roast: Rotation Gene Set Tests

In richierocks/limma2: Linear Models for Microarray Data

Description

Rotation gene set testing for linear models.

Usage

## S3 method for class 'EList'
roast(y, index=NULL, design=NULL, contrast=ncol(design), set.statistic="mean",
     gene.weights=NULL, array.weights=NULL, weights=NULL, block=NULL, correlation,
     var.prior=NULL, df.prior=NULL, trend.var=FALSE, nrot=999)
## S3 method for class 'EList'
mroast(y, index=NULL, design=NULL, contrast=ncol(design), set.statistic="mean",
     gene.weights=NULL, array.weights=NULL, weights=NULL, block=NULL, correlation,
     var.prior=NULL, df.prior=NULL, trend.var=FALSE, nrot=999, adjust.method="BH", midp=TRUE, sort="directional")

Arguments

y	numeric matrix giving log-expression or log-ratio values for a series of microarrays, or any object that can coerced to a matrix including ExpressionSet, MAList, EList or PLMSet objects. Rows correspond to probes and columns to samples. If either var.prior or df.prior are null, then y should contain values for all genes on the arrays. If both prior parameters are given, then only y values for the test set are required.
index	index vector specifying which rows (probes) of y are in the test set. This can be a vector of indices, or a logical vector of the same length as statistics, or any vector such as y[index,] contains the values for the gene set to be tested. For mroast, index is a list of index vectors. The list can be made using symbols2indices.
design	design matrix
contrast	contrast for which the test is required. Can be an integer specifying a column of design, or else a contrast vector of length equal to the number of columns of design.
set.statistic	summary set statistic. Possibilities are "mean","floormean","mean50" or "msq".
gene.weights	optional numeric vector of weights for genes in the set. Can be positive or negative. For mroast this vector must have length equal to nrow(y). For roast, can be of length nrow(y) or of length equal to the number of genes in the test set.
array.weights	optional numeric vector of array weights.
weights	optional matrix of observation weights. If supplied, should be of same dimensions as y and all values should be positive. If y is an EList or MAList object containing weights, then those weights will be used.
block	optional vector of blocks.
correlation	correlation between blocks.
var.prior	prior value for residual variances. If not provided, this is estimated from all the data using squeezeVar.
df.prior	prior degrees of freedom for residual variances. If not provided, this is estimated using squeezeVar.
trend.var	logical, should a trend be estimated for var.prior? See eBayes for details. Only used if var.prior or df.prior are NULL.
nrot	number of rotations used to estimate the p-values.
adjust.method	method used to adjust the p-values for multiple testing. See p.adjust for possible values.
midp	logical, should mid-p-values be used in instead of ordinary p-values when adjusting for multiple testing?
sort	character, whether to sort output table by directional p-values ("directional"), non-directional p-value ("mixed"), or not at all ("none").

Details

These functions implement the ROAST gene set tests proposed by Wu et al (2010). They perform self-contained gene set tests in the sense defined by Goeman and Buhlmann (2007). For competitive gene set tests, see camera. For a gene set enrichment analysis style analysis using a database of gene sets, see romer.

roast and mroast test whether any of the genes in the set are differentially expressed. They can be used for any microarray experiment which can be represented by a linear model. The design matrix for the experiment is specified as for the lmFit function, and the contrast of interest is specified as for the contrasts.fit function. This allows users to focus on differential expression for any coefficient or contrast in a linear model. If contrast is not specified, then the last coefficient in the linear model will be tested.

The argument gene.weights allows directional weights to be set for individual genes in the set. This is often useful, because it allows each gene to be flagged as to its direction and magnitude of change based on prior experimentation. A typical use is to make the gene.weights 1 or -1 depending on whether the gene is up or down-regulated in the pathway under consideration.

The arguments array.weights, block and correlation have the same meaning as for the lmFit function. The arguments df.prior and var.prior have the same meaning as in the output of the eBayes function. If these arguments are not supplied, they are estimated exactly as is done by eBayes.

The gene set statistics "mean", "floormean", "mean50" and msq are defined by Wu et al (2010). The different gene set statistics have different sensitivities to small number of genes. If set.statistic="mean" then the set will be statistically significantly only when the majority of the genes are differentially expressed. "floormean" and "mean50" will detect as few as 25% differentially expressed. "msq" is sensitive to even smaller proportions of differentially expressed genes, if the effects are reasonably large.

The output gives p-values three possible alternative hypotheses, "Up" to test whether the genes in the set tend to be up-regulated, with positive t-statistics, "Down" to test whether the genes in the set tend to be down-regulated, with negative t-statistics, and "Mixed" to test whether the genes in the set tend to be differentially expressed, without regard for direction.

roast estimates p-values by simulation, specifically by random rotations of the orthogonalized residuals (Langsrud, 2005), so p-values will vary slightly from run to run. To get more precise p-values, increase the number of rotations nrot. The p-value is computed as (b+1)/(nrot+1) where b is the number of rotations giving a more extreme statistic than that observed (Phipson and Smyth, 2010). This means that the smallest possible p-value is 1/(nrot+1).

mroast does roast tests for multiple sets, including adjustment for multiple testing. By default, mroast reports ordinary p-values but uses mid-p-values (Routledge, 1994) at the multiple testing stage. Mid-p-values are probably a good choice when using false discovery rates (adjust.method="BH") but not when controlling the family-wise type I error rate (adjust.method="holm").

Value

roast produces an object of class "Roast". This consists of a list with the following components:

p.value	data.frame with columns Active.Prop and P.Value, giving the proportion of genes in the set contributing meaningfully to significance and estimated p-values, respectively. Rows correspond to the alternative hypotheses mixed, up or down.
var.prior	prior value for residual variances.
df.prior	prior degrees of freedom for residual variances.

mroast produces a data.frame with a row for each set and the following columns:

NGenes	number of genes in set
PropDown	proportion of genes in set with z < -sqrt(2)
PropUp	proportion of genes in set with z > sqrt(2)
Direction	direction of change, "Up" or "Down"
PValue	two-sided directional p-value
FDR	two-sided directional false discovery rate
PValue.Mixed	non-directional p-value
FDR.Mixed	non-directional false discovery rate

Note

The default setting for the set statistic was changed in limma 3.5.9 (3 June 2010) from "msq" to "mean".

Author(s)

Gordon Smyth and Di Wu

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. http://www.statsci.org/smyth/pubs/PermPValuesPreprint.pdf

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/content/26/17/2176

See Also

camera, romer, geneSetTest, symbols2indices.

An overview of tests in limma is given in 08.Tests.

Examples

y <- matrix(rnorm(100*4),100,4)
design <- cbind(Intercept=1,Group=c(0,0,1,1))

# First set of 5 genes contains 3 that are genuinely differentially expressed
index1 <- 1:5
y[index1,3:4] <- y[index1,3:4]+3

# Second set of 5 genes contains none that are DE
index2 <- 6:10

roast(y,index1,design,contrast=2)
mroast(y,index1,design,contrast=2)
mroast(y,list(set1=index1,set2=index2),design,contrast=2)

richierocks/limma2 documentation built on May 27, 2019, 8:47 a.m.