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    <meta name="description" content="来源：**BatchBench**和生信技能树jimmy 批次整合效果  左图是批次上色，右图是细胞类型上色 左图不同区域颜色越混杂越好 右图不同区域颜色越纯粹越好，且同一颜色最好在且只在一个区域  资源消耗量  实线是批次的熵，越高表示混合程度越好 虚线是细胞类型的熵，越低表示区分度越好 第一行是批次数量固定为2，细胞数量增长(总计61,000进行下采样) 第二行是每个批次固定1001个细胞，批">
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            <h1 id="seo-header">单细胞数据去批次效应算法比较</h1>
            
            
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                <p>来源：**<a target="_blank" rel="noopener" href="https://doi.org/10.1093/nar/gkab004">BatchBench</a>**和<a target="_blank" rel="noopener" href="https://cloud.tencent.com/developer/article/1638879">生信技能树jimmy</a></p>
<h2 id="批次整合效果"><a href="#批次整合效果" class="headerlink" title="批次整合效果"></a>批次整合效果</h2><p><img src="https://img-cdn.limour.top/2022/08/13/62f67e2f6b109.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></p>
<ul>
<li>左图是批次上色，右图是细胞类型上色</li>
<li>左图不同区域颜色越混杂越好</li>
<li>右图不同区域颜色越纯粹越好，且同一颜色最好在且只在一个区域</li>
</ul>
<h2 id="资源消耗量"><a href="#资源消耗量" class="headerlink" title="资源消耗量"></a>资源消耗量</h2><p><img src="https://img-cdn.limour.top/2022/08/13/62f68229451e4.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></p>
<ul>
<li>实线是批次的熵，越高表示混合程度越好</li>
<li>虚线是细胞类型的熵，越低表示区分度越好</li>
<li>第一行是批次数量固定为2，细胞数量增长(总计61,000进行下采样)</li>
<li>第二行是每个批次固定1001个细胞，批次数量增长</li>
<li>第二列是计算消耗的时间，不清楚作者的计算节点性能，但应该很强</li>
<li>第三列是计算占用的内存，可以看到Seurat在2个批次6万个细胞时占用了高于30G的内存；而20个批次，2万个细胞时占用了60G内存</li>
</ul>
<h2 id="校正批次后对聚类的影响"><a href="#校正批次后对聚类的影响" class="headerlink" title="校正批次后对聚类的影响"></a>校正批次后对聚类的影响</h2><p><img src="https://img-cdn.limour.top/2022/08/13/62f6854896d38.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></p>
<ul>
<li>上面的col是组织，下面的col是数据来源，左边的row是批次去除算法，右边的row是无监督聚类算法</li>
<li>颜色表示调整兰德指数的值，越接近1越好，说明该组织越适合这种批次去除算法和无监督聚类算法</li>
</ul>
<h2 id="校正批次后对Marker基因的影响"><a href="#校正批次后对Marker基因的影响" class="headerlink" title="校正批次后对Marker基因的影响"></a>校正批次后对Marker基因的影响</h2><p><img src="https://img-cdn.limour.top/2022/08/13/62f6895dad503.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></p>
<ul>
<li>红色表示DEG中检测到的标记基因数量占实际标记基因数量的比例</li>
<li>深青色表示DEG与实际标记基因集合的相似性</li>
<li>可以看出效果一塌糊涂，还是<strong>先分群再合并比较好，这些校正批次效应的算法只能用于可视化</strong>。</li>
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