SAMseq(samr)
SAMseq()所属R语言包：samr
Significance analysis of sequencing data - simple user interface
分析测序数据的意义 - 简单的用户界面
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Correlates a large number of features (eg. genes) with an outcome variable, such as a group indicator, quantitative variable or survival time. This is a simple user interface for the samr function applied to sequencing data. For array data applications, see the function SAM.
相互关联，大量的功能（如基因）与结果的变量，如成组指示符，定量变量或生存时间。为SAMR应用于测序数据的函数，这是一个简单的用户界面。对于数组数据的应用程序，请参阅SAM的功能。
用法----------Usage----------
SAMseq(x, y, censoring.status = NULL,
resp.type = c("Quantitative", "Two class unpaired",
"Survival", "Multiclass", "Two class paired"),
geneid = NULL, genenames = NULL, nperms = 100,
random.seed = NULL, nresamp = 20, fdr.output = 0.20)
参数----------Arguments----------
参数：x
Feature matrix: p (number of features) by n (number of samples), one observation per column (missing values allowed)
特征矩阵：对（功能数）由n（样品数），每列的一个观测值（允许缺失值）
参数：y
n-vector of outcome measurements
n维向量结果测量
参数：censoring.status
n-vector of censoring censoring.status (1=died or event occurred, 0=survived, or event was censored), needed for a censored survival outcome
n维向量的审查censoring.status（1 =死亡或事件发生，0 =活了下来，审查或事件），需要审查的生存结果
参数：resp.type
Problem type: "Quantitative" for a continuous parameter; "Two class unpaired" for two classes with unpaired observations; "Survival" for censored survival outcome; "Multiclass": more than 2 groups; "Two class paired" for two classes with paired observations.
问题类型：“量化”的连续参数“两课不成对的”两个类具有未配对的观测，“求生存”为删失的生存结果，“多类”：2组以上，“两班配对”两个类配对观测。
参数：geneid
Optional character vector of geneids for output.
可选字符的矢量为输出的geneids的。
参数：genenames
Optional character vector of genenames for output.
可选字符的矢量为输出的genenames的。
参数：nperms
Number of permutations used to estimate false discovery rates
估计假发现率的排列数
参数：random.seed
Optional initial seed for random number generator (integer)
可选的初始种子的随机数发生器（整数）
参数：nresamp
Number of resamples used to construct test statistic. Default 20.
用于构造检验统计量的重新采样数。默认为20。
参数：fdr.output
(Approximate) False Discovery Rate cutoff for output in significant genes table
（概约）在重要基因表输出的假发现率阈值
Details
详细信息----------Details----------
This is a simple, user-friendly interface to the samr package used on sequencing data. It automatically disables arguments/features that do not apply to sequencing data. It calls samr, samr.compute.delta.table and samr.compute.siggenes.table. samr detects differential expression for microarray data, and sequencing data, and other data with a large number of features. samr is the R package that is called by the "official" SAM Excel Addin. The format of the response vector y and the calling sequence is illustrated in the examples below. A more complete description is given in the SAM manual
这是一个简单，友好的用户界面测序数据的SAMR包的使用。它会自动禁用参数/功能并不适用于测序数据。它要求SAMR，samr.compute.delta.table的和samr.compute.siggenes.table。 SAMR检测差异表达微阵列数据和序列数据，其他数据与大量的功能。 SAMR是R包，被称为“正式的”SAM的Excel加载项。在下面的实施例中示出的格式的响应矢量y和调用序列。更完整的说明中给出的SAM手册
值----------Value----------
A list with components <table summary="R valueblock"> <tr valign="top"><td>samr.obj</td> <td> Output of samr. See documentation for samr for details </td></tr> <tr valign="top"><td>siggenes.table</td> <td> Table of significant genes, output of samr.compute.siggenes.table. This has components: genes.up—matrix of significant genes having positive correlation with the outcome and genes.lo—matrix of significant genes having negative correlation with the outcome. For survival data, genes.up are those genes having positive correlation with risk- that is, increased expression corresponds to higher risk (shorter survival) genes.lo are those whose increased expression corresponds to lower risk (longer survival).</td></tr> <tr valign="top"><td>delta.table</td> <td> Output of samr.compute.delta.table.</td></tr> <tr valign="top"><td>del</td> <td> Value of delta (distance from 45 degree line in SAM plot) for used for creating delta.table and siggenes.table. Changing the input value fdr.output will change the resulting del.</td></tr> <tr valign="top"><td>call</td> <td> The calling sequence</td></tr> </table>
组件列表<table summary="R valueblock"> <tr valign="top"> <TD>samr.obj</ TD>的SAMR <TD>输出。为SAMR的详细信息，请参阅文档</ TD> </ TR> <tr valign="top"> <TD> siggenes.table </ TD> <TD>表显着的基因，输出samr.compute.siggenes。表。这部分组成：genes.up矩阵与的结果和genes.lo矩阵的重要基因呈负相关的结果具有正相关性的重要基因。为了生存数据，genes.up这些基因具有正相关性表达增加风险，也就是说，对应于较高的风险（生存期短）genes.lo是那些表达增加相对应，以降低风险（存活时间较长）。</ TD> </ TR> <tr valign="top"> <TD>delta.table </ TD> <TD>的samr.compute.delta.table输出。</ TD> </ TR> <TR VALIGN =“顶“<TD> del</ TD> <TD> Delta值（SAM图从45度直线距离）用于创建delta.table siggenes.table。更改的输入值fdr.output的改变DEL。</ TD> </ TR> <tr valign="top"> <TD>call </ TD> <TD>的调用序列</ TD > </ TR> </ TABLE>
（作者）----------Author(s)----------
Jun Li and Balasubrimanian Narasimhan and Robert Tibshirani
参考文献----------References----------
Significance analysis of microarrays applied to the ionizing radiation response PNAS 2001 98: 5116-5121, (Apr 24). http://www-stat.stanford.edu/~tibs/SAM
Li, Jun and Tibshirani, R. (2011). Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. To appear, Statistical Methods in Medical Research.
