MEDIPS.selectSignificants(MEDIPS)
MEDIPS.selectSignificants()所属R语言包：MEDIPS
Selects candidate ROIs that show significant differential methylation between two MEDIPS SETs.
选择候选人的投资回报显着的差甲基两MEDIPS集。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Based on the results matrix returned from the MEDIPS.diffMethyl function, the function selects candidate ROIs that show significant differential methylation between the CONTROL.SET and the TREAT.SET in consideration of the background data included in the INPUT.SET. Filtering for significant frames proceeds in the following order: ROIs that do not contain any data either in the CONTROL.SET nor in the TREAT.SET are neglected first; ROIs associated to p-values > p.value are neglected; ROIs with a CONTROL/TREATMENT ratio < up (or > down, respectively) are neglected; From the INPUT mean rpm distribution, a mean rpm threshold was defined by the quant parameter and all ROIs that have a mean rpm value within the CONTROL.SET (or TREAT.SET, respectively) smaller than the estimated background rpm threshold are discarded; The last filter is again based on the INPUT data. While the latter filter estimates a minimum rpm signal for the CONTROL.SET (or TREAT.SET, respectively) from the total background distribution, we now define that the rpm value from the CONTROL SET (or TREAT.SET, respectively) of a ROI exceeds the local background data of the INPUT.SET by the parameter up. This is, because MeDIP-Seq background data varies along the chromosomes due to varying DNA availability.
矩阵从MEDIPS.diffMethyl函数返回的结果的基础上，功能选择候选人的投资回报，表明之间的CONTROL.SET考虑在INPUT.SET包括背景资料TREAT.SET的显著差甲基化。重大帧的收益按下列顺序进行筛选：投资回报，不包含任何数据在CONTROL.SET也不在TREAT.SET被忽视第一;> p.value被忽视的p值相关联的投资回报;与投资回报<上升（或下降，分别）被忽视;从输入控制/处理率平均转速分布，平均转速阈值的定量参数和所有的投资回报，平均内的CONTROL.SET的转速值（定义或TREAT.SET），分别被丢弃比估计背景转速阈值较小的最后一个过滤器再次输入数据为基础。而后者的过滤器CONTROL.SET（或TREAT.SET），分别从总的背景分布估计的最低转速信号，我们现在定义的RPM值的投资回报率（或TREAT.SET），分别从控制集参数最多，超过当地的INPUT.SET背景资料。这是因为MeDIP-Seq的背景资料以及不同的染色体，由于不同的DNA可用性。
用法----------Usage----------
MEDIPS.selectSignificants(frames = NULL, input = T, control = T, up = 1.333333, down = 0.75, p.value = 0.01,quant = 0.9)
参数----------Arguments----------
参数：frames
specifies the results table derived from the MEDIPS.diffMethyl
指定结果表的MEDIPS.diffMethyl而得
参数：input
default=T; Setting the parameter to TRUE requires that the results table includes a column for summarized rpm values of an INPUT SET. In case, there is no INPUT data available, the input parameter has to be set to a rpm value that will be used as threshold during the subsequent analysis. How to estimate such a threshold without background data is not yet solved by MEDIPS.
默认值= T，参数设置为TRUE要求的结果表包括一个输入设置为总结转值的列。的情况下，有没有输入数据，输入参数被设置为一个rpm值将在随后的分析中使用阈值。如何估计没有阈值的背景资料尚未由MEDIPS解决。
参数：control
can be either TRUE or FALSE; MEDIPS allows for selecting frames that are higher methylated in the CONTROL SET compared to the TREAT SET and vice versa but both approaches have to be perfomed in two independent runs. By setting control=T, MEDIPS selects genomic regions, where the CONTROL SET is higher methylated. By setting control=F, MEDIPS selects genomic regions, where the TREAT SET is higher methylated.
可以是TRUE或FALSE; MEDIPS可以选择在控制高甲基化的组框架相比，“将SET，反之亦然，但两种方法都必须在两个独立运行，俟。通过设置控制= T，MEDIPS选择的基因组区域，控制集是较高的甲基化。通过设置控制楼MEDIPS选择的基因组区域，与TREAT集是较高的甲基化。
参数：up
default=1.333333; defines the lower threshold for the ratio CONTROL/TREAT as well as for the lower ratio for CONTROL/INPUT (if control=T) or TREATMENT/INPUT (if control=F), respectively.
默认值= 1.333333;比例控制/治疗以及较低的比例控制/输入（如果控制= T）或处理/输入（如果控制= F）分别为定义的阈值较低。
参数：down
default=0.75; defines the upper threshold for the ratio: CONTROL/TREATMENT (only if control=F).
默认值= 0.75;只有当定义为比例的上限阈值：控制/处理（控制）。
参数：p.value
default=0.01; defines the threshold for the p-values. One of the p-values derived from the wilcox.test or t.test function has to be <= p.value.
默认值= 0.01;定义为p-值的阈值。来自的wilcox.test或t.test功能的P-值<= p.value。
参数：quant
default=0.9; from the distribution of all summarized INPUT rpm values, MEDIPS calculates the rpm value that represents the quant quantile of the whole INPUT distribution.
