# Methods for Function mroast_score in Package gCMAP

### Description

These methods provide a wrapper for the Rotation Gene Set Tests function mroast mroast tests whether any of the genes in the set are differentially expressed.

### Usage

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## S4 method for signature 'eSet,CMAPCollection'
mroast_score(
experiment,
sets,
predictor=NULL,
design.matrix=NULL,
element="exprs",
keep.scores=FALSE,
...)
## S4 method for signature 'matrix,CMAPCollection'
mroast_score(experiment, sets,...)
## S4 method for signature 'matrix,GeneSet'
mroast_score(experiment,sets,...)
## S4 method for signature 'eSet,GeneSet'
mroast_score(experiment, sets,...)
## S4 method for signature 'matrix,GeneSetCollection'
mroast_score(experiment,sets,...)
## S4 method for signature 'eSet,GeneSetCollection'
mroast_score(experiment,sets,...)
``` |

### Arguments

`sets` |
A |

`experiment` |
An |

`predictor` |
A character vector or factor indicating the phenotypic
class of the |

`design.matrix` |
A design matrix for the experiment. Either the 'predictor' or 'design' parameter must be supplied. If both are supplied, the 'design' is used. |

`element` |
Character vector specifying which channel of an eSet to extract (defaults to "exprs", alternatives may be e.g. "z", etc.) |

`keep.scores` |
Logical: keep gene-level scores for all gene sets (Default: FALSE) ? The size of the generated CMAPResults object increases with the number of contained gene sets. For very large collections, setting this parameter to 'TRUE' may require large amounts of memory. |

`...` |
Additional arguments passed to downstream methods. |

### Value

A `CMAPResults`

object.

### References

Goeman, JJ, and Buhlmann, P (2007). Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 23, 980-987.

Langsrud, O (2005). Rotation tests. Statistics and Computing 15, 53-60.

Phipson B, and Smyth GK (2010). Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Statistical Applications in Genetics and Molecular Biology, Volume 9, Article 39.

Routledge, RD (1994). Practicing safe statistics with the mid-p. Canadian Journal of Statistics 22, 103-110.

Wu, D, Lim, E, Francois Vaillant, F, Asselin-Labat, M-L, Visvader, JE, and Smyth, GK (2010). ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176-2182. http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btq401?

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
data(gCMAPData)
gene.set.collection <- induceCMAPCollection(gCMAPData, "z", higher=2, lower=-2)
sampleNames( gene.set.collection ) <- c("set1", "set2", "set3")
## random score matrix
y <- matrix(rnorm(1000*6),1000,6, dimnames=list(featureNames(gCMAPData), 1:6))
## set1 is differentially regulated
effect <- as.vector(members(gene.set.collection[,1]) * 2)
y[,4:6] <- y[,4:6] + effect
predictor <- c( rep("Control", 3), rep("Case", 3))
res<- mroast_score(y, gene.set.collection, predictor = predictor,keep.scores=TRUE)
res
## heatmap of expression scores for set1
set1.expr <- geneScores(res)[["set1"]]
heatmap(set1.expr, scale="none", Colv=NA, labCol=predictor,
RowSideColors=ifelse( attr(set1.expr, "sign") == "up", "red", "blue"),
margin=c(7,5))
legend(0.35,0,legend=c("up", "down"),
fill=c("red", "blue"),
title="Annotated sign",
horiz=TRUE, xpd=TRUE)
``` |