"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, approx.zscore=TRUE, ...)
"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, approx.zscore=TRUE, adjust.method="BH", midp=TRUE, sort="directional", ...)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.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 ids2indices.design, or else a contrast vector of length equal to the number of columns of design."mean","floormean","mean50" or "msq".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.y and all values should be positive. If y is an EList or MAList object containing weights, then those weights will be used.squeezeVar.squeezeVar.var.prior? See eBayes for details. Only used if var.prior or df.prior are NULL.p.adjust for possible values."directional"), non-directional p-value ("mixed"), or not at all ("none").TRUE then a fast approximation is used to convert t-statistics into z-scores prior to computing set statistics. If FALSE, z-scores will be exact.roast produces an object of class "Roast".
This consists of a list with the following components:
Active.Prop and P.Value, giving the proportion of genes in the set contributing materially to significance and estimated p-values, respectively.
Rows correspond to the alternative hypotheses Down, Up, UpOrDown (two-sided) and Mixed.mroast produces a data.frame with a row for each set and the following columns:
z < -sqrt(2)z > sqrt(2)"Up" or "Down"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").
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
camera, romer, geneSetTest, ids2indices.There is a topic page on 10.GeneSetTests.
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)
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