BCGreport2
ureport2 参数
ureport2 参数
随着科技的不断进步，各种参数越来越重要。
port2参数也是其中一种重要的参数，被广泛应用。
port2参数是一种统计分析方法，它可以从数据中推断出两个对象之间的关系。
它可以用来计算关联系数，可以用来衡量两个变量的相关性，从而可以更准确地反映出它们之间的关联关系或相关性。
与其它统计分析方法不同，port2参数不仅考虑变量之间的直接关系，还考虑变量之间共同影响的情况，从而可以更准确地分析出数据之间的关系。
port2参数实际上是一种线性回归分析，其核心是用一个线性模型来描述两个变量之间的关系，它可以用来预测一个变量的变化情况，以及它会如何受另一个变量的影响。
在实践中，为了正确估计模型的参数，需要拟合多个变量的数据，以避免出现过拟合的情况。
同时，port2参数也可以用来检验数据间的相关性，从而可以排除统计上的偏差，可以精确地捕捉出数据之间的相关性。
它还可以用来验证变量之间是否存在非线性关系，也可以进行多元线性回归，从而获得更准确的参数估计。
总之，port2参数是一种广泛应用的统计分析方法，可以用来检验数据之间的相关性，可以排除统计上的偏差，可以用来验证变量之间是否存在非线性关系，也可以用来进行多元线性回归，从而获得更准确的参数估计。
由于它在统计分析中的重要性，越来越多的科学家和应用者开始重视和使用这一参数。
希望本文对那些正在学习研究
port2参数的人有所帮助，激励他们积极地探索知识，为科学发展做出贡献。
ureport2函数百分比
ureport2函数百分比urusport2是一个用于计算百分比的函数。
百分比是指以每百单位为基数的比例。
这个函数可以将一个数值转换为百分比形式，并返回结果。
下面是urusport2函数的伪代码：```function ureport2(num, total):percentage = (num / total) * 100return percentage```在这个函数中，`num`表示要计算百分比的数值，`total`表示总数。
首先，函数将`num`除以`total`得到一个小数，然后乘以100将其转换为百分比形式。
最后，函数返回计算后的百分比值。
以下是关于urusport2函数的一些示例：示例1：```percentage = ureport2(10, 20)print(percentage)```输出为：50说明：将10除以20得到0.5，然后乘以100得到50%，所以结果为50。
示例2：```percentage = ureport2(4, 5)print(percentage)```输出为：80说明：将4除以5得到0.8，然后乘以100得到80%，所以结果为80。
示例3：```percentage = ureport2(30, 100)print(percentage)```输出为：30说明：将30除以100得到0.3，然后乘以100得到30%，所以结果为30。
以上就是urusport2函数的实现方法和相关示例。
使用这个函数可以方便地计算任意数值的百分比，并得到对应的结果。
[官方]BeyondCompare里面二进制比较的含义.