默认值= 0.9;从所有汇总输入转速值的分布，MEDIPS计算的转速值，表示整个输入分布的定量位数。
值----------Value----------
参数：chr
the chromosome of the ROI
投资回报率的染色体
参数：start
the start position of the ROI
投资回报率的起始位置
参数：stop
the stop position of the ROI
投资回报率的停止位置
参数：length
the number of genomic bins included in the ROI
基因箱的数量，包括在投资回报率
参数：coupling
the mean coupling factor of the ROI
投资回报率的平均耦合系数
参数：input
the mean reads per million value of the INPUT MEDIPS SET at input (if provided)
平均读取每百万在输入设置的输入MEDIPS的价值（如果提供）
参数：rpm_A
the mean reads per million value for the MEDIPS SET at data1
平均每百万在DATA1 MEDIPS价值读取
参数：rpm_B
the mean reads per million value for the MEDIPS SET at data2
平均每百万在DATA2 MEDIPS价值读取
参数：rms_A
the mean relative mathylation score for the MEDIPS SET at data1
平均在DATA1 MEDIPS相对mathylation得分
参数：rms_B
the mean relative methylation score for the MEDIPS SET at data2
平均在DATA2 MEDIPS相对甲基得分
参数：ams_A
the mean absolute mathylation score for the MEDIPS SET at data1. The ams scores are derived by dividing the mean rms value of the ROI by the mean coupling factor of the ROI before the log2 and interval transformations are performed.
平均为DATA1在设置MEDIPS的绝对mathylation得分。除以平均均方根值的投资回报率的投资回报率平均耦合因素前的log2和执行的时间间隔转换得到本队得分。
参数：ams_B
the mean absolute mathylation score for the MEDIPS SET at data2. The ams scores are derived by dividing the mean rms value of the ROI by the mean coupling factor of the ROI before the log2 and interval transformations are performed.
在DATA2设置MEDIPS的绝对mathylation得分平均为。除以平均均方根值的投资回报率的投资回报率平均耦合因素前的log2和执行的时间间隔转换得到本队得分。
参数：var_A
the variance of the rpm or rms values (please see the parameter select) of the MEDIPS SET at data1
rpm或有效值的MEDIPS（请参阅参数选择）的方差在DATA1
参数：var_B
the variance of the rpm or rms values (please see the parameter select) of the MEDIPS SET at data2
rpm或有效值的MEDIPS（请参阅参数选择）的方差在DATA2
参数：var_co_A
the variance coefficient of the rpm or rms values (please see the parameter select) of the MEDIPS SET at data1
rpm或有效值的变异系数（请参阅参数选择），在DATA1设置MEDIPS
参数：var_co_B
the variance coefficient of the rpm or rms values (please see the parameter select) of the MEDIPS SET at data2
rpm或有效值的变异系数（请参阅参数选择），在DATA2设置MEDIPS
参数：ratio
rpm_A/rpm_B or rms_A/rms_B, respectively (please see the parameter select)
rpm_A / rpm_B或rms_A / rms_B的，分别为（请参阅参数选择）
参数：pvalue.wilcox
the p.value returned by R's wilcox.test function for comparing the rpm values (or rms values, respectively; please see the parameter select) of the MEDIPS SET at data1 and of the MEDIPS SET at data2
p.value返回R的wilcox.test函数的转速值（或有效值，分别比较，请参阅参数选择）在DATA1 MEDIPS的MEDIPS在DATA2
参数：pvalue.ttest
the p.value returned by R's t.test function for comparing the rpm values (or rms values, respectively; please see the parameter select) of the MEDIPS SET at data1 and of the MEDIPS SET at data2
p.value返回R的t.test函数的转速值（或有效值，分别比较，请参阅参数选择）在DATA1 MEDIPS的MEDIPS在DATA2
作者（S）----------Author(s)----------
Lukas Chavez
举例----------Examples----------
library(BSgenome.Hsapiens.UCSC.hg19)
file=system.file("extdata", "MeDIP_hESCs_chr22.txt", package="MEDIPS")
CONTROL.SET = MEDIPS.readAlignedSequences(BSgenome="BSgenome.Hsapiens.UCSC.hg19", file=file)
CONTROL.SET = MEDIPS.genomeVector(data = CONTROL.SET, bin_size = 50, extend = 400)
CONTROL.SET = MEDIPS.getPositions(data = CONTROL.SET, pattern = "CG")
CONTROL.SET = MEDIPS.couplingVector(data = CONTROL.SET, fragmentLength = 700, func = "count")
CONTROL.SET = MEDIPS.calibrationCurve(data = CONTROL.SET)
CONTROL.SET = MEDIPS.normalize(data = CONTROL.SET)
file=system.file("extdata", "MeDIP_DE_chr22.txt", package="MEDIPS")
TREAT.SET = MEDIPS.readAlignedSequences(BSgenome = "BSgenome.Hsapiens.UCSC.hg19", file = file)
TREAT.SET = MEDIPS.genomeVector(data = TREAT.SET, bin_size = 50, extend = 400)
TREAT.SET = MEDIPS.getPositions(data = TREAT.SET, pattern = "CG")
TREAT.SET = MEDIPS.couplingVector(data = TREAT.SET, fragmentLength = 700, func = "count")
TREAT.SET = MEDIPS.calibrationCurve(data = TREAT.SET)
TREAT.SET = MEDIPS.normalize(data = TREAT.SET)
file=system.file("extdata", "Input_StemCells_chr22.txt", package="MEDIPS")
INPUT.SET = MEDIPS.readAlignedSequences(BSgenome = "BSgenome.Hsapiens.UCSC.hg19", file = file)
INPUT.SET = MEDIPS.genomeVector(data = INPUT.SET, bin_size = 50, extend = 400)
diff.methyl = MEDIPS.methylProfiling(data1 = CONTROL.SET, data2= TREAT.SET, input=INPUT.SET, chr="chr22", frame_size=1000, select=1)
diff.methyl.sig=MEDIPS.selectSignificants(diff.methyl)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-2-26 00:38:58
