R语言 GGtools包 eqtlTests()函数中文帮助文档(中英文对照)

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eqtlTests(GGtools)
eqtlTests()所属R语言包：GGtools

                                         perform genome x transcriptome eQTL searches with high-performance options
                                         高性能选项执行的基因组x转录eQTL搜索

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

perform genome x transcriptome eQTL searches with high-performance options
高性能选项执行的基因组x转录eQTL搜索


用法----------Usage----------


eqtlTests(smlSet, rhs = ~1 - 1, runname = "foo", targdir = "foo",
        geneApply = lapply, chromApply = lapply, shortfac = 100, computeZ = FALSE,
        checkValid = TRUE, saveSummaries = TRUE, uncert=TRUE,
        family, genegran=50, prefilter = dropMonomorphies, ...)
eqtlEstimates(smlSet, rhs = ~1 - 1,
   runname = "fooe", targdir = "fooe",
   geneApply = lapply, chromApply = lapply,
   shortfac = 100, checkValid = TRUE,
   saveSummaries = TRUE, uncert = TRUE, family,
   genegran = 50, prefilter = dropMonomorphies, ...)
ieqtlTests (smlSet, rhs = ~1 - 1, rules, runname = "ifoo", targdir = "ifoo",
    geneApply = lapply, chromApply = lapply, shortfac = 100,
    computeZ = FALSE, uncert=TRUE, saveSummaries=TRUE,
    family, ...)



参数----------Arguments----------

参数：smlSet
instance of smlSet-class  
实例smlSet-class


参数：rhs
standard formula without dependent variable; predictors must be found in pData(smlSet)  
没有因变量的标准公式;预测必须找到pData(smlSet)


参数：runname
arbitrary character string that will identify a serialized object storing references to results  
任意字符串，将确定一个序列化的对象存储结果


参数：targdir
arbitrary character string that will name a folder where results are stored as ff files  
任意字符串，将其命名为一个结果ff文件保存的文件夹


参数：geneApply
lapply-like function for iterating over genes  
lapply样的迭代多基因的功能


参数：chromApply
lapply-like function for iterating over chromosomes  
lapply类似的功能，遍历过染色体


参数：shortfac
quantity by which chisquared tests will be inflated before coercion to short int  
数量chisquared测试，其中前强制将膨胀到短整型


参数：computeZ
logical to direct calculation of Zscore instead of X2  
逻辑X2的，而不是直接计算的Zscore


参数：checkValid
logical: shall the function run validObject on input smlSet?
逻辑：应函数的运行对输入smlSet validObject？


参数：saveSummaries
logical: shall a set of ff files be stored that includes genotype and allele frequency data for downstream filtering?
逻辑：FF文件应当包括下游过滤的基因型和等位基因频率数据存储？


参数：uncert
setting for value of uncertain argument in snp.rhs.tests
uncertainsnp.rhs.tests参数值设置


参数：family
specify the GLM family to use; defaults to 'gaussian' if left missing
如果左失踪指定的GLM的家庭使用;默认“高斯”


参数：...
parameters passed to snp.rhs.tests
参数传递snp.rhs.tests


参数：genegran
numeric value of frequency at which gene names will be catted to stdout in case options()$verbose == TRUE
频率数值基因的名字将被catted到stdout的情况下options()$verbose == TRUE


参数：rules
instance of ImputationRules-class
实例ImputationRules-class


参数：prefilter
function that takes and returns smlSet instance to be executed prior to any analysis
功能需要和返回smlSet实例被执行之前，任何分析


Details

详情----------Details----------

snp.rhs.tests (or snp.rhs.estimates is run for all genes enumerated in featureNames(smlSet) individually as dependent variables, and all SNP  in smList(smlSet) as predictors, one by one.  Each model fitted for SNP genotype is additionally adjusted for elements in rhs.  There are consequently G*S test results where G is the number of features in exprs(smlSet), and S is the total number of SNP in smlSet.  These are stored in ff files in folder targdir.  For eqtlEstimates the ff files are three-dimensional arrays with dimensions S x G x 2 where the top SxG subarray provides estimates, and the bottom, standard errors.
snp.rhs.tests（或snp.rhs.estimates运行featureNames(smlSet)单独作为因变量中列举的所有基因，所有的SNP在smList(smlSet)预测，逐个安装的SNP，每一个模型基因型此外调整为元素rhs。有因此G*S测试结果的G是功能exprs(smlSet)的数量，和S总的SNP的smlSet。这些都储存在ff文件夹targdir。eqtlEstimatesff文件是三维阵列尺寸S X国祥2顶部SXG子阵提供的估计，和底部，标准误差。

