summarize.peaks(BayesPeak)
summarize.peaks()所属R语言包：BayesPeak
Summarize Peaks
总结峰
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Summarize Peaks - Combine the potentially enriched bins found by BayesPeak into contiguous peaks, and associate each with a posterior probability. summarise.peaks is an alias for summarize.peaks
总结群山 - 结合潜在的丰富由BayesPeak到相邻峰箱，并关联每一个后验概率。 summarise.peaks是summarize.peaks的别名
用法----------Usage----------
summarize.peaks(x, threshold = 0.5, method = c("lowerbound", "max"), exclude.jobs = NULL)
summarise.peaks(x, threshold = 0.5, method = c("lowerbound", "max"), exclude.jobs = NULL)
参数----------Arguments----------
参数：x
Raw output from the function bayespeak.
从原料输出功能bayespeak。
参数：threshold
Numeric vector. threshold must have length equal to either 1 or nrow(x$QC) (i.e. the number of jobs).
数字向量。 threshold长度必须等于1或nrow(x$QC)（即就业人数）。
If threshold is of length 1, then for each job, all bins with a posterior probability (PP) lower than threshold will be discarded before summarising.
threshold如果是长度为1，然后为每个作业，后验概率（PP）的所有箱比threshold将丢弃前总结性较低。
If threshold is of length nrow(x$QC), then jobs are taken to have separate thresholds - in other words, bins in job i will be discarded if they have a PP less than threshold[i]. Note that this behaviour is irrespective of how many jobs are excluded (see the exclude.jobs argument below) - excluded jobs are still assigned a PP threshold, which is essentially ignored.
如果threshold长度nrow(x$QC)，然后工作采取有单独的阈值 - 换句话说，我在工作箱将被丢弃，如果他们有一个PP大于阈值[I]。请注意，这种行为是不论有多少职位被排除在外（见的下面exclude.jobs参数） - 排除工作仍分配一个PP的阈值，基本上是忽略。
参数：method
The method used to combine the posterior probabilities of multiple peaks. Current methods are:
所采用的方法相结合的多峰的后验概率。目前的方法是：
lowerbound: A lower bound is found for the posterior probability of the region containing a peak. In contiguous regions of moderately high probability, this method should report a fairer value than method = max. Suppose we have a set of n non-intersecting calls within our region, with posterior probabilities p_1 to p_n respectively of containing peaks. Then the probability of there being a peak in this region is at least 1 - (1-p_1)...(1-p_n). We maximise this over all possible sets of non-intersecting calls within the region. (Usually, this will simply be a choice between exclusively using the offset or the non-offset analyses.)
lowerbound：下界被发现含有一个峰值后该区域的概率。在适度的高概率的连续区域，此方法应报告比method = max公平价值。假设我们与了n不相交的呼吁在本区域的后验概率集，p_1p_n分别含有峰。然后有在这一区域的峰值的概率至少的1 - (1-p_1)...(1-p_n)。我们最大限度地提高了区域内的非交叉调用的所有可能的套。 （通常情况下，这只会是一个专门使用的偏移或偏移分析之间的选择。）
max: Combined region has probability equal to the maximum posterior probability over all the peaks it contains.
max：结合区域拥有超过它所包含的所有山峰的最大后验概率的概率相等。
参数：exclude.jobs
A vector of integers, denoting jobs to be excluded from later analysis. Alternatively, a logical vector (to be passed through which()).
一个整数，表示工作的向量，被排除在以后的分析。另外，逻辑向量（可通过which()）。
值----------Value----------
A RangedData object corresponding to the peaks called - each range has an associated PP (Posterior Probability) value.
峰对应一个RangedData对象称为 - 每个范围有关联的PP值（后验概率）。
作者（S）----------Author(s)----------
Jonathan Cairns
参见----------See Also----------
bayespeak.
bayespeak。
举例----------Examples----------
dir <- system.file("extdata", package="BayesPeak")
treatment <- file.path(dir, "H3K4me3reduced.bed")
input <- file.path(dir, "Inputreduced.bed")
##look at specific region 92-95Mb on chromosome 16[＃看看16号染色体上的特定区域的92-95MB]
##(we've used half the number of iterations here to reduce the time this example takes)[＃（我们已经使用一半数量的迭代，以减少这个例子所需要的时间）]
raw.output <- bayespeak(treatment, input, chr = "chr16", start = 9.2E7, end = 9.5E7, iterations = 5000L, use.multicore = TRUE)
output <- summarize.peaks(raw.output)
output
##higher threshold[＃阈值较高]
output.ht <- summarize.peaks(raw.output, threshold = 0.9)
output.ht
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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。
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发表于 2012-2-25 12:31:35
