MATScore(rMAT)
MATScore()所属R语言包:rMAT
Detection of enriched regions
富硒区域的检测
译者:生物统计家园网 机器人LoveR
描述----------Description----------
This function is used to compute the rMAT scores following normalization of expression values in order to locate putative enriched regions. This function is now defunct now defunct and you should instead use 'computeMATScore'.
这个函数用来计算表达式的值标准化后,为了找到公认的富集区域的rMAT分数。此功能是现在倒闭现已解散的,你应该使用“computeMATScore的。
用法----------Usage----------
MATScore(tilingSet, cName="NULL", dMax=600,nProbesMin=8, dMerge=300,method="score",threshold=5,verbose=FALSE,bedName="NULL")
参数----------Arguments----------
参数:tilingSet
This object contains an ExpressionSet
此对象包含1 ExpressionSet的
参数:cName
Unique identifier of control name
控制名称的唯一标识符
参数:dMax
An integer value. The sliding window side of which the adjacent probes are to average upon in order to compute the rMAT score.
一个整型值。其中相邻探针是为了计算rMAT的得分平均后滑动窗口的一面。
参数:nProbesMin
An integer value. The minimum number of probes to average upon. If the number of probes within the interval is less than nProbesMin, the rMAT score of the region will not be computed.
一个整型值。平均后的最低数量的探针。如果区间内的探针数量是少比nProbesMin,在该区域rMAT的得分将不会被计算。
参数:dMerge
An integer value. The maximum size to merge adjacent probes and categorize them as one region for scores of adjacent probes uniformly above the input threshold.
一个整型值。最大大小,合并相邻的探针和分数统一输入阈值以上相邻探针作为一个区域进行分类。
参数:method
A character string value equal to "score", "pValue" or "FDR". "score" denotes the method of calling enriched regions based sliding widow scores. "pValue" denotes the method of calling enriched regions based on p-values. Method "FDR" uses an FDR procedure to call regions. See Details below.
一个字符串值等于“得分”,“pValue”或“FDR”。 “得分”表示调用滑动寡妇分数为基础的富集区域的方法。 “pValue”表示方法调用基于p-值的富集区域。 “FDR”的方法使用了FDR程序调用的区域。详见下文。
参数:threshold
An integer value. The threshold of rMAT Score to be labeled as an enriched region. For method=1 or 3, the higher the score, the more confident we are about enriched regions. For method=2, the lower the score, the more confident we are about enriched regions.
一个整型值。 rMAT分数的阈值被标记为丰富的区域。对于方法= 1或3,得分越高,越有信心,我们丰富的区域。对于方法2,得分较低的,更加自信,我们即将富集区域。
参数:verbose
A logical value. If verbose is TRUE, progress information would be displayed.
一个逻辑值。如果verbose是TRUE,将显示进度信息。
参数:bedName
This file file includes columns "chromosome rMATScore region pValue" for each probe.
这个文件的文件包括列每个探针“的染色体rMATScore区域pValue”。
Details
详情----------Details----------
For more details on the calculation of the rMAT score, pvalues, etc, please refer to the following paper: Johnson et al. Model-based analysis of tiling-arrays for ChIP-chip. Proc Natl Acad Sci USA (2006) vol. 103 (33) pp. 12457-62
计算得分rMAT,pvalues等更多细节,请参阅下列文件:约翰逊等人。基于模型分析的瓦片阵列芯片的芯片。 PROC基因科学美国(2006年)卷。 103(33)第12457-62
值----------Value----------
The rMAT Score, pValues, and regions. For the regions vector, let 0 denotes the unenriched region. If an enriched region is found, the interval of the region is labeled by a none 0 value. The first region detected is labeled 1 and the next regions are subsequently incremented.
分数的rMAT,pValues,和区域。为区域向量,让0表示unenriched区域。如果发现丰富的区域,该区域的时间间隔是没有0值标记。检测到的第一个区域被标记为1和未来的区域随后递增。
作者(S)----------Author(s)----------
Charles Cheung, <a href="mailto:cykc@interchange.ubc.ca">cykc@interchange.ubc.ca</a> and
Raphael Gottardo, <a href="mailto:rgottard@fhcrc.org">rgottard@fhcrc.org</a>
Arnaud Droit, <a href="mailto:arnaud.droit@crchuq.ulaval.ca">arnaud.droit@crchuq.ulaval.ca</a>
参见----------See Also----------
NormalizeProbes, computeMATScore, callEnrichedRegions for normalizing expression values before computing the rMAT enriched regions.
NormalizeProbes,computeMATScore,callEnrichedRegions标准化之前,表达式的值计算的rMAT富集区域。
举例----------Examples----------
####################################################[################################################## #]
#The data are in inst/doc folder in rMAT package.[数据INST / DOC rMAT包中的文件夹。]
####################################################[################################################## #]
#pwd<-"" #INPUT FILES- BPMAP, ARRAYS, etc.[PWD < - “#输入文件BPMAP,数组等。]
#path<- system.file("doc/Sc03b_MR_v04_10000.bpmap",package="rMAT")[路径< - 。系统(“doc/Sc03b_MR_v04_10000.bpmap”,包=的“rMAT”)]
#bpmapFile<-paste(pwd,path,sep="")[bpmapFile <粘贴(PWD,路径,SEP =“”)]
#pathCEL<- system.file("doc/Swr1WTIP_Short.CEL",package="rMAT")[pathCEL < - 。系统(“doc/Swr1WTIP_Short.CEL”,包=的“rMAT”)]
#arrayFile<-paste(pwd,c(pathCEL),sep="")[粘贴arrayFile <(PWD C(pathCEL),SEP =“”)]
# Show the all the different sequences[在所有不同的序列]
#ReadBPMAPAllSeqHeader(bpmapFile)[ReadBPMAPAllSeqHeader(bpmapFile)]
# create a tiling Set from the corresponding data[建立相应的数据平铺集]
# This will only grep the sequences with Sc[这只会用grep与SC序列]
#ScSet<-BPMAPCelParser(bpmapFile, arrayFile, verbose=FALSE,groupName="Sc") [ScSet <BPMAPCelParser(bpmapFile,arrayFile,详细= FALSE的groupName =“SC”)]
# show the object[显示对象]
#show(ScSet)[展(ScSet)]
# summarize its content[总结其内容]
#summary(ScSet)[摘要(ScSet)]
#ScSetNorm<-NormalizeProbes(ScSet, method="MAT",robust=FALSE, all=FALSE, standard=TRUE, verbose=FALSE)[ScSetNorm的<NormalizeProbes(ScSet,方法=“垫”,健全= FALSE时,所有为FALSE,标准= TRUE时,详细为FALSE)]
#ScScore<- MATScore(ScSetNorm, cName=NULL, dMax=600,nProbesMin=8, dMerge=300,method="score",threshold=5,verbos=TRUE,bedName="MyBedFile") [ScScore < - MATScore(ScSetNorm,CNAME = NULL,DMAX = 600,nProbesMin = 8,dMerge = 300,方法=“得分”,阈值= 5,verbos = TRUE,bedName =“MyBedFile”)]
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注:
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