![[官方]BeyondCompare里面二进制比较的含义.](https://img.taocdn.com/s3/m/c23bcb5ace84b9d528ea81c758f5f61fb7362865.png)
[官⽅]BeyondCompare ⾥⾯⼆进制⽐较的含义.Content ComparisonsActions > Compare Contents In the Actions menu, the Compare Contents command performs content comparisons on the selected pairs of files to determine if they match.Content comparison methodsCRC comparison compares files using their CRC values.Binary comparison compares files byte-by-byte.Rules-based comparison compares files based on their associations. It allows you to define unimportant differences, such as changes in whitespace or source code comments. A rules-based comparison can also ignore differences in file encoding or line endings.Results of content comparisonsCRC and Binary comparisons return one of these results:Binary same The files are exactly the same.Binary differences At least one byte is different between the files.Rules-based comparisons have a built-in binary comparison and return one of these results:Binary sameThe files are exactly the same.Rules-based same The files have binary differences, such as character encoding, that can be ignored.Unimportantdifferences A rules-based comparison found only unimportant differences.Importantdifferences A rules-based comparison found important differences.When content comparisons are performedContent comparisons are performed:·when a folder session is loaded, and its session settings call for automatic content comparisons·explicitly, when the Compare Contents command is used·when a pair of files is opened in a file session。
CBCgrps 2.8 版本软件说明书
Package‘CBCgrps’October12,2022Type PackageTitle Compare Baseline Characteristics Between GroupsVersion2.8.2Date2021-4-16Author Zhongheng Zhang,Sir Run Run Shaw hospital,Zhejiang university school of medicineMaintainer Zhongheng Zhang<********************.cn>Depends R(>=3.2.0),nortest(>=1.0-4)Description Compare baseline characteristics between two or more groups.The variables being com-pared can be factor and numeric variables.The function will automatically judge the type and dis-tribution of the variables,and make statistical description and bivariate analysis.License GPL-2RoxygenNote7.1.1NeedsCompilation noRepository CRANDate/Publication2021-04-1506:00:04UTCR topics documented:CBCgrps2.8-package (2)df (3)dt (4)multigrps (5)twogrps (7)Index1012CBCgrps2.8-package CBCgrps2.8-package Compare Baseline Characteristics Between GroupsDescriptionThe package aims to automate the process of comparing Baseline Characteristics between groups.DetailsThe DESCRIPTIONfile:In clinical studies emplying electronic medical records,the variables to be investigated are usually large in number.It is sometimes cumbersome to compare these variables between two or more groups one by one.I design this package to automate the process of statistical description and bivariate statistical inference.Author(s)Zhongheng Zhang Department of emergency medicine,Sir Run-Run Shaw Hospital,Zhejiang Uni-versity School of Medicine,Hangzhou,310016,China.<********************.cn>Maintainer: Zhongheng ZhangReferencesZhang Z.Univariate description and bivariate statistical inference:thefirst step delving into data.Ann Transl Med.2016Mar;4(5):91.Zhang Z,Gayle AA,Wang J,Zhang H,Cardinal-Fernasndez paring baseline characteristics between groups:an introduction to the CBCgrps package.Ann Transl Med.2017Dec;5(24):484.doi:10.21037/atm.2017.09.39.See AlsoNo ther reference.Examplesdata(df)a<-twogrps(df,"mort")df3 df simulated dataset as a working exampleDescriptionA data frame with1000observations on the following7variables.Usagedata("df")FormatA data frame with1000observations on the following7variables.crp a numeric vector,C-reactive protein measured in mg/lhb a numeric vector,hemoglobinddimer a numeric vectorwbc a numeric vector,white blood cellcomorbid a factor with levels cirrhosis COPD diabetes heartfailure hypertension renalfailure