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

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SPIATest(SPIAssay)
SPIATest()所属R语言包：SPIAssay

                                        Function to compute SPIA test
                                         函数来计算SPIA测试

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Function SPIATest computes SPIA distance and performs probabilistic test on a set of cell lines.
函数SPIATest计算的SPIA距离和执行概率测试的一组单元系。


用法----------Usage----------


SPIATest(x, row.names = TRUE, test.prob = TRUE,
         test.param = list(Pmm = 0.1, nsigma = 2, Pmm_nonM = 0.6, nsigma_nonM = 3, PercValidCall=0.9),
         verbose = FALSE)



参数----------Arguments----------

参数：x
a matrix with a column for each cell line and a row for each SNP
每种单元系的列和行的一个矩阵与每个SNP


参数：row.names
specify if the first column of x contains SNPs names/identificators
指定的x如果第一列包含单核苷酸多态性名称/ identificators的


参数：test.prob
specify if the function has to perform SPIA probabilistic test
指定的功能执行SPIA概率测试


参数：test.param
specify the parameters of the probabilistic test.<br> - Pmm: SNP probability of mismatch in a matching population<br> - nsigma: area limit for Pmm<br> - Pmm_nonM: SNP probability of mismach in a non matching population<br> - nsigma_nonM: area limit for Pmm_nonM<br> - PercValidCall: percentage of valid SNP calls to consider the test valid<br>
指定的参数的概率试验。参考 -  PMM：SNP错配的概率在一个匹配人口<br>物理化学学报 -  nsigma：区域限制为PMM参考 -  Pmm_nonM：SNP概率mismach在非匹配人口<br>物理化学学报 -  nsigma_nonM：面积限制为Pmm_nonM参考 -  PercValidCall：有效的SNP比例要求，考虑到测试的有效<BR>


参数：verbose
print verbose information
打印详细信息


值----------Value----------

<table summary="R valueblock"> <tr valign="top"><td>SPIAresult</td> <td> a matric with a line for each cell line and with columns with the informationss about distances In particular, each row of SPIAresult has 7 columns:<br> 1. SPIA distance  <br> 2. number of valid calls <br> 3. number of total calls <br> 4. number of calls where one of the two SPNs are not available <br> 5. number of calls where both SNPs are not available <br> 6. number of calls where SNP change from AA, BB to AB or from AB to AA, BB <br> 7. number of calls where SNP change from AA to BB or from BB to AA  <br></td></tr> <tr valign="top"><td>parameters</td> <td> the parameters used by the test (test.param)</td></tr> <tr valign="top"><td>input.param</td> <td> the number of samples (N_samples), the number of SNPs (N_SNPs), and if the probabilistic test has been done (test.prob)</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> SPIAresult</ TD> <td>一个行每种单元系和与列的informationss左右的距离基质在特别地，每个排SPIAresult有7列如下：<br> 1。上海浦东机场距离参考。的的有效调用<BR> 3号。数量的总通话<BR> 4。其中之一，两个SPN的不提供参考5的呼叫的数量。数调用两个单核苷酸多态性是不是可以参考6。数量呼叫SNP从AA，BB，AB或AB AA，BB参考7。通话数量的SNP变化，从AA到BB或BB AA参考</ TD> </ TR> <tr valign="top"> <TD> parameters</ TD> <TD>的使用测试（test.param）</ TD> </ TR> <tr valign="top"> <TD>input.param </ TD> <TD>的数量的样品（N_samples），参数数量的单核苷酸多态性（N_SNPs），如果已经完成的概率试验（test.prob）</ TD> </ TR> </表>


（作者）----------Author(s)----------


Francesca Demichelis &lt;demichelis@science.unitn.it&gt;, Davide Prandi &lt;prandi@science.unitn.it&gt;



实例----------Examples----------



library(SPIAssay)

# An example of genotype calls with:[一个例子基因型的调用方式：]
# - four cell lines[ - 四个单元系]
# - for each cell line five SNP calls[ - 每一个单元系5个SNP要求]
GenotypeCalls <- rbind(
c("SNP1", "AA", "AA", "AB", "AB"),
c("SNP2", "NoCall", "AA", "AB", "AB"),
c("SNP3", "AB", "AB", "AA" , "AB"),
c("SNP4", "BB", "BB", "BB", "BB"),
c("SNP5", "AB", "BB", "AA", "AA"))
colnames(GenotypeCalls) <- c("SNP_ID","CellLine1","CellLine2","CellLine3","CellLine4")

# Encode the data into SPIA format.[到SPIA的格式，对数据进行编码。]
# SPIA uses 0 for AA, 1 for BB, 2 for AB, and NA for NoCall[SPIA使用0为AA，BB，AB，NA NoCall]
# therefore, GenotypeCalls has to be encoded by meand of[因此，要被编码GenotypeCalls由劲歌]
# toSPIAData[toSPIAData]
encoding <- c("AA","BB","AB","NoCall")
SPIAGenotypeCalls <- toSPIAData(GenotypeCalls,encoding)

# Perform SPIA analysis[执行SPIA分析]
SPIAanalysis <- SPIATest(SPIAGenotypeCalls)
# This analysis give an error because there are not enough SNPs[这种分析给出一个错误，因为没有足够的单核苷酸多态性]

# Perform SPIA analysis with parameters SPIAParam to reduce the 'similar' region[执行SPIA分析与参数SPIAParam的，以减少“类似”的区域]
SPIAParam <- list(Pmm = 0.1, nsigma = 1, Pmm_nonM = 0.6, nsigma_nonM = 1, PercValidCall=0.7)
SPIAanalysis <- SPIATest(SPIAGenotypeCalls, row.names = TRUE, test.prob = TRUE, SPIAParam)


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