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	<title>生信菜鸟团 &#187; 笔记</title>
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		<title>kegg在线链接图的颜色设置</title>
		<link>http://www.bio-info-trainee.com/2061.html</link>
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		<pubDate>Fri, 25 Nov 2016 10:17:59 +0000</pubDate>
		<dc:creator><![CDATA[ulwvfje]]></dc:creator>
				<category><![CDATA[杂谈-随笔]]></category>
		<category><![CDATA[api]]></category>
		<category><![CDATA[KEGG]]></category>
		<category><![CDATA[笔记]]></category>
		<category><![CDATA[颜色]]></category>

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		<description><![CDATA[一般来说， 有了kegg的ID，就可以直接去官网查看具体的通路图片，但是需要把差 &#8230; <a href="http://www.bio-info-trainee.com/2061.html">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
				<content:encoded><![CDATA[<div>一般来说， 有了kegg的ID，就可以直接去官网查看具体的通路图片，但是需要把差异基因给标注上去，就有点麻烦了，我以前做过类似的工作，结果没有做笔记，这次相当于重新造了个轮子，好惨！</div>
<div>简单的KEGG图片，看下面的url：</div>
<div><a href="http://www.genome.jp/kegg-bin/show_pathway?hsa03040+hsa:10450">http://www.genome.jp/kegg-bin/show_pathway?hsa03040 </a></div>
<div><a href="http://www.genome.jp/kegg-bin/show_pathway?hsa05168+hsa:406">http://www.genome.jp/kegg-bin/show_pathway?hsa05168 </a></div>
<div>可以看出来<a href="http://www.genome.jp/kegg-bin/show_pathway?hsa03040+hsa:10450">只需要变化KEGG的ID即可。</a></div>
<div>如果要做下面的这个，上调基因用红色表示，下调基因用绿色表示：</div>
<p><span id="more-2061"></span></p>
<div><img src="file:///C:/Users/jimmy1314/AppData/Local/YNote/data/jmzeng1314@163.com/e1dfb46fed924302a0477917679dfe60/clipboard.png" alt="" data-media-type="image" data-attr-org-src-id="448475DA61C544F8BBF2D00228FE782F" /><a href="http://www.bio-info-trainee.com/wp-content/uploads/2016/11/clipboard.png"><img class="alignnone  wp-image-2065" src="http://www.bio-info-trainee.com/wp-content/uploads/2016/11/clipboard.png" alt="clipboard" width="800" height="453" /></a></div>
<div>需要重新理解kegg的API规则，比如：<a href="http://www.kegg.jp/kegg/docs/weblink.html">http://www.kegg.jp/kegg/docs/weblink.html</a></div>
<div>我就造出了下面的links：</div>
<div><a href="http://www.genome.jp/kegg-bin/show_pathway?map=hsa04115&amp;multi_query=CDKN1A+red%2Cblue%0DSESN3+red%2Cblue%0DIGFBP3+blue%2Cred%0DFAS+red%2Cblue%0DCD82+red%2Cblue%0DGADD45B+blue%2Cred%0DSERPINB5+red%2Cblue%0DZMAT3+blue%2Cred%0DCCNB3+red%2Cblue%0D">http://www.genome.jp/kegg-bin/show_pathway?map=hsa04115&amp;multi_query=CDKN1A+red%2Cblue%0DSESN3+red%2Cblue%0DIGFBP3+blue%2Cred%0DFAS+red%2Cblue%0DCD82+red%2Cblue%0DGADD45B+blue%2Cred%0DSERPINB5+red%2Cblue%0DZMAT3+blue%2Cred%0DCCNB3+red%2Cblue%0D</a></div>
<div><a href="http://www.genome.jp/kegg-bin/show_pathway?map=hsa04151&amp;multi_query=CDKN1A+red%2Cblue%0DLAMC3+red%2Cblue%0DCOL1A2+blue%2Cred%0DCREB1+blue%2Cred%0DEGF+red%2Cblue%0DFGFR3+blue%2Cred%0DGHR+red%2Cblue%0DGNG11+red%2Cblue%0DITGA2+red%2Cblue%0DITGA3+red%2Cblue%0DITGAV+red%2Cblue%0DLAMB3+red%2Cblue%0DMYC+blue%2Cred%0DPCK2+blue%2Cred%0DPPP2R5C+blue%2Cred%0DLPAR5+blue%2Cred%0DGNB4+blue%2Cred%0DSGK1+blue%2Cred%0DCREB3L2+blue%2Cred%0DTEK+red%2Cblue%0DTHBS2+blue%2Cred%0DPDGFD+blue%2Cred%0DOSMR+red%2Cblue%0D">http://www.genome.jp/kegg-bin/show_pathway?map=hsa04151&amp;multi_query=CDKN1A+red%2Cblue%0DLAMC3+red%2Cblue%0DCOL1A2+blue%2Cred%0DCREB1+blue%2Cred%0DEGF+red%2Cblue%0DFGFR3+blue%2Cred%0DGHR+red%2Cblue%0DGNG11+red%2Cblue%0DITGA2+red%2Cblue%0DITGA3+red%2Cblue%0DITGAV+red%2Cblue%0DLAMB3+red%2Cblue%0DMYC+blue%2Cred%0DPCK2+blue%2Cred%0DPPP2R5C+blue%2Cred%0DLPAR5+blue%2Cred%0DGNB4+blue%2Cred%0DSGK1+blue%2Cred%0DCREB3L2+blue%2Cred%0DTEK+red%2Cblue%0DTHBS2+blue%2Cred%0DPDGFD+blue%2Cred%0DOSMR+red%2Cblue%0D</a></div>