strokesex a factor with levels female malemort a factor with levels alive deadDetailsThe dataset is generated as a working example without clinical relevance.SourceSimulated dataset without sources.Referencessimulated dataset without reference.Examplesdata(df)##maybe str(df);plot(df)...4dt dt simulated dataset as a working exampleDescriptionA data frame with500observations on the following8variables.Missing values are also presentUsagedata("dt")FormatA data frame with500observations on the following8variables.crp a numeric vector,C-reactive protein measured in mg/l,with missing valuesvaso a factor with two levels Yes No,indicating the use of vasopressor or notwbc a numeric vector,white blood cell countlac a numeric vector,serum lactateage a numeric vector,age in yearstype a factor with levels surgery medical emergencygender a factor with levels female malemort an integer with two values10DetailsThe dataset is generated as a working example without clinical relevance.SourceSimulated dataset without sources.Referencessimulated dataset without reference.Examplesdata(dt)##maybe str(df);plot(df)...multigrps Compare Baseline Characteristics between three or more groupsDescriptionThe main function of the CBCgrps package.Usagemultigrps(df,gvar,p.rd=3,varlist=NULL,skewvar=NULL,norm.rd=2,sk.rd=2,tabNA="no",cat.rd=0,pnormtest=0.05,maxfactorlevels=30,minfactorlevels=10,sim=FALSE,workspace=2e+05,ShowStatistic=F)Argumentsdf The data frame on which statistical descroption and inference are performed.gvar The group variable.p.rd Decimal space of p value to be displayed.If the p value is less than the minimum value of that decimal space,it will print less than that value.For instance,if p.rd=3and p=0.00045,then it will print"<0.001"in the p column of the table.varlist Specify a vector of variable names to be compared between groups(i.e.not all variables in the*df*will be compared and users can choose which variablesto be compared).This argument also allows to specify the order in which thevariables will appear in the table.It will issue an error message if varlist containsvariable names not found in the df.skewvar Specify a vector of variable names which are considered to be not normally distributed.This function is useful for some large datasets where the normalitytest is too sensitive and users may want to specify skew variables by their ownjudgement.skewvar is NULL by default.If it is not NULL and skew data werespecified by the users,the statistical test for normality is switched off.norm.rd Decimal space of normally distributed numeric variables to be displayed.sk.rd Decimal space of skewed numeric variables to be displayed.tabNA Weather categorical variables with NA be displayed or not."no"to be omitted, "ifany"to be displayed.The default value is"no".cat.rd Decimal space of categorical variables(the proportion)to be displayed.pnormtest Significance level for the normal test.It is0.05by convention(default).How-ever,for some large datasets the test will be too sensitive that only a small de-viation in magnitude from the normal distribution will give a p value less than0.05.In this situation,users can specify smaller significance level.Note thatthe normality test will no longer be used to judge the normality if the skewvarargument is not NULL.maxfactorlevelsThe maximum levels for factor variables,the default is30.The argument is usedto avoid treating date or time variables as factor variables.minfactorlevelsIf a numeric variable has only several values,it is treated as categorical variable.The default value is10.sim a logical indicating whether to compute p-values by Monte Carlo simulation,in larger than2by2tables.The default is FALSE.workspace If thefisher.test()fucntion requires more workspace,it can be defined here.The default is workspace euqals to2e+05.ShowStatistic logic value for whether showing statistics or not.The defaule is FALSE