SIM-package(SIM)
SIM-package()所属R语言包：SIM
Statistical Integration of Microarrays
统计集成的微阵列
译者：生物统计家园网 机器人LoveR
描述----------Description----------
SIM is a statistical model to identify associations between two genomic datasets. Where one is assigned as dependent variable and the other as independent e.g. copy number measurements on several samples versus expression measurements on the same samples. A region of interest can be chosen to run the integrated analysis on either the same region for both dependent and independent datasets or different regions. For each dependent feature a P-value measures the association with the independent data, the contribution of each independent feature is given as Z-scores. The integrated analysis is based on the random-effect model for gene-sets as implemented in gt.
SIM卡是一个统计模型，以确定两个基因数据集之间的关联。其中一个被指定为因变量，作为独立的，如其他复制与表达对同一样品测量的几个样品的数量测量。一个区域的利益，可以选择运行依赖和独立的数据集或不同区域或者同一区域的综合分析。对于每个功能取决于一个P-值测量协会与独立的数据，每个独立的功能贡献给定的Z分数。综合分析的基础上随机效应模型的基因设置在GT实施。
maybe something about annotation?
也许约注释东西？
By default we use method.adjust = "BY" (Benjamini-Yekutieli) for multiple testing correction. This method accounts for dependence between measurements and is more conservative than "BH" (Benjamini-Hochberg). For details on the multiple testing correction methods see p.adjust. We have experienced that a rather low stringency cut-off on the BY-values of 20% allows the detection of associations for data with a low number of samples or a low frequency of abberations. False positives are rarely observed.
默认情况下，我们使用method.adjust = "BY"（Benjamini Yekutieli的）多个测试校正。占该方法测量之间的依赖是比“波黑”（Benjamini Hochberg）保守。对于多个测试校正方法的细节见p.adjust的的。我们已经经历了一个相当低的紧缩切断值的20％，允许协会检测的样品数量低或低频率abberations数据。误报很少看到。
Make sure that the array probes are mapped to the same builds of the genome, and that the chrom.table used by the integrated.analysis is from the same build as well. See sim.update.chrom.table.
确保阵探针映射到相同的基因组构建，并从相同的建立以及，由integrated.analysis chrom.table。看到sim.update.chrom.table。
Details
详情----------Details----------
作者（S）----------Author(s)----------
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes <a href="mailto:R.X.Menezes@lumc.nl">R.X.Menezes@lumc.nl</a>
参考文献----------References----------
Integrated analysis of DNA copy number and gene expression microarray data using gene sets. BMC Bioinformatics, 10, 203-.
A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20, 93-109.
参见----------See Also----------
assemble.data, integrated.analysis, sim.plot.zscore.heatmap, sim.plot.pvals.on.region, sim.plot.pvals.on.genome, tabulate.pvals, tabulate.top.dep.features, tabulate.top.indep.features, impute.nas.by.surrounding, sim.update.chrom.table, sim.plot.overlapping.indep.dep.features, getoverlappingregions
assemble.data，integrated.analysis，sim.plot.zscore.heatmap，sim.plot.pvals.on.region，sim.plot.pvals.on.genome，tabulate.pvals，tabulate.top.dep.features，制表。 top.indep.features，impute.nas.by.surrounding，sim.update.chrom.table，sim.plot.overlapping.indep.dep.features，getoverlappingregions
举例----------Examples----------
#load the datasets and the samples to run the integrated analysis[加载的数据集和样品运行综合分析]
data(expr.data)
data(acgh.data)
data(samples)
#assemble the data[装配数据]
assemble.data(dep.data = acgh.data,
indep.data = expr.data,
dep.ann = colnames(acgh.data)[1:4],
indep.ann = colnames(expr.data)[1:4],
dep.id="ID",
dep.chr = "CHROMOSOME",
dep.pos = "STARTPOS",
dep.symb="Symbol",
indep.id="ID",
indep.chr = "CHROMOSOME",
indep.pos = "STARTPOS",
indep.symb="Symbol",
overwrite = TRUE,
run.name = "chr8q")
#run the integrated analysis[运行综合分析]
integrated.analysis(samples = samples,
input.regions ="8q",
zscores=TRUE,
run.name = "chr8q")
# use functions to plot the results of the integrated analysis[使用函数来绘制的综合分析结果]
#plot the P-values along the genome[沿基因组绘制的P-值]
sim.plot.pvals.on.genome(input.regions = "8q",
significance = c(0.2, 0.05),
adjust.method = "BY",
pdf = FALSE,
run.name = "chr8q")
#plot the P-values along the regions[绘制沿线区域的P-值]
sim.plot.pvals.on.region(input.regions = "8q",
adjust.method="BY",
run.name = "chr8q")
#plot the z-scores in an association heatmap[在协会热图绘制Z-分数]
#plot the zscores in a heatmap[1热图绘制的zscores]
sim.plot.zscore.heatmap(input.regions = "8q",
method="full",
significance=0.2,
z.threshold=3,
show.names.indep=TRUE,
show.names.dep=TRUE,
adjust.method = "BY",
add.plot = "smooth",
smooth.lambda = 2,
pdf = FALSE,
run.name = "chr8q")
sim.plot.zscore.heatmap(input.regions = "8q",
method="full",
significance = 0.05,
z.threshold = 1,
show.names.indep=TRUE,
show.names.dep=FALSE,
adjust.method = "BY",
add.plot = "heatmap",
smooth.lambda = 2,
pdf = FALSE,
run.name = "chr8q")
sim.plot.zscore.heatmap(input.regions = "8q",
method="full",
significance = 0.05,
z.threshold = 1,
show.names.indep=TRUE,
show.names.dep=TRUE,
adjust.method = "BY",
add.plot = "none",
pdf = FALSE,
run.name = "chr8q")
#tabulate the P-values per region (prints to screen)[制表每个区域的P-值（打印屏幕）]
tabulate.pvals(input.regions = "8q",
adjust.method="BY",
bins=c(0.001,0.005,0.01,0.025,0.05,0.075,0.10,0.20,1.0),
run.name = "chr8q")
table.dep <- tabulate.top.dep.features(input.regions="8q",
adjust.method="BY",
method="full",
significance=0.05,
run.name="chr8q")
head(table.dep[["8q"]])
table.indep <- tabulate.top.indep.features(input.regions="8q",
adjust.method="BY",
method="full",
significance= 0.05,
z.threshold=c(-1, 1),
run.name="chr8q")
head(table.indep[["8q"]])
sim.plot.overlapping.indep.dep.features(input.regions="8q",
adjust.method="BY",
significance=0.1,
z.threshold= c(-1,1),
log=TRUE,
summarize="consecutive",
pdf=FALSE,
method="full",
run.name="chr8q")
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。
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发表于 2012-2-26 14:24:01
