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1. GWAS可视化学习原文链接：gwaslab/examples/7_visualization/visualization_regional.ipynb at main · Cloufield/gwaslab
# 创建新环境# conda env create -n gwaslab_test -c conda-forge python=3.9# pip install gwaslab# pip install wget
import wgetimport inspectimport sysimport gwaslab as gl########## ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2025/01/03/Genomics/" title="Genomic">Genomic</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2025-01-03T07:55:58.000Z" title="发表于 2025-01-03 15:55:58">2025-01-03</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2025-03-17T02:56:41.810Z" title="更新于 2025-03-17 10:56:41">2025-03-17</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/Study-Notes/">Study Notes</a></span></div><div class="content">1. Data Type1.1 RNA-seq数据Gene Expression Quantification用于测量基因组中所有基因的表达水平；
STAR-Counts表示工作流程：Spliced Transcripts Alignment to a Reference是一个高效的对比工具，能够处理剪接位点，实现转录本到基因组的精确比对。能够统计比对到每个基因区域的读段数量，用于后续表达量分析。
1.2 miRNA-seq数据Isoform Expression Quantification是异构体表达定量。数据类型分为Isoform Expression Quantification和miRNA Expression Quantification，其中Isoform Expression Quantification包含5p和3p的表达量数据，能够区分同一个前体miRNA产生的不同成熟体，提供更详细的miRNA异构体信息；miRNA Expression Quantification仅提供stem-loop水平的定量数据，不区分来自同一前体的5p和3p，反映的是整体miRNA前体的 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/10/14/Basic%20statistic%20method/" title="Basic Statistic Method">Basic Statistic Method</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-10-14T06:52:21.000Z" title="发表于 2024-10-14 14:52:21">2024-10-14</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2025-03-12T07:06:52.357Z" title="更新于 2025-03-12 15:06:52">2025-03-12</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/StudyNotes/">StudyNotes</a></span></div><div class="content">0 Preface在此记录下遇见的比较常用的或者比较有意思的统计方法。
1 数据处理方法1 缺失值处理-MICE填补法MICE(Multiple Imputation by Chained Equations)，数据集中指定的变量的缺失值使用其他变量作为预测变量进行估算。没有相关性的完全独立变量的数据集不会产生准确的插补。其基本步骤是：
初始插补：对每个缺失值进行初始插补（通常使用均值、回归或随机采样）。
迭代插补：逐变量插补：将缺失的变量视为被预测的目标变量，对于每个缺失的变量基于现有数据构建回归模型预测缺失值；
循环迭代：重复步骤（一般为5到10次），逐步更新插补结果，直到收敛（插补值不再发生显著变化）。
MICE可以使用线性回归、逻辑回归、预测均值匹配（PMM）的程序来选择要估算的值。
PMM（预测均值匹配Predictive Mean Matching），核心思想是通过匹配（注意，是匹配）预测值来插补缺失数据。首先构建回归模型，对缺失值通过回归模型计算其预测值，在原本无缺失值（观测数据中的真实值）的数据中找到与预测值最接近的值，这意味着插补的缺失值直接来源于已有数据中的观测 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/09/20/Medical%20Basic%20Knowledge/" title="Medical Basic Knowledge (Main in Epidemiological)">Medical Basic Knowledge (Main in Epidemiological)</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-09-20T06:00:21.000Z" title="发表于 2024-09-20 14:00:21">2024-09-20</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2025-01-07T07:28:26.234Z" title="更新于 2025-01-07 15:28:26">2025-01-07</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/StudyNotes/">StudyNotes</a></span></div><div class="content">Medical Basic Knowledge (Main in Epidemiological)0 Preface笔者最近开始正式着手慢性病、蛋白质组学分析的工作，经过反馈才发现自己对医学知识的欠缺十分严重，对流程和原因的了解和把控几乎为零，在此记录下基础的医学知识（主要为流行病学）。