for not showing statistics.Statistics is used for statistical inference such as F value forChi-square test and T value for student t test.No statistic will be shown becauseFisher’s exact test jumps past a test statistic and goes straight to a p-value.DetailsThe function compares differences in categorical and continuous variables between three or more groups.The function automatically judges the distribution of the continuous variable and use ap-propriate description for them.Chi-square test is used for categorical data.Analysis of variance is used for normally distributed numeric data.Kruskal-Wallis rank sum test is used for non-normally distributed data.It is common that some categorical variables contain numeric or integer values.For example,the gender variable may contain values1and2,representing male and female respectively.Such a variable can be identified by counting the number of integer values.Thus,the minfactor-levels argument is used to define the minimum value for a variable to be considered as numeric variable.For some large dataset,the normality test is extremely sensitive that a small deviation from normal distribution will lead to the rejection of the null hypothesis of normality.In such a cir-cumstance,users may opt to switch off the normality test by explicitly specify the skewed data(i.e.skewvar=some variables names)or set a smaller p value for normal test(i.e.pnormtest=0.0001).ValueTable The table with string values.The mean and standard error are put in a single cell,and connected by plus and minus symbol.NoteNo further notesAuthor(s)Zhongheng Zhang Department of emergency medicine,Sir Run-Run Shaw Hospital,Zhejiang Uni-versity School of Medicine,Hangzhou,310016,China.<********************.cn> ReferencesMyles Hollander and Douglas A.Wolfe(1973),Nonparametric Statistical Methods.New York: John Wiley&Sons.Pages115-120.Zhang Z.Univariate description and bivariate statistical inference:thefirst step delving into data.Ann Transl Med.2016Mar;4(5):91.Zhang Z,Gayle AA,Wang J,Zhang H,Cardinal-Fernasndez paring baseline characteristics between groups:an introduction to the CBCgrps package.Ann Transl Med.2017Dec;5(24):484.doi:10.21037/atm.2017.09.39.See AlsoNo other referenceExamplesdata(df)b<-multigrps(df,"comorbid")print(b,quote=TRUE)twogrps Compare Baseline Characteristics between two groupsDescriptionThe main function of the CBCgrps package.The function compares differences in categorical and continuous variables between two groups.The function automatically judges the distribution of the continuous variable and use appropriate description for them.Wilcoxon rank sum test is employed for non-normal data.Usagetwogrps(df,gvar,varlist=NULL,p.rd=3,skewvar=NULL,norm.rd=2,sk.rd=2,tabNA="no",cat.rd=0,pnormtest=0.05,maxfactorlevels=30,minfactorlevels=10,sim=FALSE,workspace=2e+05,ShowStatistic=F,ExtractP=0.05)Argumentsdf The data frame on which statistical descroption and inference are performed.gvar The group variable.p.rd Decimal space of p value to be displayed.If the p value is less than the minimum value of that decimal space,it will print less than that value.For instance,if p.rd=3and p=0.00045,then it will print"<0.001"in the p column of the table.varlist Specify a vector of variable names to be compared between groups(i.e.not all variables in the df will be compared and users can choose which variablesto be compared).This argument also allows to specify the order in which thevariables will appear in the table.skewvar Specify a vector of variable names which are considered to be not normally distributed.This function is useful for some large datasets where the normalitytest is too sensitive and users may want to specify skew variables by their ownjudgement.skewvar is NULL by default.If it is not NULL and skew data werespecified by the users,the statistical test for normality is switched off.norm.rd Decimal space of normally