roast {limma} | R Documentation |
Rotation gene set testing for linear models.
## Default S3 method: roast(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL, set.statistic = "mean", gene.weights = NULL, var.prior = NULL, df.prior = NULL, nrot = 999, approx.zscore = TRUE, ...) ## Default S3 method: mroast(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL, set.statistic = "mean", gene.weights = NULL, var.prior = NULL, df.prior = NULL, nrot = 999, approx.zscore = TRUE, adjust.method = "BH", midp = TRUE, sort = "directional", ...) ## Default S3 method: fry(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL, standardize = "posterior.sd", sort = "directional", ...)
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 |
index |
index vector specifying which rows (probes) of |
design |
design matrix |
contrast |
contrast for which the test is required.
Can be an integer specifying a column of |
geneid |
gene identifiers corresponding to the rows of |
set.statistic |
summary set statistic. Possibilities are |
gene.weights |
numeric vector of directional (positive or negative) contribution weights specifying the size and direction of the contribution of each probe to the gene set statistics.
For |
var.prior |
prior value for residual variances. If not provided, this is estimated from all the data using |
df.prior |
prior degrees of freedom for residual variances. If not provided, this is estimated using |
nrot |
number of rotations used to compute the p-values. |
approx.zscore |
logical, if |
adjust.method |
method used to adjust the p-values for multiple testing. See |
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-value ( |
standardize |
how to standardize for unequal probewise variances. Possibilities are |
... |
any argument that would be suitable for |
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 that 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 index
is often made using ids2indices but does not have to be.
Each set to be tested is represented by a vector of row numbers or a vector of gene IDs.
Gene IDs should correspond to either the rownames of y
or the entries of geneid
.
The argument gene.weights
allows directional contribution 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.
Probes with directional weights of opposite signs are expected to have expression changes in opposite directions.
If there are multiple sets to be tested, then set-specific gene weights can be included as part of the index
.
If any of the entries of index
are data.frames, then the second column will be assumed to be gene contribution weights.
All three functions (roast
, mroast
and fry
) support set-specific gene contribution weights as part of an index
data.frame.
Note that the contribution weights set by gene.weights
are different in nature and purpose to the precision weights set by the weights
argument to lmFit
.
gene.weights
control the contribution of each gene to the formation of the gene set statistics, and can be positive or negative.
weights
indicate the precision of the expression measurements and should be positive.
The weights
are used to construct genewise test statistics whereas gene.weights
are used to combine the genewise test statistics.
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, then 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"
).
fry
is a fast approximation to mroast
.
In the special case that df.prior
is large and set.statistic="mean"
, fry
gives the same result as mroast
with an infinite number of rotations.
In other circumstances, when genes have different variances, fry
uses a standardization strategy to approximate the mroast
results.
Using fry
may be advisable when performing tests for a large number of sets, because it is fast and because the fry
p-values are not limited by the number of rotations performed.
roast
produces an object of class "Roast"
.
This consists of a list with the following components:
p.value |
data.frame with columns |
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 |
PropUp |
proportion of genes in set with |
Direction |
direction of change, |
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 |
fry
produces the same output format as mroast
but without the columns PropDown
and ProbUp
.
The default setting for the set statistic was changed in limma 3.5.9 (3 June 2010) from "msq"
to "mean"
.
Gordon Smyth and Di Wu
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 10.GeneSetTests for a description of other functions used for gene set testing.
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) fry(y,list(set1=index1,set2=index2),design,contrast=2)