# MetaDE.ES

##### Identify differentially expressed genes by combining effect sizes

Function to fit the meta-analytic fixed- and random-effects models.The data consists of effect sizes and corresponding variances from your own method/calculations.

##### Usage

`MetaDE.ES(x, meta.method = c("FEM", "REM"))`

##### Arguments

- x
- a list with components.
**ES**: The observed effect sizes.**Var**: The observed variances corresponding to`ES`

**perm.ES**: The effect sizes calculated from permutations,`perm.ES`

is NULL if the argum

- meta.method
- a character string specifying whether a fixed- or a random/mixed-effects model should be fitted. A fixed-effects model is fitted when using meta.method="FEM". Random-effects model is fitted by setting meta.method equal to "REM". See "Details".

##### Details

The function can be used to combine any of the usual effect size used in meta-analysis,such as standardized mean differences.Simply specify the observed effect sizes via the x$ES
and the corresponding variances vis x$Var. If the effect sizes and corresponding varicances calculated from permutation are available,then specify them by x$perm.ES and x$perm.Var,
respectively.
The argument `paired`

is a vecter of logical values to specify whethe the corresponding study is paired design or
not. If the study is pair-designed, the effect sizes (corresponding variances) are calcualted using the formula in morris's
paper, otherwise calculated using the formulas in choi *et al*.
In addition, if the components of x, perm.ES and perm.Var, are not "NULL", the p-values are calculated using permutation method, otherwise, the p-values are calculated using parametric method by
assupming the z-scores following a standard normal distribution.

##### Value

- The object is a list containing the following components:
zval test statistics of the aggregated value. pval p-values for the test statistics. FDR A matrix with one column which has the corrected p-values using Benjamini and Hochberg method (see `references`

).Qval test statistics for the test of heterogeneity. Qpval p-values for the test of heterogeneity. tau2 estimated amount of (residual) heterogeneity.

##### References

Choi et al, Combining multiple microarray studies and modeling interstudy variation. Bioinformatics,2003, i84-i90. Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289:300.

##### See Also

##### Examples

```
#---example 1: Meta analysis of Differentially expressed genes between two classes----------#
label1<-rep(0:1,each=5)
label2<-rep(0:1,each=5)
exp1<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,2),20,5))
exp2<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,1.5),20,5))
x<-list(list(exp1,label1),list(exp2,label2))
ind.res<-ind.cal.ES(x,paired=rep(FALSE,2),nperm=100)
MetaDE.ES(ind.res,meta.method='REM')
```

*Documentation reproduced from package MetaDE, version 1.0.5, License: GPL-2*