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phreeqc中文使用手册【最新版】目录1.Phreeqc 简介2.安装与配置 Phreeqc3.Phreeqc 的基本语法4.Phreeqc 的应用示例5.总结正文【1.Phreeqc 简介】Phreeqc 是一个用于处理化学问题的开源软件包，特别是在处理矿物溶解度、岩石水化学和环境地球化学方面的问题非常有效。
它基于 Python 编程语言，通过大量的函数和方法，为用户提供了一个强大的工具来解决各种化学问题。
【2.安装与配置 Phreeqc】在使用 Phreeqc 之前，你需要先安装 Python，并使用 pip 安装Phreeqc。
安装过程非常简单，只需要在终端中输入以下命令：```pip install phreeqc```安装完成后，你可以通过编写 Python 脚本来使用 Phreeqc。
需要注意的是，Phreeqc 对于 Python 的版本有一定的要求，如果你使用的Python 版本过低，可能需要升级到新版本才能正常使用 Phreeqc。
【3.Phreeqc 的基本语法】Phreeqc 的基本语法非常简单，主要包括以下几个部分：- 导入模块：在使用 Phreeqc 之前，需要先导入 phreeqc 模块。
- 创建对象：Phreeqc 中的许多功能都需要创建对象，例如溶液对象、离子对象等。
- 调用方法：通过对象调用相应的方法，可以实现各种化学计算。
下面是一个简单的 Phreeqc 使用示例：```pythonimport phreeqc# 创建一个溶液对象solution = phreeqc.Solution("example.iqc")# 添加物质到溶液中solution.add("CaCO3", 1.0)solution.add("H2O", 1000.0)# 计算溶液中的离子浓度solution.calculate_ions()# 输出结果solution.print_ions()```【4.Phreeqc 的应用示例】下面是一个使用 Phreeqc 计算矿物溶解度的示例：```pythonimport phreeqc# 创建一个溶液对象solution = phreeqc.Solution("example.iqc")# 添加物质到溶液中solution.add("CaCO3", 1.0)solution.add("H2O", 1000.0)# 设置溶液的温度和压力solution.set_temperature(25)solution.set_pressure(101325)# 计算矿物的溶解度solution.calculate_dissolution("CaCO3")# 输出结果solution.print_dissolution()```【5.总结】Phreeqc 是一个功能强大的化学计算软件包，可以帮助用户解决各种化学问题。
phreeqc中文使用手册【实用版】目录1.Phreeqc 简介2.Phreeqc 的功能3.Phreeqc 的使用方法4.Phreeqc 的示例5.Phreeqc 的安装与卸载6.Phreeqc 的常见问题与解答正文【Phreeqc 简介】Phreeqc 是一款中文的文字处理工具，主要用于中文分词、词性标注、命名实体识别等自然语言处理任务。
它支持多种中文分词算法，可以对中文文本进行精确的分词处理，同时也可以对分词后的词语进行词性标注和命名实体识别。
【Phreeqc 的功能】Phreeqc 主要有以下功能：1.中文分词：支持多种中文分词算法，可以对中文文本进行精确的分词处理。
2.词性标注：对分词后的词语进行词性标注，包括名词、动词、形容词等。
3.命名实体识别：对文本中的命名实体进行识别，包括人名、地名、组织名等。
4.依存句法分析：对句子进行依存句法分析，展示句子的句法结构。
【Phreeqc 的使用方法】Phreeqc 的使用方法非常简单，只需要在命令行中输入“phreeqc”命令，后面跟上需要处理的文本文件路径即可。
例如：“phreeqcinput.txt”。
【Phreeqc 的示例】我们以一个简单的例子来说明 Phreeqc 的使用。
假设我们有一段中文文本：“小明是一个学生，他喜欢打篮球。
”，我们在命令行中输入“phreeqc example.txt”，Phreeqc 将会对这段文本进行分词、词性标注和命名实体识别，输出结果如下：```小明/np 是/v 一个/dt 学生/nn，/p 他/pr 喜欢/v 打/v 篮球/nn。
/p```【Phreeqc 的安装与卸载】Phreeqc 的安装非常简单，只需要下载最新版本的 Phreeqc 软件包，然后解压缩即可。
解压缩后的文件夹中，有一个名为“phreeqc”的可执行文件，这就是 Phreeqc 的主程序。
卸载 Phreeqc 也非常简单，只需要删除解压缩后的文件夹即可。
phreeqc中文使用手册摘要：1.Phreeqc 简介2.安装与配置3.使用方法4.常见问题5.总结正文：1.Phreeqc 简介Phreeqc 是一个用于解决化学问题的计算机程序，特别是在矿物溶解度、离子交换、吸附和沉淀等方面具有很高的应用价值。
该程序采用离子强度罗兰兹法，可以模拟各种复杂的水文地球化学过程。
Phreeqc 具有丰富的功能和灵活性，适用于科研、教学和工程应用等多种场景。
2.安装与配置在使用Phreeqc 之前，首先需要进行程序的安装。
根据用户的操作系统，可以选择相应的安装包进行安装。
安装完成后，需要对Phreeqc 进行配置，包括设置计算参数、定义输入输出文件格式等。
具体配置方法可以参考Phreeqc 的用户手册或在线教程。
3.使用方法Phreeqc 的使用方法主要包括以下几个步骤：（1）准备输入文件：根据需要解决的问题，编写相应的输入文件。
输入文件主要包括化学物质的化学式、初始浓度、溶液的物理化学性质等。
（2）运行Phreeqc：在命令行界面，输入Phreeqc 命令并指定输入文件，启动程序进行计算。
（3）查看结果：Phreeqc 计算完成后，会生成相应的输出文件。
用户可以根据输出文件查看计算结果，包括溶液中各离子的浓度、pH 值、溶解度积等。
4.常见问题在使用Phreeqc 过程中，可能会遇到一些常见问题，如输入文件格式错误、计算结果不符合预期等。
针对这些问题，可以通过以下方法进行解决：（1）检查输入文件：确保输入文件格式正确，化学物质的化学式和初始浓度正确无误。
（2）调整计算参数：根据问题的实际情况，调整Phreeqc 的计算参数，如计算方法、离子强度等。
（3）参考教程和手册：查阅Phreeqc 的用户手册或在线教程，了解程序的使用方法和注意事项。
5.总结Phreeqc 是一个功能强大的化学计算程序，广泛应用于水文地球化学、环境科学等领域。
phreeqc中文使用手册摘要：1.引言2.phreeqc 软件的概述3.phreeqc 软件的安装与配置4.phreeqc 软件的使用方法5.phreeqc 软件的应用领域6.结论正文：【引言】phreeqc 是一款用于水文地球化学计算的软件，广泛应用于矿床学、环境科学、地质学等领域。
本文将详细介绍phreeqc 软件的使用方法及其应用领域。
【phreeqc 软件的概述】phreeqc（PHReactor EQuilibrium Code）是一款基于PHREEQC （1995）模型开发的水文地球化学计算软件。
该软件可以通过输入地质体、水溶液的化学组分及有关参数，计算水文地球化学反应系列及平衡条件。
【phreeqc 软件的安装与配置】1.下载并解压phreeqc 软件；2.安装Java 运行环境（JRE），以支持phreeqc 软件的运行；3.配置环境变量，使phreeqc 软件可以在命令行中运行。
【phreeqc 软件的使用方法】1.准备输入文件，包括地质体、水溶液的化学组分及有关参数；2.使用命令行运行phreeqc 软件，输入文件路径；3.查看输出文件，分析计算结果。
【phreeqc 软件的应用领域】1.矿床学：研究矿床形成的地球化学条件、矿床的成因及矿床的勘探；2.环境科学：评估地下水、河流等地表水体的污染程度及生态环境影响；3.地质学：分析地质体的水文地球化学条件，推断地质过程。
【结论】phreeqc 软件是一款功能强大的水文地球化学计算工具，广泛应用于矿床学、环境科学、地质学等领域。
通过本文的介绍，相信大家对phreeqc 软件的使用方法及其应用领域有了更深入的了解。
phreeqc中文使用手册【原创实用版】目录1.phreeqc 简介2.安装与配置 phreeqc3.phreeqc 的基本操作4.phreeqc 的高级功能5.常见问题与解决方案6.总结正文【1.phreeqc 简介】phreeqc 是一款用于计算化学的免费开源软件，全称为“PHase Equilibria Calculation”。
它主要用于研究气相、液相和固相之间的平衡关系，适用于化学工程、石油化工、材料科学等领域。
phreeqc 具有计算速度快、结果准确等优点，广泛应用于工业和学术研究。
【2.安装与配置 phreeqc】phreeqc 支持多种操作系统，如 Windows、Linux 和 Mac OS。
安装过程相对简单，只需按照官方提供的安装指南进行操作即可。
安装完成后，需要配置一些环境变量和路径，以便正常运行 phreeqc。
具体配置方法可参考官方文档或相关教程。
【3.phreeqc 的基本操作】phreeqc 的基本操作主要包括创建项目、添加物质、定义相、设置边界条件、运行计算等。
用户可以通过图形界面或命令行方式进行操作。
对于初学者，建议先熟悉 phreeqc 的基本概念和操作，再进行实际应用。
【4.phreeqc 的高级功能】phreeqc 还提供了许多高级功能，如物质数据库管理、热力学性质计算、模拟优化等。
用户可以根据自己的需求选择使用。
此外，phreeqc 还支持脚本编写和自定义函数，便于用户进行个性化定制。
【5.常见问题与解决方案】在使用 phreeqc 过程中，可能会遇到一些问题，如计算结果不准确、软件崩溃等。
针对这些问题，可以通过检查输入数据、更新软件版本、调整计算参数等方法进行解决。
如果问题仍无法解决，可以寻求官方论坛或社区的帮助。
【6.总结】phreeqc 是一款功能强大的计算化学软件，适用于各种平衡计算任务。
通过掌握其基本操作和高级功能，用户可以更好地解决实际问题。
摘要PHREEQC第二版是一个用C语言编写的计算机程序，对各种各样低温下的地球化学性质进行了演算。
PHREEQC是以离子联系的水化学模型为基础的，可以推算（1）生成物和饱和系数；（2）涉及到可逆反应以及不可逆反应的批反应和一维（1D）运移计算，可逆反应包括水、矿物/无机溶液、气体、固体溶液、表面络合、离子交换平衡；涉及到的不可逆反应包括指定成分摩尔转换、动态控制反应、溶液混合和温度变化；（3）逆向模拟实验，其中多组的无机物和气体摩尔转换以解释在特定成分不确定范围水体之间混合物的不同。
和第一版相比，PHREEQC 第二版的新特点如下：具有在一维运移计算中模拟弥散（或扩散）和滞流区的能力，用用户确定的速率表达式模拟分子反应，模拟标准的多种成分或非标准的两种成分的固体溶液的沉淀和溶解，模拟定体积气相和定压力气相，考虑表面系数或交换位置随着无机物的溶解和沉淀或者分子反应的变化而变化，自动采用多套收敛参数，打印用户指定量到原始输出文件和（或）适合输入到扩展表格的文件上，以一种与扩展表程序更兼容的形式确定溶解成分。
这个版本报告中说明了化学平衡、动态平衡、运移计算以及逆向模拟计算基础方程式，描述了程序输入，以及举例说明了许多程序功能目的和范围这个报告的目的是说明PHREEQC 程序的理论和操作，包括组成成分方程的确定，转换这些方程为数值计算方法的解释，补充数值方法计算机代码组织的描述，程序输入描述，以及一系列数据组输入和许多论证的程序功能的数据输入和模拟结果的说明。
生成方程和正向模拟在报告的这一部分，说明了用于确定水样热动力活动，离子交换物质、表面络合物质、气相成分、固体溶液和纯相的代数方程式。
