lung73(scaleboot)
lung73()所属R语言包：scaleboot
Clustering of 73 Lung Tumors
聚类的73个肺部肿瘤
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Bootstrapping hierarchical clustering of the DNA microarray data set of 73 lung tissue samples each containing 916 observed genes.
引导73肺组织样本，每个含916观察到的基因的DNA微阵列数据集的层次聚类。
用法----------Usage----------
data(lung73)
lung73.pvclust
lung73.sb
格式----------Format----------
lung73.pvclust is an object of class "pvclust" defined in pvclust of Suzuki and Shimodaira (2006).
lung73.pvclust是一个对象类"pvclust"中定义的pvclust的铃木和Shimodaira（2006年）。
lung73.sb is an object of class "scalebootv" of length 72.
lung73.sb是一个对象类"scalebootv"长度为72。
Details
详细信息----------Details----------
The microarray dataset of Garber et al. (2001) is reanalyzed in Suzuki and Shimodaira (2006), and is found in data(lung) of the pvclust package. We reanalyze it, again, by the script shown in Examples. The result of pvclust is stored in lung73.pvclust, and model fitting to bootstrap probabilities by the scaleboot package is stored in lung73.sb. The AU p-values obtained by using the scaleboot package are sometimes very different from those obtained by the pvclust package. For example, pvclust with default parameter value gave AU p-value of 0.70 for Edge-67, but the sbfit gives AU p-value (named "k.3") of 0.95 for the same edge. Note that the raw bootstrap probability (i.e., the ordinary bootstrap probability with scale=1) is 0.04.
加伯等人的微阵列数据集。 （2001）重新分析在铃木和Shimodaira（2006年），在data(lung)的pvclust包。我们再分析它，再次，通过实施例中所示的脚本。 pvclust是存储在lung73.pvclust，引导概率模型拟合的scaleboot包存储在lung73.sb“的。非盟的p值有时是非常不同的使用scaleboot包pvclust包。例如，pvclust默认的参数值给AU-67的边缘为0.70 p值，但sbfit给出了AU p值（名为“K.3”）的相同边缘为0.95 。需要注意的是原料的自举的概率（即，普通的自举带刻度= 1的概率）为0.04。
The AU p-values for all nodes are shown by the summary method, <pre> > summary(lung73.sb[60:70]) Corrected P-values (percent): raw k.1 k.2 k.3 model aic 60 20.21 (0.40) 20.29 (0.18) 71.40 (0.20) 78.98 (0.44) sing.3 80.46 61 58.45 (0.49) 55.08 (0.17) 63.15 (0.24) 56.34 (0.38) poly.3 575.85 62 95.68 (0.20) 95.92 (0.10) 98.64 (0.10) 98.61 (0.12) poly.3 -12.01 63 58.31 (0.49) 57.30 (0.17) 82.09 (0.20) 81.74 (0.28) poly.3 20.74 64 15.81 (0.36) 15.58 (0.16) 75.36 (0.21) 84.86 (0.37) sing.3 71.47 65 2.96 (0.17) 2.80 (0.07) 76.73 (0.51) 94.88 (0.20) sing.3 33.34 66 15.75 (0.36) 15.92 (0.16) 78.02 (0.20) 87.98 (0.29) sing.3 7.30 67 3.63 (0.19) 3.31 (0.07) 77.02 (0.47) 95.10 (0.17) sing.3 25.11 68 26.20 (0.44) 27.06 (0.17) 83.06 (0.18) 84.90 (0.27) poly.3 8.67 69 29.49 (0.46) 29.65 (0.17) 75.37 (0.22) 75.83 (0.34) poly.3 -14.09 70 28.31 (0.45) 29.04 (0.19) 76.62 (0.17) 81.54 (0.37) sing.3 0.99 </pre>
非盟对所有节点的值summary方法，<PRE>总结（lung73.sb [60:70]）校正P值（％）：生K.1 K.2 sing.3 80.46 61 58.45 K.3模型AIC 60 20.21（0.40）20.29（0.18）71.40（0.20）78.98（0.44）（0.49）55.08（0.17）63.15（0.24）56.34（0.38）poly.3 575.85 62 95.68（ 0.20）95.92（0.10）98.64（0.10）98.61（0.12）（0.49）poly.3 -12.01 63 58.31 57.30（0.17）82.09（0.20）81.74（0.28）（0.36）poly.3 20.74 64 15.81 15.58（0.16）75.36 （0.21）84.86（0.37）sing.3 71.47 65 2.96（0.17）2.80（0.07）76.73（0.51）94.88（0.20）sing.3 33.34 66 15.75（0.36）15.92（0.16）78.02（0.20）87.98（0.29）唱0.3 7.30 67 3.63（0.19）3.31（0.07）77.02（0.47）95.10（0.17）sing.3 25.11 68 26.20（0.44）27.06（0.17）83.06（0.18）84.90（0.27）poly.3 8.67 69 29.49（0.46） 29.65（0.17）75.37（0.22）75.83（0.34）poly.3 -14.09 70 28.31 29.04（0.45）（0.19）76.62（0.17）81.54（0.37）sing.3 0.99 </ pre>
Shown above are four types of p-values as well as selected model and AIC values. "raw" is the ordinary bootstrap probability, "k.1" is equivalent to "raw" but calculated from the multiscale bootstrap, "k.2" is equivalent to the third-order AU p-value of CONSEL, and finally "k.3" is an improved version of AU p-value. By default, we use "k.3" when copying back the p-values to an object of class "pvclust".