实例----------Examples----------
######### two class unpaired comparison[＃＃＃＃＃＃＃＃2类未配对的比较]
set.seed(100)
mu <- matrix(100, 1000, 20)
mu[1:100, 11:20] <- 200
mu <- scale(mu, center=FALSE, scale=runif(20, 0.5, 1.5))
x <- matrix(rpois(length(mu), mu), 1000, 20)
y <- c(rep(1, 10), rep(2, 10))
samfit <- SAMseq(x, y, resp.type = "Two class unpaired")
# examine significant gene list[检查显着的基因列表]
print(samfit)
# plot results[图结果]
plot(samfit)
######### two class paired comparison[＃＃＃＃＃＃＃＃两个类配对比较]
set.seed(100)
mu <- matrix(100, 1000, 20)
mu[1:100, 11:20] <- 200
mu <- scale(mu, center=FALSE, scale=runif(20, 0.5, 1.5))
x <- matrix(rpois(length(mu), mu), 1000, 20)
y <- c(-(1:10), 1:10)
samfit <- SAMseq(x, y, resp.type = "Two class paired")
# examine significant gene list[检查显着的基因列表]
print(samfit)
# plot results[图结果]
plot(samfit)
######### Multiclass comparison[＃＃＃＃＃＃＃＃多类型的比较]
set.seed(100)
mu <- matrix(100, 1000, 20)
mu[1:20, 1:5] <- 120
mu[21:50, 6:10] <- 80
mu[51:70, 11:15] <- 150
mu[71:100, 16:20] <- 60
mu <- scale(mu, center=FALSE, scale=runif(20, 0.5, 1.5))
x <- matrix(rpois(length(mu), mu), 1000, 20)
y <- c(rep(1:4, rep(5, 4)))
samfit <- SAMseq(x, y, resp.type = "Multiclass")
# examine significant gene list[检查显着的基因列表]
print(samfit)
# plot results[图结果]
plot(samfit)
######### Quantitative comparison[＃＃＃＃＃＃＃＃定量比较]
set.seed(100)
mu <- matrix(100, 1000, 20)
y <- runif(20, 1, 3)
mu[1 : 100, ] <- matrix(rep(100 * y, 100), ncol=20, byrow=TRUE)
mu <- scale(mu, center=FALSE, scale=runif(20, 0.5, 1.5))
x <- matrix(rpois(length(mu), mu), 1000, 20)
samfit <- SAMseq(x, y, resp.type = "Quantitative")
# examine significant gene list[检查显着的基因列表]
print(samfit)
# plot results[图结果]
plot(samfit)
######### Survival comparison[＃＃＃＃＃＃＃＃生存比较]
set.seed(100)
mu <- matrix(100, 1000, 20)
y <- runif(20, 1, 3)
mu[1 : 100, ] <- matrix(rep(100 * y, 100), ncol=20, byrow=TRUE)
mu <- scale(mu, center=FALSE, scale=runif(20, 0.5, 1.5))
x <- matrix(rpois(length(mu), mu), 1000, 20)
y <- y + runif(20, -0.5, 0.5)
censoring.status <- as.numeric(y < 2.3)
y[y >= 2.3] <- 2.3
samfit <- SAMseq(x, y, censoring.status = censoring.status,
resp.type = "Survival")
# examine significant gene list[检查显着的基因列表]
print(samfit)
# plot results[图结果]
plot(samfit)
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 2012-9-29 22:12:36
