gsva(GSVA)
gsva()所属R语言包：GSVA
Gene Set Variation Analysis
基因组变异分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates GSVA enrichment scores.
估计GSVA富集分数。
用法----------Usage----------
## S4 method for signature 'ExpressionSet,list'
gsva(expr, gset.idx.list,
abs.ranking=FALSE,
min.sz=1,
max.sz=Inf,
no.bootstraps=0,
bootstrap.percent = .632,
parallel.sz=0,
parallel.type="SOCK",
verbose=TRUE,
mx.diff=TRUE)
## S4 method for signature 'ExpressionSet,GeneSetCollection'
gsva(expr, gset.idx.list,
abs.ranking=FALSE,
min.sz=1,
max.sz=Inf,
no.bootstraps=0,
bootstrap.percent = .632,
parallel.sz=0,
parallel.type="SOCK",
verbose=TRUE,
mx.diff=TRUE)
## S4 method for signature 'matrix,list'
gsva(expr, gset.idx.list,
abs.ranking=FALSE,
min.sz=1,
max.sz=Inf,
no.bootstraps=0,
bootstrap.percent = .632,
parallel.sz=0,
parallel.type="SOCK",
verbose=TRUE,
mx.diff=TRUE)
参数----------Arguments----------
参数：expr
Gene expression data which can be given either as an ExpressionSet object or as a matrix of expression values where rows correspond to genes and columns correspond to samples.
基因表达给定的数据可以作为一个ExpressionSet对象，或作为一个表达式的值，其中行对应的基因，列对应的样本矩阵。
参数：gset.idx.list
Gene sets provided either as a list object or as a GeneSetCollection object.
基因组，无论是作为一个list对象或作为一个GeneSetCollection对象提供。
参数：abs.ranking
Flag to determine whether genes should be ranked according to their sign (flag=FALSE) or by absolute value (flag=TRUE). In the latter, pathways with genes enriched on either extreme (high or low) will be regarded as 'highly' activated.
标志，以确定是否应根据自己的标志（标志= FALSE）或绝对值（标志= TRUE），排名基因。在后者，具有丰富的基因上的两个极端（高或低）的途径，将被视为“高度”激活。
参数：min.sz
Minimum size of the resulting gene sets.
由此产生的基因集的最小尺寸。
参数：max.sz
Maximum size of the resulting gene sets.
由此产生的基因集的最大大小。
参数：no.bootstraps
Number of bootstrap iterations to perform.
引导迭代的数量执行。
参数：bootstrap.percent
.632 is the ideal percent samples bootstrapped.
是0.632％的样本引导的理想。
参数：parallel.sz
Number of processors to use when doing the calculations in parallel. This requires to previously load either the multicore or the snow library. If multicore is loaded and this argument is left with its default value (parallel.sz=0) then it will use all available core processors unless we set this argument with a smaller number. If snow is loaded then we must set this argument to a positive integer number that specifies the number of processors to employ in the parallel calculation.
做并行计算时使用的处理器数目。这需要以前加载multicore或snow库。 multicore如果加载，这一论点是与它的默认值（parallel.sz=0），然后将使用所有可用的核心处理器，除非我们设置一个较小的数字，这种说法离开。如果snow被加载，那么我们必须将此参数设置为正整数，指定在并行计算处理器的数量，聘请。
参数：parallel.type
Type of cluster architecture when using snow.
聚类架构的类型时使用snow。
参数：verbose
Gives information about each calculation step. Default: FALSE.
给每个计算步骤的信息。默认：FALSE。
参数：mx.diff
Offers two approaches to calculate the enrichment statistic (ES) from the KS random walk statistic. mx.diff=FALSE: ES is calculated as the maximum distance of the random walk from 0. mx.diff=TRUE (default): ES is calculated as the magnitude difference between the largest positive and negative random walk deviations.