<div>当然，肯定不是手动的啦！！！</div>
<div>代码很简单</div>
<div>this_keggID 就是 04115 等等的KEGG的ID，数字格式的哦！</div>
<div>this_kegg_has_geneID &lt;- kegg2GeneID_list[[this_keggID]] ## 拿到这个KEGG的所有基因</div>
<div>this_kegg_has_geneSymbol &lt;- unique(geneAnno(this_kegg_has_geneID)$symbol) ## 把基因的entrez ID转换为gene 的symbol ，其中geneAnno是我自定义的！</div>
<div></div>
<div>## 跟我们的显著上下调基因取交集，其中diff_gene_list是以symbol为标记的</div>
<div>this_kegg_has_geneSymbol_diff &lt;- intersect(this_kegg_has_geneSymbol,diff_gene_list)</div>
<div>## UP GENE: DHX8+red%2Cblue%0D ##自己定义调色规则</div>
<div>## DOWN GENE:DHX8+blue%2Cred%0D</div>
<div>color = ifelse(vcx15_DEG[this_kegg_has_geneSymbol_diff,]$logFC &gt;0 ,'red%2Cblue%0D','blue%2Cred%0D')</div>
<div>## 根据交集的基因来拼接调色字符串咯</div>
<div>kegg_suffix &lt;- paste0(paste(this_kegg_has_geneSymbol_diff,color,sep='+'),collapse = '')</div>
<div>## 再把link给拼起来。</div>
<div>href=paste0("http://www.genome.jp/kegg-bin/show_pathway?map=hsa",</div>
<div>this_keggID,"&amp;multi_query=", kegg_suffix</div>
<div>)</div>
<div>整个过程做成一个循环就好啦！！</div>
<div>我开发的R包有集成这个功能：https://github.com/jmzeng1314/humanid</div>
<div>https://github.com/jmzeng1314/humanid/blob/master/R/add_kegg_up_down_link.R</div>
<div></div>
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		<title>文献笔记-2015-nature-molecular analysis of gastric cancer新的分类及预后调查</title>
		<link>http://www.bio-info-trainee.com/969.html</link>
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		<pubDate>Mon, 31 Aug 2015 10:35:05 +0000</pubDate>
		<dc:creator><![CDATA[ulwvfje]]></dc:creator>
				<category><![CDATA[cancer]]></category>
		<category><![CDATA[文献]]></category>
		<category><![CDATA[笔记]]></category>

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		<description><![CDATA[文献：Molecular analysis of gastric cancer  &#8230; <a href="http://www.bio-info-trainee.com/969.html">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
				<content:encoded><![CDATA[<p>文献：Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes</p>
<p>A small pre-defined set of gene expression signatures</p>
<table>
<tbody>
<tr>
<td width="355">epithelial-to-mesenchymal transition (EMT)</td>
<td width="283"> 上皮细胞向间充质细胞转化</td>
</tr>
<tr>
<td width="355">microsatellite instability (MSI)</td>
<td width="283">微卫星不稳定性</td>
</tr>
<tr>
<td width="355">cytokine signaling</td>
<td width="283">细胞因子信号</td>
</tr>
<tr>
<td width="355">cell proliferation</td>
<td width="283"> 细胞增殖</td>
</tr>
<tr>
<td width="355">DNA methylation</td>
<td width="283">DNA甲基化</td>
</tr>
<tr>
<td width="355">TP53 activity</td>
<td width="283">TP53活性</td>
</tr>
<tr>
<td width="355">gastric tissue</td>
<td width="283">胃组织</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<p>经典的分类方法是：Gastric cancer may be subdivided into <strong>3 distinct subtypes—proximal, diffuse, and distal gastric cancer</strong>—based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology.</p>
<p>我们用主成分分析Principal component anaylsis (PCA)</p>
<p>PC1</p>
<p>PC2</p>
<p>PC3</p>
<p>这三个主成分与上面的七个特征是相关联的。</p>
<p>根据我们的主成分分析，可以把我们的300个GC样本分成如下四组，命名如下：</p>
<p>Gene expression signatures define four molecular subtypes of GC:</p>
<p>MSI (n = 68),</p>
<p>MSS/EMT (n = 46),</p>
<p>MSS/TP53+ (n = 79)</p>
<p>MSS/TP53− (n = 107)</p>
<p>然后用本文的分类方法，测试了另外另个published数据，还是分成四个组</p>
<p>(MSI, MSS/EMT, MSS/TP53+ and MSS/TP53-)</p>
