ssdCoverage(htSeqTools)
ssdCoverage()所属R语言包：htSeqTools
Standardized SD of the genomic coverage
标准化的SD基因组覆盖
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute variability of the genomic coverage, measured as standardized SD per thousand sequences (see details). For instance, this can measure how pronounced are the peaks in a ChIP-Seq experiments, which can serve as a quality control to detect inefficient immuno-precipitation.
计算变异基因的覆盖测量，为每千序列标准化的SD（见详情）。例如，这可以衡量明显是在一个芯片的SEQ实验，它可以作为一种质量控制，检测效率低下的免疫沉淀的高峰。
用法----------Usage----------
ssdCoverage(x, mc.cores=1)
参数----------Arguments----------
参数：x
Object with ranges indicating the start and end of each read. Currently, x can be of class RangedDataList, RangedData and IRangesList.
对象与范围表示每次读取的开始和结束。目前，x可以类RangedDataList的，RangedData和IRangesList。
参数：mc.cores
Set mc.cores to a value greater than 1 to perform computations in parallel, using package mclapply.
设置的值大于1，执行并行计算mc.cores包mclapply。
Details
详情----------Details----------
ssdCoverage first computes the coverage for each sample and computes the standard deviation (SD) of the coverage. However, SD is not an appropriate measure of coverage unevenness, as its expected value is proportional to sqrt(n), where n is the number of reads (this can be seen with simple algebra).
ssdCoverage首先计算每个样品的覆盖范围和计算标准偏差（SD）的覆盖。然而，SD是不是一个适当的措施覆盖面不平衡，其预期值是成正比的sqrt（N），其中n是读取（这可以用简单的代数）。
ssdCoverage therefore reports 1000*SD/sqrt(n), which can be interpreted as the standardized SD per thousand sequences.
ssdCoverage因此报告1000 * SD / SQRT（N），每千序列可作为标准化的SD解释。
值----------Value----------
Numeric vector with coefficients of variation.
变异系数的数值向量。
方法----------Methods----------
A single coefficient of variation is returned, as a weighted average of the coefficients of variation for each chromosome (weighted according to the chromosome length).
返回一个单一的变异系数，加权平均每个染色体的变异系数（根据染色体长度加权）。
The method for IRangesList is used on ranges(x).
的IRangesList方法使用ranges(x)。
A vector with coefficients of variation for each element in x are returned, by repeatedly calling the method for RangedData objects. Use mc.cores to speed up computations with mclapply, but be careful as this requires more memory.
x每个元素的变异系数向量，通过反复调用RangedData对象的方法返回。使用mc.cores加快mclapply计算，但要小心，因为这需要更多的内存。
举例----------Examples----------
set.seed(1)
#Simulate IP data[模拟IP数据]
peak1 <- round(rnorm(500,100,10))
peak1 <- RangedData(IRanges(peak1,peak1+38),space='chr1')
peak2 <- round(rnorm(500,200,10))
peak2 <- RangedData(IRanges(peak2,peak2+38),space='chr1')
ip <- rbind(peak1,peak2)
#Generate uniform background[生成统一的背景]
bg <- runif(1000,1,300)
bg <- RangedData(IRanges(bg,bg+38),space='chr1')
rdl <- RangedDataList(ip,bg)
ssdCoverage(rdl)
giniCoverage(rdl)
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-2-25 22:04:46