首先，说明了水样、交换种类和表面性质的热动力活动和质量作用方程。
然后，定义一组函数，用f 表示，他们必须能同时求解以确定给定条件下的平衡，许多这样的方程是各种元素或元素的价电子状态、交换位置和表面位置的摩尔平衡方程，或来自于纯相和固体溶液的质量作用方程。
phreeqc中文使用手册【实用版】目录1.Phreeqc 简介2.安装与配置 Phreeqc3.Phreeqc 的使用方法4.Phreeqc 的功能与应用领域5.常见问题与解答正文【1.Phreeqc 简介】Phreeqc 是一款开源的中文自然语言处理工具，全称为“现代汉语语义分析工具集”。
它由哈尔滨工业大学的自然语言处理实验室研发，旨在为中文自然语言处理提供一套功能完善、性能高效的工具。
Phreeqc 集成了多种中文自然语言处理技术，如分词、词性标注、命名实体识别、依存句法分析等，广泛应用于文本分类、情感分析、信息抽取、机器翻译等领域。
【2.安装与配置 Phreeqc】Phreeqc 支持 Linux、macOS 和 Windows 操作系统。
安装过程相对简单，用户只需根据官方提供的安装指南进行操作即可。
对于 Linux 系统，可以使用包管理器如 apt、yum 等进行安装；对于 macOS 和 Windows 系统，则需要从源代码编译安装。
配置 Phreeqc 主要包括设置环境变量、数据路径、模型路径等。
环境变量用于指定 Phreeqc 的可执行文件位置；数据路径用于存储Phreeqc 的词库、规则文件等；模型路径则用于存储 Phreeqc 的模型文件。
用户可以根据自己的需求进行相应的配置。
【3.Phreeqc 的使用方法】Phreeqc 提供了丰富的 API 接口，用户可以通过命令行、Python、Java 等方式调用这些接口。
下面以命令行为例，介绍 Phreeqc 的基本使用方法：(1) 分词：使用`phreeqc_cut`命令进行分词，如`phreeqc_cut -i input.txt -o output.txt`。
(2) 词性标注：使用`phreeqc_pos`命令进行词性标注，如`phreeqc_pos -i input.txt -o output.txt`。
(3) 命名实体识别：使用`phreeqc_ner`命令进行命名实体识别，如`phreeqc_ner -i input.txt -o output.txt`。
phreeqc中文使用手册摘要：一、引言- 介绍phreeqc中文使用手册的目的和适用对象二、phreeqc软件概述- 解释phreeqc的含义和作用- 介绍phreeqc软件的发展历程和主要功能三、phreeqc中文使用手册的结构- 概述各个章节的内容和主题四、软件安装与配置- 说明软件的安装流程和注意事项- 介绍如何配置phreeqc以满足用户需求五、模型与方法- 详述phreeqc软件中包含的各种模型和计算方法- 解释如何选择合适的模型和方法进行计算六、输入与输出- 说明如何准备和输入数据- 介绍phreeqc软件的输出结果及其含义七、常见问题与解决方案- 分析在使用过程中可能遇到的问题- 提供相应的解决方法和技巧八、软件更新与维护- 介绍如何获取软件的最新版本- 说明如何进行软件的更新和维护九、结论- 总结phreeqc中文使用手册的主要内容- 强调软件在实际应用中的优势和价值正文：一、引言phreeqc中文使用手册是为了帮助用户更好地理解和使用phreeqc软件而编写的。
本手册适用于phreeqc软件的初学者和有经验的用户，旨在提供全面、详细的软件操作指南和实用技巧。
通过阅读本手册，用户可以掌握phreeqc软件的使用方法，充分发挥其在地球化学计算领域的优势。
二、phreeqc软件概述phreeqc是一款功能强大的地球化学计算软件，全称为“PHREEQC - A Geochemical Modeling Program for Speciation, Batch, and One-Dimensional Transport”。
该软件由美国地质调查局（USGS）开发，基于PHREEQE软件，适用于多种场景下的地球化学计算。
phreeqc软件可以用于解决以下问题：1.分析化学反应在特定条件下的平衡性质；2.计算溶液中各种离子的活度系数；3.模拟物质在环境中的迁移和转化过程；4.为地质调查、环境保护和资源开发等项目提供地球化学数据支持。
安捷伦高效液相色谱仪操作说明一、校枪及样品处理1. 校枪仪器：两个小烧杯、分析天平、移液枪1000μL、100μL步骤：打开分析天平预热，一只烧杯放到称量盘上调零，一只装入纯水。
将移液枪1000μL、100μL调至950μL、50μL，分别吸取纯水称质量，如偏大或偏小，调节移液枪直至准确到0.001g。
（如枪使用过久或气密性不好，可以准备一个小烧杯装入纯水，每次先轻轻沾湿枪口，甩掉水或在用卫生纸吸取口上的水，再插上枪头使用。
）注意慢吸慢放，不要挂珠。
2. 样品处理：用1.5mL离心管取0.5mL发酵液，取出样品后，首现在离心机上离心（13000rap/min 3min）。
然后将样品稀释20倍，取950μL纯水、50μL发酵液于离心管中。
混匀后在离心机上离心（13000rap/min 3min）。
准备自动进样瓶（瓶中放上内衬管），取200μL待测液盖紧盖子。
按顺序放到自动进样盘中。
二、开机1、仪器各组件将在线脱气器、泵：四元、进样器：自动进样器（六通阀）、柱温箱、检测器：示差折光检测器开关打开。
打开计算机,进入Windows XP 画面,并运行CAC Server程序，打开色谱仪各组件电源，待显示已联上各组件的信息及各模块自检完成后，双击Instrument 1 Online ，图标打开工作站。
化学工作站自动与1200LC通讯。
流动相:将流动相（一般不主张使用偏酸、偏碱的流动相）放入溶剂瓶中，打开冲洗阀，设流速为2ml/min，单击确定，再依次单击泵→控制，选中启动，单击确定，则系统开始冲洗，至管路无气泡为止，切换管路反复操作至所需管路均无气泡。
在“控制”选项中选“关闭”，关闭泵，关闭冲洗阀。
单击泵下面瓶图标，输入溶剂的实际体积和瓶体积。
每次四元泵开始前，要打开冲洗阀阀门以5ml/min的流速冲洗10分钟，使脱气机与泵之间的流动相从冲洗阀流出，以免隔夜的流动相损伤色谱柱。
每次进样检测前，要用流动相冲洗色谱柱30分钟，达到柱温前20min流速设为0.2ml/min，达柱温后再改为0.6ml/min。
教程本节可使您了解使用 EZChrom Elite 的基础知识。
遵循这些步骤设臵方法并采集数据文件，然后优化积分方法并设臵校正。
可以使用软件附带的教程文件来“执行”该软件并熟悉其用法。
有关数据文件结构、应用程序窗口功能、如何打开和保存文件的详细信息均在“操作基础”中介绍。
注意：本教程假定 EZChrom Elite 的所有仪器都已安装并正确配臵。
程概述通过下面的列表，您可快速了解教程各节所包含的内容。
如果您刚刚开始使用 EZChrom Elite，则应按照显示的顺序执行教程中所有的步骤。
步骤 1：创建数据采集方法（可选）设臵采集运行时间和采样速率保存方法运行原始样品以图形方式设臵积分参数步骤 2：创建单级校正命名已校正的峰运行单级校正步骤 3：创建及运行样品序列步骤 4：使用教程文件检查多极校正浏览峰表检查自定义报告更改积分参数教程 - 采集和分析数据在本节教程中，您将使用数据系统采集数据和优化积分，设臵和运行校正标准，以及创建和运行样品序列。
有关高级操作的详细信息在本手册的后面几节中介绍。
仪器和方法向导要使系统使用方便，您可以使用内臵仪器和方法向导。
通过这些向导，可以方便地找到创建或修改方法所需的对话框，并逐步完成这些对话框。
如果使用方法向导来创建教程方法，使用向导按钮可转到与当前教程步骤相关联的对话框。
可从“仪器向导”框启动方法向导，方法是单击标识为“创建或修改方法”的按钮。
单击此按钮时，将出现方法向导，您可以通过它选择要使用向导的方式。
选择“创建新方法”按钮以开始创建教程方法。
选择了此按钮后，方法向导将在应用程序窗口设臵一个按钮库，使您可以“逐步”完成生成方法的所有对话框。
同时提供了一个保存按钮。
教程 - 创建数据采集方法采集数据文件的第一步是创建数据采集方法。
此时，您只需要确保使用缺省数据采集采样速率（除快速毛细管外,足够大多数样品的运行）时,使用该方法采集数据的运行时间足够长，能够让最后一个峰完成洗脱。
phreeqc中文使用手册摘要：一、引言- 介绍phreeqc 中文使用手册的目的和适用对象二、phreeqc 软件简介- 简要介绍phreeqc 软件的背景、功能和应用领域三、phreeqc 中文使用手册的结构- 详述手册中各章节的内容和作用四、phreeqc 软件安装与配置- 说明软件的安装流程和注意事项- 介绍如何进行软件配置以满足不同用户需求五、phreeqc 模型与输入参数- 详述phreeqc 软件中涉及的各种模型及作用- 解释输入参数的意义和设置方法六、phreeqc 软件操作步骤- 给出使用phreeqc 软件进行计算的具体步骤七、phreeqc 结果解读与分析- 说明如何解读和分析软件输出的结果- 提供一些实际应用案例以供参考八、phreeqc 软件的局限性与未来发展- 阐述phreeqc 软件在实际应用中可能存在的局限性- 展望软件未来发展的方向和可能的功能改进九、附录- 提供一些实用的附录，如软件操作中的常见问题解答、术语解释等正文：一、引言《phreeqc 中文使用手册》旨在帮助广大用户更好地理解和使用phreeqc 软件，为相关领域的科研和工程应用提供支持。
本手册适用于具有一定地球化学背景知识的读者，无论是初学者还是有经验的用户，都可以从中学到有关phreeqc 软件的详细信息。
二、phreeqc 软件简介phreeqc 是一款功能强大的地球化学计算软件，主要用于研究地下水、地表水、土壤等介质中的化学物质迁移转化过程。
该软件广泛应用于水资源管理、环境保护、地质勘探、农业等领域，为科研人员和工程师提供了方便的分析工具。
三、phreeqc 中文使用手册的结构本手册共分为九章，分别为：引言、phreeqc 软件简介、phreeqc 中文使用手册的结构、phreeqc 软件安装与配置、phreeqc 模型与输入参数、phreeqc 软件操作步骤、phreeqc 结果解读与分析、phreeqc 软件的局限性与未来发展以及附录。
【最新整理，下载后即可编辑】PHREEQC实例分析例2——矿物相的溶解平衡这个例子测定了最稳定相——石膏或是无水石膏在一定温度范围内的溶解性。
输入数据组见表13。
仅用pH和温度来定义纯水溶液。
缺省单位是millimolal，但没有指定浓度。
缺省状态下，pe为4.0，缺省的氧化还原计算使用pe，且水的密度为1.0（这是没有必要的，因为没有浓度为“每升”）。
在批反应期间，所有允许反应达到指定的饱和指数的反应都列在EQUILIBRIUM_PHASES中，无论开始时它们是否存在。
输入数据包含相的名字（之前通过PHASES输入在数据库或输入文件中定义）、指定的饱和指数和以摩尔数表示的当前存在的相的数量。
如果一种相在当前不存在，则在纯相集合中给定其浓度为0.0 mol。
在这个例子中，石膏和无水石膏允许反应来达到平衡（饱和指数等于0.0），初始相的集合中每种矿物都为1mol。
每一种矿物或是反应达到平衡或是被耗尽。
在大多数的情况下，1 mol的单纯相足够达到反应平衡。
表13--例2输入数据的设置TITLE Example 2.--Temperature dependence of solubilityof gypsum and anhydriteSOLUTION 1 Pure waterpH 7.0temp 25.0EQUILIBRIUM_PHASES 1Gypsum 0.0 1.0Anhydrite 0.0 1.0REACTION_TEMPERATURE 125.0 75.0 in 51 stepsSELECTED_OUTPUTfile ex2.selsi anhydrite gypsumEND在REACTION_TEMPERATURE数据块中，指定反应温度计算步长为1℃，从25℃开始，75℃结束，共计算温度变化51次。