蛋白质组学分析流程相关问题现有流程记录（非代表性）对已有队列进行数据质控、清洗得到干净数据，此时的干净数据可以称为目标研究人群，再从干净数据中挑选出病例组以及对照组。
健康对照组：排除基线诊断时患重大疾病（CHD、中风、ECG异常）、无定点医保的人群，排除近期核查患重大疾病人群。以此得到健康对照组的子队列抽样源，再根据性别、年龄进行分层抽样，得到较小的子队列样本，再进行排除，排除无血浆样本、基因型未测或者不合格、甲基化未测或不合格样本；再排除随访期内出现重大疾病或者死亡的人群；排除子队列内部存在亲缘关系，以及与对照组存在亲缘关系的样本。
病例组：挑选随访截止日期前的新发病例，排除诊断时间不明确、排除无血浆样本、排除GWAS和甲基化未测或不合格样本；排除发病距随访时间过长（3年）的样本；排除特定检测点 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/08/26/proteomics/" title="Proteomics">Proteomics</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-08-26T03:48:19.000Z" title="发表于 2024-08-26 11:48:19">2024-08-26</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2025-01-15T07:34:25.555Z" title="更新于 2025-01-15 15:34:25">2025-01-15</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/StudyNotes/">StudyNotes</a></span></div><div class="content">Proteomics0 Preface蛋白质组学分析基础概念知识。
蛋白质组学研究蛋白质结构、功能及其在生物系统中表达。分析过程中通常需要处理大量复杂的数据，包括蛋白质的鉴定、定量、修饰、相互作用等。
一般流程包括：
数据获取与预处理
蛋白质鉴定：利用质谱（Mass Spectrometry，MS）技术识别样本中的蛋白质。
原始数据处理：将质谱数据转化为可识别的肽段和蛋白质信息。
数据过滤：移除低置信度的蛋白质或肽段。
数据定量分析
标签定量：使用标记技术对蛋白质进行相对或绝对定量。
无标签定量：通过光谱计数或基于强度的方法对蛋白质定量。
数据标准化和归一化
标准化：消除批次效应或实验条件的差异，常用Z-score标准化、分位数归一化。
归一化：调整蛋白质定量数据，使不同样本之间具有可比性。
差异表达分析
统计分析：使用t检验、ANOVA等识别不同条件下显著变化的蛋白质。
多重比较校正：校正多重比较引起的假阳性率。
功能注释与通路分析
功能注释：将蛋白质映射到功能数据库理解生物学功能。
通路分析：利用软件识别与特定生物学通路相关的差异表达蛋白。
结果验证与解释
验证实验：使用 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/08/24/Datasets/" title="Bioinformatics Datasets">Bioinformatics Datasets</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-08-24T08:54:54.000Z" title="发表于 2024-08-24 16:54:54">2024-08-24</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2025-02-10T07:43:34.604Z" title="更新于 2025-02-10 15:43:34">2025-02-10</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/StudyNotes/">StudyNotes</a></span></div><div class="content">Bioinformatics Datasets Description0 Preface笔者收集整理笔者本人遇到、主流的、必须的生物信息数据集，防止遗忘。
1 1000G千人基因组计划于2008年启动，其核心的研究方法是：全基因组测序、低覆盖测序与高覆盖外显子测序结合；变异的鉴定与注释。其主要成果在于揭示了人类基因组的遗传多样性、跨人群的遗传变异频率分布、结构变异的重要性、其本身是复杂疾病研究的基础数据。
2 UK BiobankUK Biobank旨在收集和分析50万英国成年人的健康数据、生活方式信息、基因组数据和临床记录，其特点是一个长时间随访的队列研究。
UK Biobank于2006年启动，汇集了40到69岁的志愿者，共计约500000人。
其数据包括：
基因组数据：全基因组数据，UK Biobank对约50万名志愿者的DNA样本进行了基因分型，并且其中15万人提供了全基因组测序数据，进行了超过90000人的外显子组测序。单核苷酸多态性（SNP）：基因分型数据提供了每个参与者数百万个SNP位点的信息。