distributed numeric variables to be displayed.sk.rd Decimal space of skewed numeric variables to be displayed.tabNA Weather categorical variables with NA be displayed or not."no"to be omitted, "ifany"to be displayed.The default value is"no".cat.rd Decimal space of categorical variables(the proportion)to be displayed.pnormtest Significance level for the normal test.It is0.05by convention(default).How-ever,for some large datasets the test will be too sensitive that only a small de-viation in magnitude from the normal distribution will give a p value less than0.05.In this situation,users can specify smaller significance level.Note that thenormality test will be switched off by specifying skewvar argument.maxfactorlevelsThe maximum levels for factor variables,the default is30.The argument is usedto avoid treating date or time variables as factor variables.minfactorlevelsIf a numeric variable has only several values,it is treated as categorical variable.The default value is10.sim A logical indicating whether to compute p-values by Monte Carlo simulation,in larger than2by2tables.The default is FALSE.workspace If thefisher.test()fucntion requires more workspace,it can be defined here.The default is workspace=2e+05.ShowStatistic logic value for whether showing statistics or not.The defaule is FALSE for not showing statistics.Statistics is used for statistical inference such as F value forChi-square test and T value for student t test.No statistic will be shown becauseFisher’s exact test jumps past a test statistic and goes straight to a p-value.ExtractP Some variables with p value less than a certain threshoud can be extracted for subsequent multivariate regression modeling.The parameter specifies thethreshoud of p value for a variable to be extrtacted.DetailsIt is common that some categorical variables contain numeric or integer values.For example,the gender variable may contain values1and2,representing male and female respectively.Such a variable can be identified by counting the number of integer values.Thus,the minfactorlevels argument is used to define the minimum value for a variable to be considered as numeric variable. ValueTable A table with string values.The mean and standard error are put in a single cell, and connected by plus and minus symbol.VarExtract A character vector containing variable names.These extracted variables have p value less than ExtractP in univariate analysis.NoteNo further notesAuthor(s)Zhongheng Zhang Department of emergency medicine,Sir Run-Run Shaw Hospital,Zhejiang Uni-versity School of Medicine,Hangzhou,310016,China.<********************.cn> ReferencesZhang Z.Univariate description and bivariate statistical inference:thefirst step delving into data.Ann Transl Med.2016Mar;4(5):91.Zhang Z,Gayle AA,Wang J,Zhang H,Cardinal-Fernandez paring baseline characteristics between groups:an introduction to the CBCgrps package.Ann Transl Med.2017Dec;5(24):484.doi:10.21037/atm.2017.09.39.See AlsoNo other referenceExamplesdata(df)a<-twogrps(df,"mort")print(a,quote=TRUE)#define skewed variables manuallyprint(twogrps(df,"mort",skewvar=c("crp","wbc")),quote=TRUE)Index∗Comparetwogrps,7∗baselinemultigrps,5twogrps,7∗bivariate analysis;statisitcal descriptionCBCgrps2.8-package,2∗comparemultigrps,5∗datasetsdf,3dt,4CBCgrps(CBCgrps2.8-package),2CBCgrps2.8-package,2df,3dt,4multigrps,5twogrps,710。
monocle2结果解读
monocle2结果解读
Monocle2是一个用于单细胞RNA测序数据的分析工具，它可以对单细胞数据进行降维分析，并模拟出时间发育过程的动态变化。
在解读Monocle2结果时，首先需要了解数据的基本特征和实验设计。
Monocle2基于关键基因的表达模式，对每个细胞进行排序，以模拟时间发育过程的动态变化。
因此，结果中通常包括每个细胞的基因表达谱、聚类结果、拟时间值等信息。
基因表达谱：Monocle2可以绘制每个细胞的基因表达谱，展示不同基因在不同细胞中的表达情况。
通过比较不同细胞之间的基因表达模式，可以发现不同细胞之间的差异和相似性，从而对细胞进行分类和聚类。
聚类结果：Monocle2可以将相似的细胞聚类成一组，并可以对每个聚类进行注释和标记。
通过比较不同聚类之间的基因表达模式，可以发现不同细胞类型之间的差异和相似性。
拟时间值：Monocle2可以根据每个细胞的基因表达模式计算出拟时间值，从而模拟出时间发育过程的动态变化。
拟时间值可以帮助研究人员了解细胞在时间发育过程中的变化趋势和顺序。
在解读Monocle2结果时，还需要注意数据的质量控制和可重复性。
由于单细胞RNA测序数据存在较高的技术变异性和批次效应，因此需要进行充分的数据质量控制和标准化处理。