温度的每一度变化都详细指定输入数据，在可能的情况下，在EQUILIBRIUM_PHASES（石膏和无水石膏）中定义的物质将会反应达到平衡，或是直到两种物质都完全溶解。
phreeqc中文使用手册【原创版】目录1.Phreeqc 简介2.安装与配置 Phreeqc3.Phreeqc 的基本语法4.Phreeqc 的应用示例5.总结正文【1.Phreeqc 简介】Phreeqc 是一个用于解决化学问题的计算机程序，特别是在矿物加工和矿物水文学方面。
它是一个基于 Python 的免费软件，可以运行在Windows、Linux 和 Mac OS X 等操作系统上。
Phreeqc 提供了大量的功能，包括溶液模拟、沉淀模拟、酸碱滴定、质量平衡计算等。
【2.安装与配置 Phreeqc】Phreeqc 的安装相对简单。
首先，需要下载 Phreeqc 的源代码，并按照官方提供的安装指南进行安装。
安装完成后，需要配置 Phreeqc 的环境变量，以便在运行时可以找到 Phreeqc 的可执行文件。
此外，Phreeqc 还需要一些依赖库，例如 Python 的 SciPy 和 NumPy 库，需要提前安装。
【3.Phreeqc 的基本语法】Phreeqc 的基本语法包括两个部分：输入文件和命令行参数。
输入文件包含了要解决的问题的详细描述，包括物质的化学式、初始浓度等信息。
命令行参数则指定了要运行的 Phreeqc 程序的选项和参数。
【4.Phreeqc 的应用示例】以下是一个 Phreeqc 的应用示例：假设有一个含有铜离子和氢氧根离子的溶液，我们想要知道当 pH 值为多少时，铜离子会开始沉淀。
我们可以使用 Phreeqc 的沉淀模拟功能来解决这个问题。
首先，我们需要编写一个输入文件，描述溶液的初始条件和要解决的问题。
然后，我们在命令行中指定输入文件和沉淀模拟选项，运行 Phreeqc 程序。
最后，我们得到结果：当 pH 值小于 4.76 时，铜离子开始沉淀。
【5.总结】Phreeqc 是一个功能强大的化学计算软件，特别适用于矿物加工和矿物水文学领域。
它的安装和配置相对简单，使用起来也较为方便。
Smart pH Cuvettes InstructionsOverviewThe Fiber Optic pH Sensor system consists of the following:∙Smart pH Cuvettes (1 cm x 1 cm PMMA or quartz) and/or patches∙Ocean Optics VIS-NIR spectrometer (or Jaz Sensor module)∙SpectraSuite software for reading values∙Light source (LS-1 Tungsten Halogen Light Source with a blue filter or a white LED)∙CUV-UV Cuvette Holder∙Connecting FibersCalibration requires recording spectra in high and low pH samples, as well as in at least one pH standard solution (such as a NIST-traceable buffer).The Smart pH Cuvettes use a sol gel sensing material coated onto the inner walls of a cuvette. The immobilized indicator dye(s) are encapsulated into the sol gel matrix, allowing for the diffusion of ions while preventing leaching of the dye. The cuvettes provide very accurate measurements in the biological range, from pH 5 to 9. Advantages of these cuvettes over traditional potentiometric devices include faster response time, easy storage and no maintenance, and low cost. These are especially useful for monitoring low conductivity samples such as boiler water, where electrode devices fail. Ocean Optics’ fully integrated pH systems provide full spectral analysis to help eliminate errors from dye leaching or from changes in turbidity, temperature, and ionic strength. Inherent calibration based on the physical properties of the immobilized indicator dye eliminates the need for frequent calibration. The ratiometric algorithm provides accurate and reproducible measurements at a high resolution.Smart pH Cuvettes are 1 cm by 1 cm and are made of Poly(methyl methacrylate) (PMMA) or quartz. The PMMA material has a temperature range of -5 °C to 70 °C, while quartz is available for specific applications such as high-temperature measurements. The cuvettes are compatible with aqueous solutions, ethanol/methanol solutions, ammonia, peroxides, sodium hypochlorite solutions, while the quartz cuvettes are also compatible with concentrated acids (nitric, sulfuric, acetic, etc) and acetone. Although PMMA cuvettes are designed to be disposable, they can be used multiple times. The lifetime depends on the extent of exposure to high pH levels and harsh chemicals. See CuvetteStorage/Lifetime for more information.Smart Cuvettes InstructionsCuvette Storage/LifetimeSmart Cuvettes can be stored dry at room temperature for any amount of time. As they are used, the cuvettes may slowly leach indicator dye from the sensing material. As a rule, once the maximum absorbance at pH 11 falls below 0.1, the cuvette should be discarded and replaced (assumes a reference of pH 1). The cuvette’s lifetime depends on frequency of use, harshness of the samples it is exposed to, the temperature of samples, and other environmental factors.Nature of SamplesThe analyte solutions being measured should have a pH within the biological range (pH 5-9) for accurate readings. Data obtained from analyte solutions that register values above or below this range should not be considered valid within the specifications of the cuvette. Concentrated acids and acetone will ruin the plastic cuvettes, so these types of chemicals should be avoided. Aqueous solutions, ethanol/methanol solutions, peroxides, ammonia, and sodium hypochlorite solutions are all compatible with the sensor material and the plastic cuvette. Samples should be optically transparent, having no turbidity or sediment present. It is also ideal to have analyte solutions that are colorless, though colored liquids can be compensated for.Response time is dependent on the ionic strength of the solution, with higher salinity samples responding notably faster. For example, using the calibration buffers of pH 5 – 8 will show a 90% response in 10 seconds or less, but when pure D.I. water is being measured, more time is needed to equilibrate at a final value. Make sure that the cuvette is filled to a sufficient level with the analyte solution. If the liquid level is at or below the optical path, the data will not be valid. To ensure there is sufficient liquid, it is recommended to fill the cuvette about 70% its height. Likewise, more accurate results will be obtained if the cuvette is rinsed once or twice