提供两种方法来计算富集统计（ES）的KS随机游动的统计。 mx.diff=FALSE：ES是为0的随机游动的最大距离计算。 mx.diff=TRUE（默认）：ES作为最大的正面和负面的随机游动的偏差之间的幅度差计算。
Details
详情----------Details----------
GSVA assesses the relative enrichment of gene sets across samples using a non-parametric approach. Conceptually, GSVA transforms a p-gene by n-sample gene expression matrix into a g-geneset by n-sample pathway enrichment matrix. This facilitates many forms of statistical analysis in the 'space' of pathways rather than genes, providing a higher level of interpretability.
GSVA评估使用非参数方法的样本基因组之间的相对富集。从概念上讲，GSVA转换由一个P-基因N-样本的基因表达到G-geneset N-样品通路富集矩阵的矩阵。这有利于在“空间”的途径，而不是基因多种形式的统计分析，提供更高水平的解释性。
The gsva() function first maps the identifiers in the gene sets to the identifiers in the input expression data leading to a filtered collection of gene sets. This collection can be further filtered to require a minimun and/or maximum size of the gene sets for which we want to calculate GSVA enrichment scores, by using the arguments min.sz and max.sz.
gsva()第一个图功能基因标识设置在输入表达式过滤收集到基因组数据的标识符。此集合可以进一步筛选需要的最低限度的和/或最大规模的基因组，我们要计算GSVA富集分数，通过使用参数min.sz和max.sz。
值----------Value----------
A gene-set by sample matrix of GSVA enrichment scores.
A基因样本矩阵GSVA富集分数。
作者（S）----------Author(s)----------
J. Guinney
参考文献----------References----------
GSVA: Gene Set Variation Analysis, submitted
参见----------See Also----------
filterGeneSets computeGeneSetsOverlap
filterGeneSetscomputeGeneSetsOverlap
举例----------Examples----------
library(limma)
p <- 10 ## number of genes[＃数量的基因]
n <- 30 ## number of samples[＃样品的数量]
nGrp1 <- 15 ## number of samples in group 1[＃1组样本数]
nGrp2 <- n - nGrp1 ## number of samples in group 2[＃2组样品的数量]
## consider three disjoint gene sets[＃考虑三个不相交的基因集]
geneSets <- list(set1=paste("g", 1:3, sep=""),
set2=paste("g", 4:6, sep=""),
set3=paste("g", 7:10, sep=""))
## sample data from a normal distribution with mean 0 and st.dev. 1[从正态分布，平均0和st.dev的样本数据。 1]
y <- matrix(rnorm(n*p), nrow=p, ncol=n,
dimnames=list(paste("g", 1:p, sep="") , paste("s", 1:n, sep="")))
## genes in set1 are expressed at higher levels in the last 10 samples[在较高的水平，在过去的10个样品在SET1＃基因的表达]
y[geneSets$set1, (nGrp1+1):n] <- y[geneSets$set1, (nGrp1+1):n] + 2
## build design matrix[＃建立设计矩阵]
design <- cbind(sampleGroup1=1, sampleGroup2vs1=c(rep(0, nGrp1), rep(1, nGrp2)))
## fit linear model[＃适合线性模型]
fit <- lmFit(y, design)
## estimate moderated t-statistics[＃估计放缓的t-统计]
fit <- eBayes(fit)
## genes in set1 are differentially expressed[在SET1＃基因的差异表达]
topTable(fit, coef="sampleGroup2vs1")
## estimate GSVA enrichment scores for the three sets[＃估计GSVA三套富集分数]
gsva_es <- gsva(y, geneSets, mx.diff=1)$es.obs
## fit the same linear model now to the GSVA enrichment scores[＃适合现在相同的线性模型的GSVA富集分数]
fit <- lmFit(gsva_es, design)
## estimate moderated t-statistics[＃估计放缓的t-统计]
fit <- eBayes(fit)
## set1 is differentially expressed[＃set1的差异表达]
topTable(fit, coef="sampleGroup2vs1")
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 2012-2-25 21:20:17