健康数据：电子健康记录：项目收集了参与者的医疗记录；体检数据；影像数据。 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/08/22/Reading-Notes-Mathematical-Population-Genetics-by-Warren-J-Ewens/" title="Reading Notes-<Mathematical Population Genetics> by Warren J.Ewens">Reading Notes-<Mathematical Population Genetics> by Warren J.Ewens</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-08-22T06:13:03.000Z" title="发表于 2024-08-22 14:13:03">2024-08-22</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-08-22T08:55:26.561Z" title="更新于 2024-08-22 16:55:26">2024-08-22</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/ReadingNotes/">ReadingNotes</a></span></div><div class="content">0 Preface笔者从统计学习转入生物信息学习中，种群遗传学（Population Genetics）是笔者最开始感兴趣的一个大方向。为此写下读书笔记，以供及时翻阅，以防遗忘。
种群遗传学包含观察（observation）、实验（experiment）和理论（theory）三个部分，本书着重于结合数学的理论部分。
1 Historical Background1.1 Biometricians, Saltationists and Mendelians种群遗传学理论最初由费舍尔Fisher、霍尔丹Haldane和怀特Wright提出，其目的是在孟德尔遗传机制的基础上建立一个进化框架。人们对进化变化的方式存在分歧，达尔文Darwin认为进化是渐近连续（gradual and incremental）的，而另一些人认为进化是跳跃的。从这两个观点出发形成了两种进化学派。
对于达尔文的自然选择进化论，高尔顿几乎通过纯粹的理论推理得到相似的结论。与达尔文进化理论相反的是混合假设（blending hypothesis：后代的特征是父母特征的混合，所以在随机交配的情况下，种群中任何特征的变异性 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/08/22/Study-Notes-Genomics/" title="Study Notes-Genomics">Study Notes-Genomics</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-08-22T06:12:32.000Z" title="发表于 2024-08-22 14:12:32">2024-08-22</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-08-22T06:50:24.770Z" title="更新于 2024-08-22 14:50:24">2024-08-22</time></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/StudyNotes/">StudyNotes</a></span></div><div class="content">0 Preface基因组学包括转录组学（Transcriptomics）、蛋白质组学（Proteomics）、表观基因组学（Epigenomics）、宏基因组学（Metagenomics）、群体基因组学（Population Genomics）、进化基因组学（Evolutionary Genomics）、单细胞基因组学（Single-cell Genomics）等等，基因组学的研究在由生物、医学、健康三大方向构成的框架下开展研究。
1 基因组学基本概念基本概念：基因组（Genome）、染色体（Chromosome）、基因（Gene）、密码子（Codon）、碱基（Base）。
基因（Gene）：产生一条编码蛋白或RNA产物的核苷酸序列。
基因组（Genome）：GENe+chromosOME，生物体所有遗传物质的综合。包括核基因组、线粒体基因组、叶绿体基因组。
基因组学（Genomics）：以生物体全部基因为研究对象，研究基因组结构、功能、演化、组装、编辑等方面的交叉学科。包括拼接组装生物体全部基因组序列（测序）、系统解析所有基因功能（注释）、明确基因之间的相互作用关系（调控关系）、揭示 ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/loading.gif" data-original="/img/Kakasi.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">远山</div><div class="author-info__description">大鹏一日同风起，扶摇直上九万里</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">8</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">4</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">0</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/BenjaminMount"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/BenjaminMount/" target="_blank" title="Github"><i class="fab fa-github"></i></a><a class="social-icon" href="tencent://AddContact/?fromId=45&fromSubId=1&subcmd=all&uin=2396979300&website=www.oicqzone.com" target="_blank" title="QQ"><i class="fab fa-qq"></i></a><a class="social-icon" href="mailto:liutongyan@hust.edu.cn" target="_blank" title="Email"><i class="fas fa-envelope-open-text"></i></a></div></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">Keep thinking. 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