with the analyte solution after calibration. This removes any residual buffer solution that may contaminate your sample. Note that once the cuvette has been affixed into the cuvette holder, it should not be moved or removed until all measurements have been completed.When immersed in solution, the film dyes may leach very slowly over time and will have to be replaced. The film response rate is limited by diffusion of ions into the material, therefore increasing stirring speed and ionic strength tend to increase the response rate.pH Smart Cuvette Set UpThe following procedures describe how to connect and calibrate pH Smart Cuvettes using a VIS-NIR spectrometer, a light source and SpectraSuite software. See your spectrometer and SpectraSuite manual for more detailed installation information.Installing the pH Sensor System►ProcedurePerform the steps below to install the pH Sensor components:1.Install SpectraSuite on your computer.2.Connect the spectrometer to your computer using the supplied USB cable.3.Install the light source as specified in its instructions.Smart Cuvettes Instructions4.Attach the fibers between the spectrometer, cuvette holder, and light source.5.Turn on the light source and allow it to warm up for the period specified in the light sourceinstructions.CautionMake sure that the cuvette says fastened in the cuvette holder with the tighteningscrew and that it does not move until all measurements have been completed.Any movement will change the optical signal, disrupting the quality of themeasurement.Calibrating the pH Sensor SystemThe Smart Cuvettes include a pre-calibrated pK value determined at the factory. This value was originally obtained at 22°C, and it is recalculated using the temperature compensation algorithm based on the temperature that was entered in SpectraSuite’s Calibration Wizard. Using the Factory Calibration method is ideal for being able to start making pH measurements quickly, though it is less accurate than performing an Independent Calibration. The specifications listed for the cuvettes assume a complete Independent Calibration, as this eliminates the errors seen from temperature and other environmental differences.Using Factory Calibration►Procedure1.Open SpectraSuite and select File | New | New Sol Gel pH Measurement.2.Click the Calibration Wizard button to begin the calibration.3.Select the spectrometer to use and click Next.4.Select Use Factory Calibration and click Next. The Experimental Parameters screen appears.Smart Cuvettes Instructions5.Enter your Experimental parameters: Acquisition Wavelength, Baseline Wavelength, andapproximate Ambient Temperature. Click Set, then click Next.NoteFor Smart pH Cuvettes that perform in the biological range (pH 5 – 9), theAcquisition Wavelength is 620nm and the Baseline Wavelength is 750nm.6.Enter the value for pK that came with your Smart pH Cuvette. Then click Next.7.Take a low pH reference spectrum at pH 1.0. To do this, fill the cuvette with pH 1 buffer.Allow it to sit for 5 seconds, then remove the buffer. Refill the cuvette with a fresh sample.Click Acquire. The program adjusts the integration time to prevent saturation. Whencomplete, click Next.8.Take a dark spectrum. To do this, block the light source and click Acquire Dark Spectrum.Then click Next.9.Unblock the light source.10.Take a high reference spectrum for pH 11.0. To do this, remove the pH 1 buffer and fill thecuvette with pH 11 buffer. Allow this to sit for 10 seconds, then remove the buffer and refillthe cuvette with a fresh sample. When complete, click Next.11. Depending on the value for pK you previously entered, the wizard will ask you to add pH 5 orpH 8 buffer. For pK values less than 6.5, pH 8 is used; for pK value greater than 6.5, pH 5 is used. Remove the pH 11 buffer and fill the cuvette with the requested pH buffer. Allow it tosit for 10 seconds, remove the buffer and refill the cuvette with a fresh sample. Click Acquire, and then click Finish.12.You are now ready to take pH measurements. See Taking pH Measurements.Performing an Independent CalibrationSmart Cuvettes Instructions ►Procedure1.Open SpectraSuite and select File | New | New Sol Gel pH Measurement.2.Click the Calibration Wizard button to begin the calibration.3.Select the spectrometer to use and click Next. Select Perform Independent Calibration andclick Next. The Experimental Parameters screen appears.4.Enter your Experimental parameters: Acquisition Wavelength, Baseline Wavelength, andapproximate Ambient Temperature. Click Set, then click Next.NoteFor Smart pH Cuvettes that perform in the biological range (pH 5 – 9), theAcquisition Wavelength is 620nm and the Baseline Wavelength is 750nm.5.Take a low pH reference spectrum at pH 1.0. To do this, fill the cuvette with pH 1 buffer.Allow it to sit for 5 seconds, then remove the buffer. Refill the cuvette with a fresh sample.Click Acquire. The program adjusts the integration time to prevent saturation. Whencomplete, click Next.6.Take a dark spectrum. To do this, block the light source and click Acquire Dark Spectrum.Then click Next.7.Unblock the light source.8.Take a high reference spectrum for pH 11.0. To do this, remove the pH 1 buffer and fill thecuvette with pH 11 buffer. Allow this to sit for 10 seconds, then remove the buffer and refill the cuvette with a fresh sample. When complete, click Next.9.Follow the wizard and repeat Step 8 for pH buffers 5, 6, 7, and 8 (follow on-screen prompts).Then, click Finish.10. You are now ready to take pH measurements. See Taking pH Measurements.Taking pH MeasurementsNow that you have finished calibration, you can take pH measurements in the biological range.