idenfify-methods(TSSi)
idenfify-methods()所属R语言包：TSSi
Identify methods
识别方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Identify transcription start sites in sequence read count data.
确定转录起始位点序列读取计数数据。
用法----------Usage----------
identifyStartSites(x, threshold=1, tau=c(20, 20), neighbor=TRUE,
fun=subtractExpectation, multicore=TRUE, ...)
参数----------Arguments----------
参数：x
Object of class TssNorm with normalized data.
类TssNorm规范化的数据对象。
参数：threshold
Numeric with the minimal number of reads to be treated as a potential TSS.
数量最少的数字被视为一个潜在的TSS读取。
参数：tau
Numeric vector of length two specifying the \sQuote{tau} parameter of the exponential function for each side of the segment. For the forward strand (“+”), the first and second value refer to the side towards the 5' and 3' end, respectively. In the case that a single value is provided it is applied to both sides.
指定\sQuote{tau}段两侧的指数函数的参数，数值向量的长度为二。 （“+”），为正链的第一个和第二个值是指一边向5和3端分别。在提供一个单一的值的情况下，它适用于双方。
参数：neighbor
Logical whether the background estimates should be iteratively assigned to the predicted TSS during the estimation (default: TRUE).
背景估计的逻辑是否应该被反复分配给在估计预测的技术支持服务（默认：true）。
参数：fun
Function to calculate the expectation for each TSS. For details, see the "details" section.
函数来计算每个TSS的期望。有关详情，请参阅“详细信息”一节。
参数：multicore
Logical whether to use the multicore package to speed up the computation. Has only an effect if the package is available and loaded. For details, see the "details" section.
逻辑是否使用multicore包，以加快计算。如果包是可用的和装，只有效果。有关详情，请参阅“详细信息”一节。
参数：...
Additional arguments passed for the multicore package if used. For details, see the "details" section.
multicore包通过，如果使用额外的参数。有关详情，请参阅“详细信息”一节。
Details
详情----------Details----------
After normalization of the count data, an iterative algorithm is applied for each segment to identify the TSS.
计数数据标准化后，迭代算法为各分部，以确定TSS的应用。
The expected number of false positive counts is initialized with a default value given by the read frequency in the whole data set. The position with the largest counts above is identified as a TSS, if the expected transcription level is at least one read above the expected number of false positive reads. The transcription levels for all TSS are calculated by adding all counts to their nearest neighbor TSS.
预计数量的假阳性计数初始化读取频率在整个数据集的默认值。最大计数以上的位置被确定为一个TSS，如果预期的转录水平是至少有一个以上的预期数量的假阳性读阅读。所有TSS的转录水平计算，加入到他们最近的邻居TSS的所有罪状。
Then, the expected number of false positive reads is updated by convolution with exponential kernels. The decay rates tau in 3' direction and towards the 5'-end can be chosen differently to account for the fact that false positive counts are preferably found in 5' direction of a TSS. This procedure is iterated as long as the set of TSS increases.
然后，假阳性读取预期更新卷积指数内核。 tau3方向和对5-端的衰变率，可以选择不同的帐户为假阳性计数，最好在5一个TSS的方向。这个过程是迭代长集TSS的增加。
In order to distribute the identification step over multiple processor cores, the mclapply function of the multicore package can be used. For this, the multicore package has to be loaded manually before starting the computation, additional parameters are passed via the ... argument, e.g.as normalizeCounts(x, mc.ncores=2). The multicore argument can further be used to temporarily disable the parallel estimation by setting it to FALSE. Pleas note that the identification step is normally very fast and thus using parallel computation here may a minor impact as compared to the normalizeCounts method.
为了分发可用于多个处理器核心的识别步骤，mclapplymulticore包的功能。对于这一点，在开始计算之前，手动加载multicore包，额外的参数传递通过...说法，EGASnormalizeCounts(x, mc.ncores=2)。 multicore参数可以进一步用来暂时禁用并行估计设置FALSE。认罪注意鉴定步骤通常是非常快，因此在这里使用并行计算可能normalizeCounts方法相比，未成年人的影响。
值----------Value----------
An object of class TssResult.
对象类TssResult。
方法----------Methods----------
Identify TSS:
确定TSS的：
signature(x="TssData")
signature(x="TssData")
identifyStartSites(x, ...)
identifyStartSites(x, ...)
作者（S）----------Author(s)----------
Maintainer: Julian Gehring <julian.gehring@fdm.uni-freiburg.de>
参见----------See Also----------
Classes: TssData, TssNorm, TssResult
类别：TssData，TssNorm，TssResult
Methods: segmentizeCounts, normalizeCounts, identifyStartSites, get-methods, plot-methods, asRangedData-methods
方法：segmentizeCounts，normalizeCounts，identifyStartSites，get-methods，plot-methods，asRangedData-methods
Functions: subtract-functions
功能：subtract-functions
Data set: physcoCounts
数据集：physcoCounts
Package: TSSi-package
包装：TSSi-package
举例----------Examples----------
## preceding steps[＃前面的步骤]
example(normalizeCounts)
## identify TSS[＃确定TSS的]
z <- identifyStartSites(yFit)
z
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。
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发表于 2012-2-26 15:48:08