上面显示的是四种类型的p-值以及选定的模型和AIC值。 “原始”是普通的自举概率，“K.1”是相当于“原始”，但计算出的多尺度引导，“K.2”是相当于三阶AU的p值CONSEL，最后的“K 0.3“是一个改进版的AU p值。默认情况下，我们使用“K.3”的复印时，后面的p值类"pvclust"的对象。
See Examples below for details.
的详细信息，请参阅下面的示例。
注意----------Note----------
The microarray dataset is not included in data(lung73), but it is found in data(lung) of the pvclust package.
微阵列数据集不包括在data(lung73)，但它在data(lung)的pvclust包。
源----------Source----------
Garber, M. E. et al. (2001) Diversity of gene expression in adenocarcinoma of the lung, Proceedings of the National Academy of Sciences, 98, 13784-13789 (dataset is available from http://genome-www.stanford.edu/lung_cancer/adeno/).
加伯，M. E.等。 （2001）在肺腺癌的基因表达差异的美国国家科学院院士，98，13784-13789（数据集是可从http://genome-www.stanford.edu/lung_cancer/adeno/）。
参考文献----------References----------
Suzuki, R. and Shimodaira, H. (2006). pvclust: An R package for hierarchical clustering with p-values, Bioinformatics, 22, 1540-1542 (software is available from CRAN or http://www.is.titech.ac.jp/~shimo/prog/pvclust/).
参见----------See Also----------
sbpvclust, sbfit.pvclust
sbpvclust，sbfit.pvclust
实例----------Examples----------
## Not run: [＃不运行：]
## script to create lung73.pvclust and lung73.sb[＃脚本来创建lung73.pvclust的和lung73.sb]
## multiscale bootstrap resampling of hierarchical clustering[＃的多尺度举重采样的层次聚类]
library(pvclust)
data(lung)
sa <- 9^seq(-1,1,length=13) # wider range of scales than pvclust default[更广泛的规模比pvclust默认]
lung73.pvclust <- pvclust(lung,r=1/sa,nboot=10000)
lung73.sb <- sbfit(lung73.pvclust) # model fitting[模型拟合]
## End(Not run)[＃（不执行）]
## Not run: [＃不运行：]
## Parallel version of the above script[＃并行版本，上面的脚本]
## parPvclust took 80 mins using 40 cpu's[＃parPvclust了80分钟，使用40个CPU的]
library(snow)
library(pvclust)
data(lung)
cl <- makeCluster(40) # launch 40 cpu's[推出40 CPU的]
sa <- 9^seq(-1,1,length=13) # wider range of scales than pvclust default[更广泛的规模比pvclust默认]
lung73.pvclust <- parPvclust(cl,lung,r=1/sa,nboot=10000)
lung73.sb <- sbfit(lung73.pvclust,cluster=cl) # model fitting[模型拟合]
## End(Not run)[＃（不执行）]
## replace au/bp entries in pvclust object[＃替换AU / BP pvclust对象中的条目]
data(lung73)
lung73.new <- sbpvclust(lung73.pvclust,lung73.sb) # au <- k.3[欧< - K.3]
## Not run: [＃不运行：]
library(pvclust)
plot(lung73.new) # draw dendrogram with the new au/bp values[画树状图新金/ BP值]
pvrect(lung73.new)
## End(Not run)[＃（不执行）]
## diagnose edges 61,...,69[＃诊断边缘61，...，69]
lung73.sb[61:69] # print fitting details[打印配件细节]
plot(lung73.sb[61:69]) # plot curve fitting[图曲线拟合]
summary(lung73.sb[61:69]) # print au p-values[打印AU p-值]
## diagnose edge 67[＃诊断边缘67]
lung73.sb[[67]] # print fitting[打印配件]
plot(lung73.sb[[67]],legend="topleft") # plot curve fitting[图曲线拟合]
summary(lung73.sb[[67]]) # print au p-values[打印AU p-值]
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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 2012-9-29 22:25:38