►Procedure1.Fill the cuvette with the analyte solution for pH measurement in the biological range. The pHvalue appears on the screen in the Current pH field (upper right corner).Smart Cuvettes Instructions2.Press the Run/Stop button to toggle data acquisition appearing in the lower table on thescreen. Data is recorded at the time interval you specify in the Time Interval (sec) field.3.Click the Reset button to clear the table and restart the run time.4.Click the Export button to open a window to save your data in a format that can be openedwith Microsoft Excel or a text program such as Wordpad. The exported data file contains all of the variables that you have entered and have been calculated, along with a time stamp for data acquisition and save, the time-resolved pH data, and complete spectra for all reference and calibration buffers used.5.Click the Export Calibration button to open a window to save your calibration data. This willcreate a file containing the reference spectra and other variables that can later be loaded via the Calibration Wizard, allowing for very quick setup.Smart Cuvettes InstructionsAlgorithms Used pH Calculation⎪⎪⎭⎫ ⎝⎛-+=Sample pH Sample Abs Abs Abs Slope pK pH 11log *…where Abs Sample is the sample absorbance at 620nm with baseline correction, and Abs pH11 is the absorbance at pH 11 at 620nm with baseline correction.Temperature CompensationWhen you select Use Factory Calibration in SpectraSuite, the value for pK is adjusted via the van’t Hoff equation based on the current temperature you entered:⎪⎪⎭⎫ ⎝⎛+=⎪⎪⎭⎫ ⎝⎛+=⎪⎪⎭⎫ ⎝⎛--⎪⎪⎭⎫ ⎝⎛--121211*4801211*48012log log T T T T e pH pH e pK pK Resetting pK and SlopeAn x-y plot is made using data obtained from intermediate buffers 5 through 8. The x-axis is of the term:⎪⎪⎭⎫ ⎝⎛-Sample pH Sample Abs Abs Abs 11log …for each of the buffers. The y-axis shows the pH value of the buffers. This generates a plot such as the one shown below:Smart Cuvettes InstructionsPerforming a linear fit gives a line with pK equal to the y-intercept and slope equal to the slope. In the example chart above, the new pK value would be 6.2977 and the new slope value would be 1.7248. SpecificationsSpecification ValueSize and Materials 1 cm x 1 cm PMMA cuvette; 1 cm x 1 cm quartz cuvettesavailable for specific applications such as high-temperaturemeasurementspH Range Biological range (5-9)Temperature range-5 to 70 °C for PMMA cuvettesAccuracy<1% of readingResolution0.01 pHResponse Time90% step response in 10 secondsStandardization User needs 2 buffers (pH 1 and 11)Factory Calibration User needs 1 buffer (pH 5 or 8)User Complete Calibration Option Users have the option to perform their own complete calibration,requiring 4 buffers (pH 5, 6, 7, and 8)Sensory Signal Absorbance at 620nm and 750nmExpendable Parts PMMA cuvettes are semi-disposable, -- they can be usedmultiple times, but they are not intended for long-termpermanent useSmart Cuvettes Instructions Specification ValueUsage Lifetime Potential of being used as many as 50 or more measurements.Lifespan depends on extent of exposure to high pH levels andharsh chemicals. Discard after maximum absorbance at pH 11falls below 0.1 (assumes reference of pH 1).Recommended Spectrometer Configuration Any VIS/NIR spectrometer can be used. Either LS-1 (with blue filter) or white LED can be used as light source.Temperature Compensation Factory calibrated pK values are adjusted based on the user-inputted temperature via the van’t Hoff equation (see AlgorithmsUsed). Factory coefficients are generated at 22°C.Chemical Compatibility:PMMA Glass Aqueous solutions, ethanol/methanol solutions, ammonia, peroxides, sodium hypochlorite solutionsAll listed for PMMA plus concentrated acids (nitric, sulfuric, acetic, etc) and acetoneChemicals to Avoid for PMMA Concentrated acids (nitric, sulfuric, acetic, etc), acetone Sterilization:PMMA Glass Gamma irradiation and Ethylene OxideAutoclavable, Gamma irradiation, and Ethylene Oxide TBDSmart Cuvettes Instructions。
