ind.cal.ES: Calculate the effect sizes
In MetaDE: MetaDE: Microarray meta-analysis for differentially expressed gene detection

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/meta_analysis03282012.r

The function can be used to calculate various effect sizes(and the corresponding sampling variances) that are commonly used in meta-analyses.

1	ind.cal.ES(x, paired, nperm = NULL,miss.tol=0.3)

`x`	a list of data sets and their labels. The first list is a list of datasets, the second list is a list of their labels
`paired`	A vector of logical values to specify the design patterns of studies. see 'Details'.
`nperm`	an integer to specify the number of permutations.
`miss.tol`	The maximum percent missing data allowed in any gene (default 30 percent).

This functions is used to calculate the effect size, standardized mean difference, often used in meta-analysis.

The argument paired is a vector of logical values to specify whether 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.

`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 argument `nperm` is set as NULL.
`perm.Var`	The corresponding variances calculated from permutations. `perm.Var` is NULL if the argument `nperm` is set as NULL.

Jia Li and Xingbin Wang

Morris SB: Distribution of the standardized mean change effect size for meta-analysis on repeated measures. Br J Math Stat Psychol 2000, 53 ( Pt 1):17-29.

Choi et al, Combining multiple microarray studies and modeling interstudy variation. Bioinformatics,2003, i84-i90.

MetaDE.ES

#---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')

Loading required package: survival
Loading required package: impute
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: combinat

Attaching package: 'combinat'

The following object is masked from 'package:utils':

    combn

Loading required package: tools
$mu.hat
 [1] 1.0940742 1.7413051 2.0915977 1.4799549 1.6290456 0.9436879 1.9087713
 [8] 1.0263015 1.9647400 1.5585980 1.1245436 1.5268566 2.3485394 1.3810362
[15] 0.8637464 2.5288764 1.8432231 2.0498139 1.4267858 2.1393629

$mu.var
 [1] 0.4154618 0.7679196 0.3114783 0.2556543 0.2878482 0.2976388 0.2955025
 [8] 0.2299672 1.5763934 0.2637792 0.2322046 0.8105116 0.7570523 0.2477028
[15] 0.3106108 0.3600550 0.3803569 0.3323811 0.2509275 0.3147503

$Qval
 [1] 1.718927271 2.558723504 0.270810787 0.140446579 1.050935022 1.292603317
 [7] 0.597938068 0.632226688 4.578909019 0.462282785 0.101564453 2.866529983
[13] 2.075623100 0.003441046 1.368491278 0.019397695 1.282735586 1.058549495
[19] 0.005512836 0.041738943

$Qpval
 [1] 0.18983119 0.10968712 0.60278841 0.70783746 0.30529184 0.25556875
 [7] 0.43936583 0.42653972 0.03236782 0.49655904 0.74996025 0.09043967
[13] 0.14966881 0.95322258 0.24207127 0.88923236 0.25739102 0.30354624
[19] 0.94081267 0.83811820

$tau2
 [1] 0.35030060 0.94583613 0.00000000 0.00000000 0.02861450 0.13586832
 [7] 0.00000000 0.00000000 2.48095699 0.00000000 0.00000000 1.06393304
[13] 0.80047015 0.00000000 0.16838825 0.00000000 0.17182236 0.03805625
[19] 0.00000000 0.00000000

$zval
 [1] 1.697388 1.987087 3.747695 2.926995 3.036348 1.729751 3.511344 2.140139
 [9] 1.564850 3.034687 2.333678 1.695972 2.699199 2.774850 1.549808 4.214472
[17] 2.988700 3.555464 2.848293 3.813304

$pval
 [1] 5.50e-02 2.65e-02 1.00e-20 1.50e-03 1.50e-03 5.00e-02 1.00e-20 1.55e-02
 [9] 7.55e-02 1.50e-03 9.50e-03 5.55e-02 4.00e-03 2.50e-03 7.95e-02 1.00e-20
[17] 1.50e-03 1.00e-20 2.50e-03 1.00e-20

$FDR
               REM
 [1,] 6.166667e-02
 [2,] 3.533333e-02
 [3,] 4.000000e-20
 [4,] 3.333333e-03
 [5,] 3.333333e-03
 [6,] 6.166667e-02
 [7,] 4.000000e-20
 [8,] 2.214286e-02
 [9,] 7.947368e-02
[10,] 3.333333e-03
[11,] 1.461538e-02
[12,] 6.166667e-02
[13,] 6.666667e-03
[14,] 4.545455e-03
[15,] 7.950000e-02
[16,] 4.000000e-20
[17,] 3.333333e-03
[18,] 4.000000e-20
[19,] 4.545455e-03
[20,] 4.000000e-20

attr(,"nstudy")
[1] 2
attr(,"meta.method")
[1] "REM"
attr(,"class")
[1] "MetaDE.ES"