Methods for Function romer_score in Package gCMAP

Description

These methods provide a wrapper for the Rotation Gene Set Enrichment Analysis function romer Romer performes a competitive test in that the different gene sets are pitted against one another. Instead of permutation, it uses rotation, a parametric resampling method suitable for linear models (Langsrud, 2005).

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
## S4 method for signature 'eSet,CMAPCollection'
romer_score(experiment,sets,predictor=NULL,
design.matrix=NULL, element="exprs", keep.scores=FALSE, ...)

## S4 method for signature 'matrix,CMAPCollection'
romer_score(experiment, sets,...)

## S4 method for signature 'matrix,GeneSet'
romer_score(experiment,sets,...)

## S4 method for signature 'eSet,GeneSet'
romer_score(experiment, sets,...)

## S4 method for signature 'matrix,GeneSetCollection'
romer_score(experiment,sets,...)

## S4 method for signature 'eSet,GeneSetCollection'
romer_score(experiment, sets,...)

Arguments

sets

A CMAPCollection, GeneSetCollection or GeneSet object containing gene sets, with which to query the experiment object.

experiment

An eSet or data matrix with numeric data to compare the query object to.

predictor

A character vector or factor indicating the phenotypic class of the experiment data columns. Either the 'predictor' or 'design' parameter must be supplied.

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

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

Doerum G, Snipen L, Solheim M, Saeboe S (2009). Rotation testing in gene set enrichment analysis for small direct comparison experiments. Stat Appl Genet Mol Biol, Article 34.

Majewski, IJ, Ritchie, ME, Phipson, B, Corbin, J, Pakusch, M, Ebert, A, Busslinger, M, Koseki, H, Hu, Y, Smyth, GK, Alexander, WS, Hilton, DJ, and Blewitt, ME (2010). Opposing roles of polycomb repressive complexes in hematopoietic stem and progenitor cells. Blood, published online 5 May 2010. http://www.ncbi.nlm.nih.gov/pubmed/20445021

Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA, Paulovich, A, Pomeroy, SL, Golub, TR, Lander, ES and Mesirov JP, 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550

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
25
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 <- romer_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)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.