Genotype quality control with plinkQCHannah Meyer2021-07-15ContentsIntroduction1 Per-individual quality control (2)Per-marker quality control (2)Clean data (2)Workflow2 Per-individual quality control (3)Per-marker quality control (6)Create QC-ed dataset (8)Step-by-step8 Individuals with discordant sex information (8)Individuals with outlying missing genotype and/or heterozygosity rates (9)Related individuals (10)Individuals of divergent ancestry (11)Markers with excessive missingness rate (12)Markers with deviation from HWE (13)Markers with low minor allele frequency (14)References15 IntroductionGenotyping arrays enable the direct measurement of an individuals genotype at thousands of markers. Subsequent analyses such as genome-wide association studies rely on the high quality of these marker genotypes.Anderson and colleagues introduced a protocol for data quality control in genetic association studies heavily based on the summary statistics and relatedness estimation functions in the PLINK software[1].PLINK is a comprehensive,open-source command-line tool for genome-wide association studies(GWAS)and population genetics research[2].It’s main functionalities include data management,computing individual-and marker-level summary statistics,identity-by-state estimation and association analysis.Integration with R is achieved through its R plugin or PLINK/SEQ R Package[4].While the plugin is limited to operations yielding simple genetic marker vectors as output,the PLINK/SEQ R Package is limited in the functionalities it can access.plinkQC facilitates genotype quality control for genetic association studies as described by[1].It wraps around plink basic statistics(e.g.missing genotyping rates per individual,allele frequencies per genetic marker)and relationship functions and generates a per-individual and per-marker quality control report. Individuals and markers that fail the quality control can subsequently be removed with plinkQC to generatea new,clean dataset.Removal of individuals based on relationship status is optimised to retain as many individuals as possible in the study.plinkQC depends on the PLINK(version1.9),which has to be manually installed prior to the usage of plinkQC.It assumes the genotype have already been determined from the original probe intensity data of the genotype array and is available in plink format.Note:PLINK2.0is still in alpha status with many re-implementations and updates,including outputfile changes.While these changes are ongoing,plinkQC will rely on users using PLINK1.9.I will monitor PLINK 2.0changes and check compatibility with plinkQC.If users require specific PLINK2.0output,I recommend using the Step-by-Step approach described below and manually saving to the outputfiles expected from PLINK1.9.The protocol is implemented in three main functions,the per-individual quality control(perIndividualQC), the per-marker quality control(perMarkerQC)and the generation of the new,quality control dataset (cleanData):Per-individual quality controlThe per-individual quality control with perIndividualQC wraps around these functions:(i)check_sex:for the identification of individuals with discordant sex information,(ii)check_heterozygosity_and_missingness: for the identification of individuals with outlying missing genotype and/or heterozygosity rates,(iii) check_relatedness:for the identification of related individuals,(iv)check_ancestry:identification of individuals of divergent ancestry.Per-marker quality controlThe per-marker quality control with perMarkerQC wraps around these functions:(i)check_snp_missingnes: for the identifying markers with excessive missing genotype rates,(ii)check_hwe:for the identifying markers showing a significant deviation from Hardy-Weinberg equilibrium(HWE),(iii)check_maf:for the removal of markers with low minor allele frequency(MAF).Clean datacleanData takes the results of perMarkerQC and perIndividualQC and creates a new dataset with all individuals and markers that passed the quality control checks.WorkflowIn the following,genotype quality control with plinkQC is applied on a small example dataset with200 individuals and10,000markers(provided with this package).The quality control is demonstrated in three easy steps,per-individual and per-marker quality control followed by the generation of the new dataset. In addition,the functionality of each of the functions underlying perMarkerQC and perIndividualQC is demonstrated at the end of this vignette.package.dir<-find.package( plinkQC )indir<-file.path(package.dir, extdata )qcdir<-tempdir()name<- datapath2plink<-"/Users/hannah/bin/plink"Per-individual quality controlFor perIndividualQC,one simply specifies the directory where the data is stored(qcdir)and the prefix of the plinkfiles(i.e.prefix.bim,prefix.bed,prefix.fam).In addition,the names of thefiles containing information about the reference population and the merged dataset used in check_ancestry have to be provided:refSamplesFile,refColorsFile and prefixMergedDataset.Per default,all quality control checks will be conducted.In addition to running each check,perIndividualQC writes a list of all fail individual IDs to the qcdir.These IDs will be removed in the computation of the perMarkerQC.If the list is not present,perMarkerQC will send a message about conducting the quality control on the entire dataset.NB:To reduce the data size of the example data in plinkQC,data.genome has already been reduced to the individuals that are related.Thus the relatedness plots in C only show counts for related individuals only. NB:To demonstrate the results of the ancestry check,the required eigenvectorfile of the combined study and reference datasets have been precomputed and for the purpose of this example will be copied to the qcdir. In practice,the qcdir will often