同时，为了确保结果的可靠性和可重复性，建议进行多次实验和重复分析。
总之，Monocle2结果解读需要结合实验设计和数据特征进行综合分析。
通过对基因表达谱、聚类结果和拟时间值等信息进行深入挖掘，可以帮助研究人员更好地理解细胞在时间发育过程中的变化和功能。
finalreport基因组数据格式介绍
finalreport基因组数据格式介绍摘要：一、引言二、基因组数据格式概述1.基因组数据的基本组成2.常见的基因组数据格式三、FASTA 格式1.FASTA 格式的特点2.FASTA 格式的应用场景四、GenBank 格式1.GenBank 格式的特点2.GenBank 格式的应用场景五、FASTQ 格式1.FASTQ 格式的特点2.FASTQ 格式的应用场景六、SAM 格式1.SAM 格式的特点2.SAM 格式的应用场景七、BED 格式1.BED 格式的特点2.BED 格式的应用场景八、结论正文：一、引言随着基因组学研究的发展，基因组数据在生物信息学领域中占据越来越重要的地位。
对于这些庞大的基因组数据，我们需要一种合适的格式进行存储和传输。
本文将对基因组数据格式进行介绍，并重点分析几种常见的格式。
二、基因组数据格式概述基因组数据通常包括序列数据和注释信息。
序列数据可以是DNA、RNA 或蛋白质序列，而注释信息包括基因注释、转录因子结合位点、调控元件等。
为了方便存储和传输这些数据，科学家们制定了许多基因组数据格式。
三、FASTA 格式FASTA 格式是一种常用于表示核酸或蛋白质序列的文本格式。
它以简单的文本形式存储序列数据，易于阅读和编辑。
FASTA 格式的主要特点包括：每行包含一个序列，序列之间以空行分隔；序列中的碱基或氨基酸用单个字符表示；对于核酸序列，碱基T 在文件中用字母"U"表示。
FASTA 格式适用于序列数据的初稿存储和传输。
四、GenBank 格式GenBank 格式是一种二进制格式，主要用于存储基因组序列和相关的注释信息。
GenBank 文件通常包含多个序列，每个序列都由独立的记录组成。
每个记录包括序列标识符、物种信息、序列长度、核酸类型、存放位置等信息。
GenBank 格式适用于基因组数据的长期存储和分布式共享。
五、FASTQ 格式FASTQ 格式是一种用于表示高通量测序数据的文本格式。
ureport2 计数
ureport2 计数Ureport2是一个短信问答系统，主要用于收集社区居民的意见和建议。
该系统可以通过短信、社交媒体和网页等方式向用户发出问题和调查，用户回复短信后，他们的回答被储存在数据库中，方便后续进行数据分析和统计。
其中一个重要的功能就是“Ureport2计数”，这个功能可以让用户对回答的选项进行投票，以便后续进行数据分析。
以下是如何使用“Ureoprt2计数”的步骤：1.登录Ureport2平台：首先，你需要登录Ureport2平台（http://ureport.in/），并创建一个账户。
如果你已经有账户了，直接登录即可。
在平台首页中，你可以看到自己创建的问卷和数据统计。
2.创建一个问题：点击左侧的“新建问题”按钮，输入问题的标题和内容。
可以选择问题的类型，如单选、多选、开放问答等。
然后，点击“下一步”按钮进入问题设置页面。
3.设置问题选项：在问题设置页面中，你可以为问题设置具体的选项，允许用户进行投票。
比如，如果问题的类型是单选，则需要为每个选项设置一个选项编号和选项文本。
如果问题的类型是多选，则可以设置多个选项。
设置完成后，点击“下一步”按钮。
4.设置“Ureport2计数”功能：在问题设置页面的下方，有一个“Ureport2计数”选项。
勾选这个选项后，系统会自动为每个选项生成一个计数器，并在用户投票后进行计数。
然后设置完毕后，点击“发布”按钮即可。
5.测试问题：在发布问题之前建议进行测试，因为软件可能会出现错误或不可预见的问题。
你可以通过自己的手机或电脑进行测试，向该问题发送短信或填写问卷，并检查系统是否正确计算了回答。
6.查看数据：如果问题发布成功，你可以在Ureport2平台上查看问题的统计数据和分析报告。
在问题统计页面中，你可以看到每个选项的计数器数量，以及回答的用户数量等信息。
如果需要进一步分析数据，你可以将数据导出到Excel文件中进行处理。
总的来说，Ureport2计数功能是一个非常有用的工具，可以帮助你收集社区居民的意见和建议，并进行数据分析和统计。
Inventory Report for Magento 2
Inventory Report for Magento 2User GuideTable of Content1.Extension Installation Guide2.Inventory Report Change on Order Placement3.Inventory Report Change after Shipment Generation4.Inventory Report Change After Credit Memo Generation5.Inventory Report Change After Product Quantity Update1.Extension Installation•Create a folder structure in Magento root as app/code/Meetanshi/Inventory•Download and extract the zip folder and upload our extension files to the app/code/Meetanshi/Inventory via FTP.•Login to your SSH and run below commands step by step:o php bin/magento setup:upgradeo For Magento version 2.0.x to 2.1.x - php bin/magento setup:static-content:deployo For Magento version 2.2.x & above - php bin/magento setup:static-content:deploy –f o php bin/magento cache:flush2.Inventory Report Change on Order PlacementThe extension gets auto enabled after the installation. Whenever a new order is placed, the salable quantity gets decreased in the inventory report under Catalog > Inventory Report.3.Inventory Report Change After Shipment GenerationWhenever the order is shipped or an order shipment is generated, the quantity gets increased in the inventory report under Catalog > Inventory Report.4.Inventory Report Change After Credit Memo GenerationWhenever the order is cancelled or a new credit memo is generated, the quantity and salable quantity get increased (*only if “Return to Stock” is ticked while generating the credit memo from the backend) in the inventory report under Catalog > Inventory Report. The admin can also export the inventory report in CSV or XML format.5.Inventory Report Change After Product Quantity UpdateWhenever the admin updates the product quantity from Catalog > Products > Product edit, the quantity and salable quantity get updated in the inventory report under Catalog >Inventory Report.Similarly, whenever an action related to inventory is performed, the quantity is updated under the inventory report in Catalog > Inventory Report.。