Description Usage Arguments Details Value Author(s) References Examples
Calculate single study estimates of effect size for lists of ExpressionSets
1 | ssStatistic(statistic = c("t", "sam", "z")[1], phenotypeLabel, esetList, ...)
|
statistic |
Character string indicating Welch t-statistic (t), SAM (sam), or a z-statistic (z) |
phenotypeLabel |
Character string indicating the name of the binary covariate |
esetList |
An object of class |
... |
Not implemented. Potentially additional arguments to the above methods that are implemented in other packages |
This function is a wrapper that provides an estimate of effect size
for each study (element) in an ExpressionSetList
object.
For Welch t-statistic, this function is a wrapper for mt.teststat in the multtest package.
For SAM, this function is a wrapper for the sam function in the siggenes package.
The "z" statistic is a standardized unbiased estimate of effect size (Hedges and Olkin, 1985) – implementation is in the zScores function in the R package GeneMeta.
See the complete references below.
A matrix: rows are genes and columns are studies
R. Scharpf
J.K. Choi, U. Yu, S. Kim, and O.J. Yoo (2003), Combining multiple microarray studies and modeling interstudy variation, Bioinformatics, 19(1) I84-I90.
Y. Ge, S. Dudoit & T. P. Speed (2003), Resampling-based multiple testing for microarray data hypothesis Test 12(1) : 1-44 (with discussions on 44-77).
L. Lusa R. Gentleman, and M. Ruschhaupt, GeneMeta: MetaAnalysis for High Throughput Experiments
L.V. Hedges and I. Olkin, Statistical Methods for Meta-analysis (1985), Academic Press
Tusher, Tibshirani and Chu (2001), Significance analysis of microarrays applied to the ionizing radiation response, PNAS 2001 98: 5116-5121, (Apr 24).
1 2 3 4 | data(expressionSetList)
if(require(siggenes)){
sam <- ssStatistic("sam", esetList=expressionSetList, phenotypeLabel="adenoVsquamous")
}
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