imphm3_1KG_20_mA2 is a set of imputation rules for SNP on chromosome 20, where the 1000 genomes genotypes distributed in "pilot1" VCF files are used to create imputations to loci not covered in the phase 3 hapmap data in ceuhm3.
imphm3_1KG_20_mA2是一组20号染色体上，其中1000个基因组的基因型分布在“pilot1 vcf文件用于创建估算位点在第3阶段HapMap数据不包括在ceuhm3为SNP的归责原则。

cisScores will fail if genes are present that are not on the chromosome for which scores are requested.
cisScores会失败，如果基因是目前分数要求的染色体上。


值----------Value----------

(i,m)eqtlTests returns instance of eqtlTestsManager
（我米）eqtlTests的实例返回eqtlTestsManager

cisScores returns list with elements for each gene consisting of chi-squared statistics for SNP cis to the genes according to settings of radius and useEnd
每个基因组成基因的SNP顺卡方统计，根据到半径和useEnd的设置元素cisScores返回列表


注意----------Note----------

We are using ff to manage the extremely voluminous results of comprehensive eqtl searches with one short int per test.  We do not have  an approach to handling NA in this framework, so for any nonexistent test result (due for example to monomorphy or total missingness) we impute a value from the null distribution of the test statistic being computed – chisq of one d.f..  There is no practical risk of misinterpreting such results in contexts of interest, but this saves us the complication of dealing with artificial masses of test statistic distributions at zero, for example.
我们使用ff管理与测试的每一个短整型极其浩繁全面eqtl搜索结果。我们不必在此框架内处理不适用的方法，对任何不存在的测试结果（例如monomorphy或总missingness到期），所以我们意指从空分布测试计算统计值 - 一个DF chisq。有曲解利益的背景下这样的结果是没有实际的风险，但是，这为我们节省了处理人工群众的检验统计量的分布在零，例如，并发症。

The topFeats methods have minMAF and minGTF parameters to assist in filtering results to SNPs with certain properties; the metadata used for these is stored in a summary ff structure.  
topFeats方法minMAF和minGTF参数，以协助过滤结果以单核苷酸多态性与某些属性，使用这些元数据存储在一个简易的FF结构。


作者（S）----------Author(s)----------



VJ Carey <stvjc@channing.harvard.edu>




举例----------Examples----------


library(GGdata)
hm2ceuSMS = getSS("GGdata", c("20", "21"), renameChrs=c("chr20", "chr21"))
library(illuminaHumanv1.db)
cptag = get("CPNE1", revmap(illuminaHumanv1SYMBOL))
indc = which(featureNames(hm2ceuSMS) == cptag[1])
#[]
# get a set of additional genes on chr20[得到额外的基因组chr20]
all20 = get("20", revmap(illuminaHumanv1CHR))
g20 = unique(c(all20[1:10], cptag))
#[]
hm = hm2ceuSMS[probeId(g20),]  # reduce problem[减少的问题]
td = tempdir()
curd = getwd()
setwd(td)
time.lapply = unix.time(e1 <- eqtlTests( hm, ~male ))
time.lapply
length(probesManaged(e1,1))
length(snpsManaged(e1,1))
e1
dir("foo")
time.lapply2 = unix.time(ee1 <- eqtlEstimates( hm, ~male ))
time.lapply2
ee1
dir("foo")
setwd(curd)
#[]
# see example("eqtlEstimatesManager-class") for illustration eqtlEstimates[看到例如（的“eqtlEstimatesManager级”）插图eqtlEstimates]
#[]
# additional examples are in the 'extras' folder, extrExt.txt[更多的例子是在“额外”的文件夹，extrExt.txt]
# []

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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