<p>分别是TCGA数据库的；n = 46, n = 62, n = 50 and n = 47.</p>
<p>Singapore的研究; n = 12, n = 85, n = 39 and n = 63 respectively</p>
<p>我们这样的分组可以得到一些规律：</p>
<p>(i) The MSS/EMT subtype occurred at a significantly younger age (P = 3e-2) than did other subtypes. The majority (&gt;80%) of the subjects in this subtype were diagnosed with diffuse-type (P &lt; 1e-4) at stage III/IV(P = 1e-3).</p>
<p>(ii) The MSI subtype occurred predominantly in the antrum (75%), &gt;60% of subjects had the intestinal subtype, and &gt;50% of subjects were diagnosed at an early stage (I/II).</p>
<p>(iii) Epstein-Barr virus (EBV) infection occurred more frequently in the MSS/TP53+ group (n = 12/18, P = 2e-4) than in the other groups.</p>
<p>&nbsp;</p>
<p>然后我们对我们的300个样本做了生存分析：</p>
<p>预后： MSI  &gt;   MSS/TP53+    &gt;   MSS/TP53 &gt;  MSS/EMT</p>
<p>Next, we validated the survival trend of GC subtypes in three independent cohorts: Samsung Medical Center cohort 2 (SMC-2,n = 277, GSE26253)31,</p>
<p>Singapore  cohort(n = 200, GSE15459)21 and</p>
<p>TCGA gastric cohort (n = 205).</p>
<p>We saw that the GC subtypes showed a significant association with overall survival</p>
<p>结论：我们这样的分类是最合理的，跟各个类别的预后非常相关。</p>
<p>&nbsp;</p>
<p>然后我们看看突变模式：</p>
<p><strong>the MSI~</strong> hypermutation ~KRAS (23.3%), the PI3K-PTEN-mTOR pathway (42%), ALK (16.3%) and ARID1A (44.2%)18.</p>
<p>We observed enrichment of PIK3CA H1047R mutations in the MSI samples</p>
<p>we saw enrichment of E542K and E545K mutations in MSS tumors</p>
<p><strong>The EMT subtype</strong> had a lower number of mutation events when compared to the other MSS groups(P = 1e−3).</p>
<p><strong>The MSS/TP53− subtype</strong> showed the highest prevalence of TP53 mutations (60%), with a low frequency of other mutations</p>
<p><strong>the MSS/TP53+ subtype</strong> showed a relatively higher prevalence (compared to MSS/TP53−) of mutations in APC, ARID1A, KRAS, PIK3CA and SMAD4.</p>
<p>再看看拷贝数变异情况：</p>
<p>再看看与另外两个研究团队的分类情况的比较</p>
<p>The TCGA study reported expression clusters (subtypes named C1–C4) and genomic subtypes (<strong>subtypes named EBV+, MSI, Genome Stable (GS) and Chromosomal Instability (CIN)).</strong></p>
<p>A follow-up study of the Singapore cohort21 described three expression subtypes <strong>(Proliferative, Metabolic and Reactive)</strong></p>
<p>However, a consensus on clinically relevant subtypes that encompasses molecular heterogeneity and that can be used in preclinical and clinical research has not been reported.</p>
<p>Here we report the molecular classification of GC linked not only to distinct patterns of genomic alterations, but also to recurrence pattern and prognosis across multiple GC cohorts.</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p><strong>microsatellite instability</strong></p>
<p>英文简称 : MI<br />
中文全称 : 微卫星不稳定性<br />
所属分类 : 生物科学<br />
词条简介 : 微卫星不稳定性（microsatellite instability,MI）检测是基于VNTR的发现，细胞内基因组含有大量的碱基重复序列，一般将6-7bp的串联重称为小卫星DNA（minisatellite DNA）,又称为VNTR。而将1-4bp的串联重复称为微卫星DNA，又称简单重复序列（simple repeat sequence,SRS）。SRS是一种最常见的重复序列之一，具有丰富的多态性、高度杂合性、重组纺低等优点。最常见的为双核苷酸重复，即（AC）n和（TG）n。研究表胆，在n≥104时，2bp重复序列在人群中呈高度多态性。SRS广泛存在于原核和真核基因组中，约占真核基因组的5％，是近年来快速发展起来的新的DNA多态性标志之一。策卫星稳定性（MI）是指简重复序列的增加或丢失。MI首先在结肠癌中观察到，1993年在HNPCC中观察到多条染色体均有（AC）n重复序列的增加或毛失，以后相继在胃癌、胰腺癌、肺癌、膀胱癌、乳腺癌、前列腺癌及其他肿瘤等也好现存在微卫星不稳定现象，提示MI可能是肿瘤细胞的另一重要分子结果显示 ，MI与肿瘤与发展有关，MI仅在肿瘤细胞中发现，从未在正常组织中检测到。在原发与移肿瘤中，MI均交分布于整个肿瘤。晚期胃癌的MI频率显著高于早期胃癌。</p>
<p>&nbsp;</p>
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