estimate.mean.fraglen(chipseq)
estimate.mean.fraglen()所属R语言包：chipseq
Estimate summaries of the distribution of fragment lengths in a short-read experiment. The methods are designed for ChIP-Seq experiments and may not work well in data without peaks.
估计在短期读实验片段长度分布的摘要。芯片SEQ实验设计的方法和无峰的数据可能无法正常工作。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
estimate.mean.fraglen implements three methods for estimating mean fragment length. The other functions are related helper functions implementing various methods, but may be useful by themselves for diagnostic purposes. Many of these operations are potentially slow.
estimate.mean.fraglen实现三个估计平均片段长度的方法。其他的功能相关的辅助功能，实施各种方法，但可能对自己有用的诊断目的。许多这些行动有可能慢。
sparse.density is intended to be similar to density, but returns the results in a run-length encoded form. This is useful when long stretches of the range of the data have zero density.
sparse.density意类似density，但在运行长度编码形式返回结果。数据范围很长一段时具有零的密度，这是非常有用的。
用法----------Usage----------
estimate.mean.fraglen(x, method = c("SISSR", "coverage", "correlation"),
...)
basesCovered(x, shift = seq(5, 300, 5), seqLen = 100, verbose = FALSE)
densityCorr(x, shift = seq(0, 500, 5), center = FALSE,
width = seqLen *2L, seqLen=100L, ...)
sparse.density(x, width = 50, kernel = "epanechnikov",
from = start(rix)[1] - 10L,
to = end(rix)[length(rix)] + 10L)
参数----------Arguments----------
参数：x
For estimate.mean.fraglen, typically an AlignedRead or a GRanges object. Also supported but deprecated, as they do not have formal strand information: RangedData (with a "strand" column), or a list-like object with elements "+" and "-" representing locations of reads aligned to positive and negative strands (the values should be integers denoting the location where the first sequenced base matched.) Supported (but again, deprecated) list types include: RangesList, IntegerList or an ordinary R list. Also a GenomeData where the per-chromosome elements are one of the above list types. For basesCovered, and densityCorr, a list with elements "+" and "-" representing locations of reads aligned to positive and negative strands (the values should be integers denoting the location where the first sequenced base matched.) For sparse.density, a numeric or integer vector for which density is to be computed.
estimate.mean.fraglen，通常AlignedRead的或农庄对象的。也支持，但是不赞成，因为他们没有正式信息股：RangedData（“链”列），或"+"和"-"代表读取的位置对齐的元素列表对象正面和负面的股（值应该是整数，表示在第一测序碱基相匹配的位置）支持（但是，过时的）列表类型包括：RangesList，IntegerList还是一个普通的R列表。也有GenomeData每个染色体元素是上述列表类型之一。对于basesCovered，densityCorr，列表中的元素"+"和"-"代表的地点读取正面和负面的股（对齐值应为整数，表示位置第一测序碱基相匹配。sparse.density），密度为要计算的数字或整数向量。
参数：method
Character string giving method to be used. method = "SISSR" implements the method described in Jothi et al (see References below). method = "correlation" implements the method described in Kharchenko et al (see References below), where the idea is to compute the density of tag start positions separately for each strand, and then determine the amount of shift that maximizes the correlation between these two densities. method = "coverage" computes the optimal shift for which the number of bases covered by any read is minimized.
字符串提供的方法可以使用。 method = "SISSR"实现Jothi等的方法（见下面的参考资料）。 method = "correlation"实现Kharchenko等（见下面的参考资料），这里的想法是标签起始位置的密度分别计算每个链中所描述的方法，然后确定偏移量，最大限度地提高这两者之间的相关性密度。 method = "coverage"计算最佳转变为碱基覆盖任何读人数减至最低。
参数：shift
Integer vector giving amount of shifts to be tried when optimizing. The current algorithm simply evaluates all supplied values and reports the one giving minimum coverage or maximum correlation.
整数向量给量的变化，将尝试优化。目前的算法简单计算提供的所有值和报告给予最低保障或最大相关。
参数：seqLen
For the "coverage" method, the assumed length of each read for computing the coverage. Typically the read length. This is added to the shift estimated by "coverage" and "correlation" to come up with the actual fragment length.
"coverage"方法，假设读每个计算覆盖长度。通常情况下，读取长度。这是添加到"coverage"和"correlation"要拿出实际的片段长度估计移位。
参数：verbose
Logical specifying whether progress information should be printed during execution.
进度信息是否应印刷在执行过程中的逻辑指定。
参数：center
For the "correlation" method, whether the calculations should incorporate centering by the mean density. The default is not to do so; as the density is zero over most of the genome, this slightly improves efficiency at negligible loss in accuracy.
"correlation"方法，是否应纳入计算的平均密度为中心的。默认是不这样做的密度超过了基因组的大部分是零，这稍微提高精度可以忽略不计损失的效率。
参数：width
half-bandwidth used in the computation. This needs to be specified as an integer, data-driven rules are not supported.
在计算中使用的半带宽。这需要指定为一个整数，不支持数据驱动的规则。
参数：kernel
A character string giving the density kernel.
一个字符串，使密度内核。
参数：from, to
specifies range over which the density is to be computed.
指定范围内的密度要计算。
参数：...
Extra arguments, passed on as appropriate to other functions.
额外的参数，通过适当的其他职能。
Details
详情----------Details----------
These functions are typically used in conjunction with gdapply.
这些功能通常用于一起gdapply。
For the correlation method, the range over which densities are computed only cover the range of reads; that is, the beginning and end of chromosomes are excluded.
的相关方法，范围，密度的计算只包括读取范围内，染色体的开头和结尾被排除。
值----------Value----------
estimate.mean.fraglen gives an estimate of the mean fragment length.
estimate.mean.fraglen给出了一个估计的平均片段长度。
basesCovered and densityCorr give a vector of the corresponding objective function evaluated at the supplied values of shift.
basesCovered和densityCorr给予了相应的目标函数向量评估所提供的值shift。
sparse.density returns an object of class "Rle".
sparse.density类"Rle"返回一个对象。
作者（S）----------Author(s)----------
Deepayan Sarkar, Michael Lawrence
参考文献----------References----------
identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Research, 36:5221–31, 2008.
analysis of ChIP experiments for DNA-binding proteins. Nature Biotechnology, 26:1351–1359, 2008.
参见----------See Also----------
gdapply
gdapply
举例----------Examples----------
data(cstest)
estimate.mean.fraglen(cstest[["ctcf"]], method = "coverage")
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 2012-2-25 14:54:40