be the same as the indir and this step will not be required.file.copy(file.path(package.dir, extdata/data.HapMapIII.eigenvec ),qcdir)#>[1]TRUEperIndividualQC displays the results of the quality control steps in a multi-panel plot.fail_individuals<-perIndividualQC(indir=indir,qcdir=qcdir,name=name,refSamplesFile=paste(indir,"/HapMap_ID2Pop.txt",sep=""),refColorsFile=paste(indir,"/HapMap_PopColors.txt",sep=""),prefixMergedDataset="data.HapMapIII",path2plink=path2plink,do.run_check_ancestry=FALSE,interactive=TRUE,verbose=TRUE)overviewperIndividualQC depicts overview plots of quality control failures and the intersection of quality control failures with ancestry exclusion.overview_individuals<-overviewPerIndividualQC(fail_individuals,interactive=TRUE)Per-marker quality controlperMarkerQC applies its checks to data in the specified directory(qcdir),starting with the specified prefix of the plinkfiles(i.e.prefix.bim,prefix.bed,prefix.fam).Optionally,the user can specify different thresholds for the quality control checks and which check to conduct.Per default,all quality control checks will be conducted.perMarkerQC displays the results of the QC step in a multi-panel plot.fail_markers<-perMarkerQC(indir=indir,qcdir=qcdir,name=name,path2plink=path2plink,verbose=TRUE,interactive=TRUE,showPlinkOutput=FALSE)overviewPerMarkerQC depicts an overview of the marker quality control failures and their overlaps. overview_marker<-overviewPerMarkerQC(fail_markers,interactive=TRUE)Create QC-ed datasetAfter checking results of the per-individual and per-marker quality control,individuals and markers that fail the chosen criteria can automatically be removed from the dataset with cleanData,resulting in the new dataset qcdir/data.clean.bed,qcdir/data.clean.bim,qcdir/data.clean.fam.For convenience,cleanData returns a list of all individuals in the study split into keep and remove individuals.Ids<-cleanData(indir=indir,qcdir=qcdir,name=name,path2plink=path2plink,verbose=TRUE,showPlinkOutput=FALSE)Step-by-stepIndividuals with discordant sex informationThe identification of individuals with discordant sex information helps to detect sample mix-ups and samples with very poor genotyping rates.For each sample,the homozygosity rates across all X-chromosomal genetic markers are computed and compared with the expected rates(typically$<$0.2for females and$>$0.8for males).For samples where the assigned sex(PEDSEX in the.famfile)contradicts the sex inferred from the homozygosity rates(SNPSEX),it should be checked that the sex was correctly recorded(genotyping often occurs at different locations as phenotyping and misrecording might occur).Samples with discordant sex information that is not accounted for should be removed from the study.Identifying individuals with discordant sex information is implemented in check_sex.Itfinds individuals whose SNPSEX!=PEDSEX. Optionally,an extra data.frame with sample IDs and sex can be provided to double check if external and PEDSEX data(often processed at different centers)match.If a mismatch between PEDSEX and SNPSEX was detected,by SNPSEX==Sex,PEDSEX of these individuals can optionally be updated.check_sex depicts the X-chromosomal heterozygosity(SNPSEX)of the samples split by their(PEDSEX).fail_sex<-check_sex(indir=indir,qcdir=qcdir,name=name,interactive=TRUE,verbose=TRUE,path2plink=path2plink)Individuals with outlying missing genotype and/or heterozygosity ratesThe identification of individuals with outlying missing genotype and/or heterozygosity rates helps to detectsamples with poor DNA quality and/or concentration that should be excluded from the study.Typically,individuals with more than3-7%of their genotype calls missing are removed.Outlying heterozygosity ratesare judged relative to the overall heterozygosity rates in the study,and individuals whose rates are morethan a few standard deviations(sd)from the mean heterozygosity rate are removed.A typical qualitycontrol for outlying heterozygosity rates would remove individuals who are three sd away from the mean rate.Identifying related individuals with outlying missing genotype and/or heterozygosity rates is implemented in check_het_and_miss.Itfinds individuals that have genotyping and heterozygosity rates that fail the set thresholds and depicts the results as a scatter plot with the samples’missingness rates on x-axis and theirheterozygosity rates on the y-axis.fail_het_imiss<-check_het_and_miss(indir=indir,qcdir=qcdir,name=name,interactive=TRUE,path2plink=path2plink)Related individualsDepending on the future use of the genotypes,it might required to remove any related individuals from the study.Related individuals can be identified by their proportion of shared alleles at the genotyped markers(identity by descend,IBD).Standardly,individuals with second-degree relatedness or higher will be excluded.Identifying related individuals is implemented in check_relatedness.Itfinds pairs of samples whose proportion of IBD is larger than the specified highIBDTh.Subsequently,for pairs of individual that do not have additional relatives in the dataset,the individual with the greater genotype missingness rate is