R语言 sdef包 simulation.indep()函数中文帮助文档(中英文对照)

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simulation.indep(sdef)
simulation.indep()所属R语言包：sdef

                                        Simulate p-values for two indipendent experiments
                                         两个indipendent实验模拟p-值

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

The function simulate two vectors of p-values using the procedure described in Hwang et al. for independent experiments
该函数模拟使用的程序中描述的Hwang等人的两个向量的p-值。独立实验


用法----------Usage----------


simulation.indep(n, GammaA = 2, GammaB = 2, epsilonM = 0,
epsilonSD = 1, r1, r2, DEfirst, DEsecond)



参数----------Arguments----------

参数：n
Number of features to be simulated
Number of features to be simulated


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
Parameter of the Gaussian noise


参数：epsilonSD
Parameter of the Gaussian noise
Parameter of the Gaussian noise


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 the first experiment
Number of DE features in the first experiment


参数：DEsecond
Number of DE features in in the second experiment
Number of DE features in in the second experiment


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 is the gene-experiment components. The former is assigned deterministically, whilst the latter is drawn from a standard Gaussian distribution. So the log fold change (FC(gk)) is the sum of all these components for each gene and experiment. We divide the n genes in three groups: genes differentially expressed only in the first experiment, genes differentially expressed only in the second experiment and genes differentially expressed in neither experiment. There are not true positive genes (i.e. truly DE in both experiments), so we should find no genes in common using our method.
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），是基因实验组件。前者被分配确定的，而后者则是来自一个标准的高斯分布。因此，所有这些组件的每个基因和实验的log倍的变化（FC（GK））的总和。我们把三组：n个基因的差异表达基因仅在第一个实验中，仅在第二个实验中，基因的差异表达基因的差异表达既不实验。有没有真正的阳性基因（即真正DE在这两个实验），所以我们应该用我们的方法没有找到共同的基因。

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))) where FC(gk) is the t statistic that evaluates the differential expression between the two classes for the g gene and k experiment.
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））），FC（GK）是t统计量，其值的差异表达两个类之间的为G基因和K实验。


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-values for the experiment 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-值在实验进行比较</ TD> </ TR> </ TABLE>


（作者）----------Author(s)----------


Alberto Cassese, Marta Blangiardo



参考文献----------References----------




实例----------Examples----------


data.indep = simulation.indep(n=500,GammaA=1,
GammaB=1,r1=0.5,r2=0.8,DEfirst=300,DEsecond=200)


转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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