simulation(sdef)
simulation()所属R语言包：sdef
Simulate p-values for two related experiments
两个相关的实验模拟的p值
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function simulates two vectors of p-values using the procedure described in Hwang et al.
该函数模拟的两个向量的使用的程序中描述的Hwang等人的p-值。
用法----------Usage----------
simulation(n, GammaA, GammaB, epsilonM = 0,
epsilonSD = 1, r1, r2, DEfirst, DEsecond, DEcommon)
参数----------Arguments----------
参数：n
Number of features to simulate
Number of features to simulate
参数：GammaA
Parameter of the Gamma distribution
Parameter of the Gamma distribution
参数：GammaB
Parameter of the Gamma distribution
Parameter of the Gamma distribution
参数：epsilonM
Parameter of the Gaussian noise specific to the genes and experiment
Parameter of the Gaussian noise specific to the genes and experiment
参数：epsilonSD
Parameter of the Gaussian noise specific to the genes and experiment
Parameter of the Gaussian noise specific to the genes and experiment
参数：r1
Additional experiment-specific noise
Additional experiment-specific noise
参数：r2
Additional experiment-specific noise
Additional experiment-specific noise
参数：DEfirst
Number of DE features in each experiment
Number of DE features in each experiment
参数：DEsecond
Number of DE features in each experiment
Number of DE features in each experiment
参数：DEcommon
Number of DE features in common between the two experiments
Number of DE features in common between the two experiments
Details
详细信息----------Details----------
Considering two experiments (k=1,2), each of them with two classes, and n genes, for each gene we simulate a true difference between the classes delta(g), drawn from a Gamma distribution with random sign. The true difference delta(g) is 0 if the gene is not differentially expressed. We then add two normal random noise components, r(k) that act as experiment specific components and epsilon(gk), that are the gene-experiment components. The former is assigned deterministically, whilst the latter is drawn from a standard Gaussian distribution. The log fold change (FC(gk)) is the sum of all these components for each gene and experiment. We assign the n genes to four groups: genes differentially expressed (DE) in both experiments, genes differentially expressed only in the first experiment, genes differentially expressed only in the second experiment and genes differentially expressed in neither experiment. When the genes are differentially expressed in both experiments, they share the same delta(g) and the only difference between them is given by the random components: FC(g1) = delta(g) + r(1) times epsilon(g1) FC(g2) = delta(g) + r(2) times epsilon(g2) This group represents the true positive genes (i.e. truly DE in both experiments) that we are interested in finding using our method. The two groups of genes differentially expressed only in one of the two experiments act like additional noise and make the simulation more realistic.
考虑到两个实验（K = 1,2），他们每个人有两个班，和n个基因，每个基因中，我们模拟了真实的不同类之间的Delta（G），来自一个Gamma分布的随机迹象。真正的差异增量（g）是0，如果没有差异表达的基因。然后，我们添加了两个正常的随机噪声成分，R（K）作为实验的具体成分和ε（GK），是基因实验的组成部分。前者被分配确定的，而后者则是来自一个标准的高斯分布。log倍的变化（FC（GK））是所有这些组件的每个基因和实验的总和。我们指定的n个基因组：基因差异表达（DE）在这两个实验中，只在第一个实验中差异表达的基因，只有在第二个实验中，基因的差异表达基因的差异表达的实验都没有。当基因的差异表达的在这两个实验中，它们共享相同的增量（g）和它们之间的唯一区别随机分量由下式给出：（FC g1的）=δ（克）+ r（下1）倍（εg1的） FC（G2）=Δ（G）+ R（2）次ε（G2）这组代表真正的阳性基因（即真正DE在这两个实验），我们发现用我们的方法有兴趣的。仅在一个，两个实验的2个组的基因差异表达像附加噪声和使模拟更逼真。
Then, as described in Hwang et al., a two tails T-test is performed for each FC(gk) and a p-value is generated as: P(gk) = 2 Normal cdf(-absolute value (FC(gk)/r(k))).
接着，Hwang等人描述的，两个尾部T-检验进行每个FC（GK），产生的p-值：P（GK）= 2普通函数cdf（绝对值（FC（GK） / R（K）））。
值----------Value----------
<table summary="R valueblock"> <tr valign="top"><td>names</td> <td> Which group each simulated gene expression value belongs to</td></tr> <tr valign="top"><td>FC1</td> <td> T statistic for the first experiment</td></tr> <tr valign="top"><td>FC2</td> <td> T statistic for the second experiment</td></tr> <tr valign="top"><td>Pval</td> <td> p-value for the experiments to be compared</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> names</ TD> <TD>每个模拟基因的表达值属于哪一组</ TD> </ TR> < TR VALIGN =“顶”> <TD>FC1 </ TD> <TD>第一个实验中的t统计量</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD> T统计值的第二个实验</ TD> </ TR> <tr valign="top"> <TD>FC2 </ TD> <TD> P-值实验进行比较</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Alberto Cassese, Marta Blangiardo
参考文献----------References----------
实例----------Examples----------
data = simulation(n=500,GammaA=1,GammaB=1,
r1=0.5,r2=0.8,DEfirst=300,DEsecond=200,
DEcommon=100)
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。
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 2012-9-29 23:28:44