selected and returned as the individual failing the relatedness check.For more complex family structures,the unrelated individuals per family are selected(e.g.in a parents-offspring trio,the offspring will be marked as fail,while the parents will be kept in the analysis).NB:To reduce the data size of the example data in plinkQC,data.genome has already been reduced to the individuals that are related.Thus the relatedness plots in C only show counts for related individuals only. exclude_relatedness<-check_relatedness(indir=indir,qcdir=qcdir,name=name,interactive=TRUE,path2plink=path2plink)Individuals of divergent ancestryThe identification of individuals of divergent ancestry can be achieved by combining the genotypes of the study population with genotypes of a reference dataset consisting of individuals from known ethnicities(for instance individuals from the Hapmap or1000genomes study[5]).Principal component analysis on this combined genotype panel can be used to detect population structure down to the level of the reference dataset (for Hapmap and1000Genomes,this is down to large-scale continental ancestry).Identifying individuals of divergent ancestry is implemented in check_ancestry.Currently,check ancestry only supports automatic selection of individuals of European descent.It uses information from principal components1and2tofind the center of the European reference samples.All study samples whose euclidean distance from the centre falls outside a specified radius are considered non-European.check_ancestry creates a scatter plot of PC1 versus PC2color-coded for samples of the reference populations and the study population.exclude_ancestry<-check_ancestry(indir=indir,qcdir=qcdir,name=name,refSamplesFile=paste(indir,"/HapMap_ID2Pop.txt",sep=""),refColorsFile=paste(indir,"/HapMap_PopColors.txt",sep=""),prefixMergedDataset="data.HapMapIII",path2plink=path2plink,run.check_ancestry=FALSE,interactive=TRUE)Markers with excessive missingness rateMarkers with excessive missingness rate are removed as they are considered unreliable.Typically,thresholds for marker exclusion based on missingness range from1%-5%.Identifying markers with high missingness rates is implemented in snp_missingness.It calculates the rates of missing genotype calls and frequency for all variants in the individuals that passed the perIndividualQC.fail_snpmissing<-check_snp_missingness(indir=indir,qcdir=qcdir,name=name,interactive=TRUE,path2plink=path2plink,showPlinkOutput=FALSE)Markers with deviation from HWEMarkers with strong deviation from HWE might be indicative of genotyping or genotype-calling errors.As serious genotyping errors often yield very low p-values(in the order of10−50),it is recommended to choose a reasonably low threshold to avoidfiltering too many variants(that might have slight,non-critical deviations). Identifying markers with deviation from HWE is implemented in check_hwe.It calculates the observed and expected heterozygote frequencies per SNP in the individuals that passed the perIndividualQC and computes the deviation of the frequencies from Hardy-Weinberg equilibrium(HWE)by HWE exact test. fail_hwe<-check_hwe(indir=indir,qcdir=qcdir,name=name,interactive=TRUE,path2plink=path2plink,showPlinkOutput=FALSE)Markers with low minor allele frequencyMarkers with low minor allele count are often removed as the actual genotype calling(via the calling algorithm) is very difficult due to the small sizes of the heterozygote and rare-homozygote clusters.Identifying markers with low minor allele count is implemented in check_maf.It calculates the minor allele frequencies for all variants in the individuals that passed the perIndividualQC.fail_maf<-check_maf(indir=indir,qcdir=qcdir,name=name,interactive=TRUE,path2plink=path2plink,showPlinkOutput=FALSE)References1.Anderson CA,Pettersson FH,Clarke GM,Cardon LR,Morris AP,Zondervan KT.Data quality control in genetic case-control association studies.Nature Protocols.2010;5:1564–73.doi:10.1038/nprot.2010.1162.Purcell S,Neale B,Todd-Brown K,Thomas L,Ferreira MAR,Bender D,et al.PLINK:a tool set for whole-genome association and population-based linkage analyses.The American Journal of Human Genetics. 2007;81:559–75.doi:10.1086/5197953.Chang CC,Chow CC,Tellier LC,Vattikuti S,Purcell SM,Lee JJ.Second-generation PLINK:rising to the challenge of larger and richer datasets.GigaScience.2015;4:7.doi:10.1186/s13742-015-0047-84.PLINK/SEQ.2014.Available:https:///plinkseq/index.shtml5.The International HapMap Consortium.A haplotype map of the human genome.Nature.2005;437: 1299–320.doi:10.1038/nature042266.The International HapMap Consortium.A second generation human haplotype map of over3.1million SNPs.Nature.2007;449:851.doi:10.1038/nature062587.The International HapMap Consortium.Integrating common and rare genetic variation in diverse human populations.Nature.2010;467.doi:10.1038/nature092988.1000Genomes Project Consortium.An integrated map of structural variation in2,504human genomes. Nature.2015;526:75–81.doi:10.1038/nature153949.1000Genomes Project Consortium.A global reference for human genetic variation.Nature.2015;526: 75–81.doi:10.1038/nature15393。
