SASPECT(SASPECT)
SASPECT()所属R语言包：SASPECT
Significant AnalysiS of PEptide CounTs
重大肽计数分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A function for identifying differentially expressed proteins
识别差异表达的蛋白质的功能
用法----------Usage----------
SASPECT(peptideData, pep.set, pep.pro.name, run.group.info,
permu.iter=50, filter.run=2, filter.score=0.95)
参数----------Arguments----------
参数：peptideData
a list of two components: PeptideCount and PeptideConfidence. Both are numeric matrices with p rows each representing one peptide and n1+n2 columns each representing one sample (n1=sample size of the first group, and n2=sample size of the second group). PeptideCount records the peptide spectral counts of all p peptides in all n1+n2 samples. PeptideConfidence tracks the confidence score of each peptide identification in the database search procedure (e.g. the PeptideProphet score). Both matrics need to be arranged in the way that the first n1 columns represents samples from the first group and the rest columns are for the second group.
两部分组成的列表：PeptideCount和PeptideConfidence。两者都是p行，每行代表一个肽和n1+n2列各代表一个样本（n1=第一组样本量，并n2=样本大小的数字矩阵第二组）。 PeptideCount记录所有的p肽在所有n1+n2样品的肽谱的罪名。 PeptideConfidence跟踪每个肽识别数据库中的搜索程序的信心得分（例如在PeptideProphet的得分）。两个阵的需要被布置在n1的第一列代表的方式，从第一组的样品和其余列的第二组。
参数：pep.set
a character vector of length p. The ith element is the peptide ID corresponding to the ith row of peptideData$PeptideCount and peptideData$PeptideConfidence.
字符向量的长度为p。第i个元素是对应于第i行的peptideData$PeptideCount和peptideData$PeptideConfidence肽ID。
参数：pep.pro.name
a character matrix with 2 columns. The first column gives the protein IDs, and the second column gives the names of the peptides matching to the proteins in the first column.
一个字符用2列的矩阵。第一列给出了蛋白质的ID，和第二列给出的名称匹配，在第一列中的蛋白质的肽。
参数：run.group.info
a data frame with two columns. The first column (run.group.info$label) is a character vector of length 2, giving the group names of the two groups. The second column (run.group.info$count) is a numeric vector of length 2, giving the number of samples in the first group (n1) and the second group (n2).
有两列数据框。第一列是一个字符（run.group.info$label）向量长度为2，给两个组的组名。第二列（run.group.info$count）是一个数值向量长度为2，给第一组中的样本数（n1）和第二组（n2）。
参数：permu.iter
an integer. It is the number of permutation iterations for estimating FDR. The default value is 50.
一个整数。这是估计FDR的置换迭代的数目。默认值是50。
参数：filter.run
an integer. It is the filter criteria for removing peptides observed in too few samples. The default value is 2.
一个整数。它是过滤器标准除去肽太少，样本中观察到的。默认值是2。
参数：filter.score
a scale. PeptideConfidence scores above this value are counted in the filtering process. The default value is 0.95
的规模。 PeptideConfidence分数高于此值的计数中的滤波处理。默认值是0.95
Details
详细信息----------Details----------
This function implements the SASPECT-hybrid method (Wang et. al. 2008, in preparation), which is a modified version of the original SASPECT mothod proposed in Whiteaker et. al. 2007. The Score1 column in the returned matrix gives test statistics using the original SASPECT method.
此功能实现SASPECT杂交法（王等人，2008年，在准备中），这是一个修改后的版本在Whiteaker等提出的原SASPECT mothod。人。 2007年。 Score1列在返回的矩阵给出了测试统计，使用原SASPECT方法。
值----------Value----------
SASPECT generates a data frame with 7 columns: <table summary="R valueblock"> <tr valign="top"><td>Protein</td> <td> Protein groups' ID.</td></tr> <tr valign="top"><td>ProteinsInGroup</td> <td> Names of proteins in each protein group (separated by .).</td></tr> <tr valign="top"><td>Score1</td> <td> test score based on Appear-Absent (AA) measurements. A positive value suggests the abundence level in the second group is higher than the first group. A negative value suggests the opposite.</td></tr> <tr valign="top"><td>Score2</td> <td> test score based on non zero total Spectral count (SpecC) measurements. A positive value suggests the abundence level in the second group is higher than the first group. A negative value suggests the opposite.</td></tr> <tr valign="top"><td>Score</td> <td> final SASPECT score (sum square of Score1 and Score2).</td></tr> <tr valign="top"><td>Qvalue</td> <td> FDR resulted from permutation test based on Score.</td></tr> <tr valign="top"><td>PeptideNumber</td> <td> number of peptides observed for each protein(protein group).</td></tr> </table>
SASPECT生成一个数据框7列：表summary="R valueblock"> <tr valign="top"> <TD>Protein </ TD> <TD>蛋白质组的ID。</ TD> </ TR> <tr valign="top"> <TD> ProteinsInGroup </ TD> <TD>在每一个蛋白质组的蛋白质的名称（分离.“）。</ TD> </ TR> <tr valign="top"> <TD> Score1</ TD> <TD>测试成绩的基础上出现缺失（AA）的测量。正值表明abundence第二组中的水平是高于第一组。负值表明了相反的。</ TD> </ TR> <tr valign="top"> <TD>Score2 </ TD> <TD>测试成绩的基础上非零的总光谱数（SpecC）测量。正值表明abundence第二组中的水平是高于第一组。负值表明了相反的。</ TD> </ TR> <tr valign="top"> <TD>Score </ TD> <TD>的最后SASPECT得分（平方和Score1和Score2）。 </ TD> </ TR> <tr valign="top"> <TD>Qvalue </ TD> <TD> FDR置换检验的基础上Score。</ TD> </ TR> <tr valign="top"> <TD> PeptideNumber </ TD> <TD>观察每个蛋白质（蛋白质组）的肽。</ TD> </ TR> </表>
（作者）----------Author(s)----------
Wang, P. and Liu, Y.
参考文献----------References----------
Whiteaker, J. R., Zhang, H., Zhao, L., Wang, P., Kelly-Spratt, K. S., Ivey, R. G., Piening, B. D., Feng, L., Kasarda, E., Gurley, K. E., Eng, J. K., Chodosh, L. A., Kemp, C. J., McIntosh, M. W., Paulovich, A. G (2007) Integrated Pipeline for Mass Spectrometry-Based Discovery and Confirmation of Biomarkers Demonstrated in a Mouse Model of Breast Cancer. J. Proteome Res., 6(10); 3962-3975.
Wang, P., Liu, Y., McIntosh, M. W., Paulovich, A. G (2008) Significant analysis for comparative proteomics studies using label free LC-MS/MS experiments (in preparation).
实例----------Examples----------
library(SASPECT)
data(mouseTissue)
SASPECT.result<-SASPECT(peptideData=mouseTissue$peptideData,
pep.set=mouseTissue$pep.set,
pep.pro.name=mouseTissue$pep.pro.name,
run.group.info=mouseTissue$run.group.info,
permu.iter=50,
filter.run=2,
filter.score=0.95)
### it takes about 1 minute to run this example. [＃＃需时约1分钟的时间来运行这个例子。]
### check the qvalue distribution[＃＃检查qvalue的分布]
qvalue=as.numeric(SASPECT.result[,"Qvalue"])
plot(sort(qvalue))
### output the result into a table file[＃＃输出结果到一个表文件]
write.table(SASPECT.result, file="SASPECT.result.txt", row.names=FALSE, sep="\t")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-29 22:20:55
