
        
        <a href="/cong-cha-yi-ji-yin-dao-RRA-ju-he" target="_self">
          【迁移】从差异基因到RRA聚合
        </a>
      </h2>

      
      <a class="index-excerpt " href="/cong-cha-yi-ji-yin-dao-RRA-ju-he" target="_self">
        <div>
          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 1234567891011121314151617181920conda create -n tcga -c conda-forge r-base=4.1.2 -yconda activate tcgaconda install -c conda-f
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            <time datetime="2022-07-22 18:46" pubdate>
              2022-07-22
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              <a href="/tags/degs/">#DEGs</a>
            
              <a href="/tags/rra/">#RRA</a>
            
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          <img src="https://api.limour.top/randomImg?d=2022-07-15 20:42:25" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="【迁移】森林图展示回归分析的结果">
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        <a href="/Forest-plot-displays-the-results-of-regression-analysis" target="_self">
          【迁移】森林图展示回归分析的结果
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      <a class="index-excerpt " href="/Forest-plot-displays-the-results-of-regression-analysis" target="_self">
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          配置环境  基础编程环境 GitHub 下载加速 可能需要用到的加速服务  1234conda create -n forestplot -c conda-forge r-forestplot -yconda activate forestplotconda install -c conda-forge r-tidyverse r-irkernel -yRscript -e &quot;IRker
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            <time datetime="2022-07-15 20:42" pubdate>
              2022-07-15
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              <a href="/tags/forestplot/">#forestplot</a>
            
              <a href="/tags/regression/">#regression</a>
            
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          Rclone使用自建应用挂上了Ondrive，接下来就该配置aria2+自动上传脚本了。 安装aira2 12345678910111213141516171819202122232425262728293031323334version: &quot;3.8&quot;services:  Aria2-Pro:    container_name: aria2-pro    image: p3t
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              2022-07-13
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              <a href="/tags/raspberrypi/">#raspberrypi</a>
            
              <a href="/tags/rclone/">#rclone</a>
            
              <a href="/tags/aria2/">#aria2</a>
            
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          <img src="https://api.limour.top/randomImg?d=2022-07-05 17:28:03" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="【迁移】时空图：以天狼星单程旅行为例">
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          【迁移】时空图：以天狼星单程旅行为例
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      </h2>

      
      <a class="index-excerpt " href="/-qian-yi--shi-kong-tu--yi-tian-lang-xing-dan-cheng-l-xing-wei-li" target="_self">
        <div>
          最近看《从一到无穷大》，对里面的闵氏时空图很感兴趣，恰好里面有去天狼星的例子，因此想用时空图画画看。为简化问题，这里取天狼星到地球的距离为9光年，实际约为8.6光年。同时为了便于画图，纵轴时间轴以年×光速为单位，横轴空间轴以光年为单位。使用 geogebra 进行绘图。 出发前的时空图  如图1所示，线段g（CAD）表示飞船，A点为质心。直线f表示天狼星B的世界线。射线h（AE）表示A的光锥。射线
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              2022-07-05
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              <a href="/tags/%E6%97%B6%E7%A9%BA%E5%9B%BE/">#时空图</a>
            
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