safe: Significance Analysis of Function and Expression

In lgeistlinger/safe: Significance Analysis of Function and Expression

Significance Analysis of Function and Expression

Description

Performs a significance analysis of function and expression (SAFE) for a gene expression experiment and a set of functional categories specified by the user. SAFE is a two-stage resampling-based method that can be applied to a 2-sample, paired, multi-class, simple linear and right-censored linear regression models. Other experimental designs can also be accommodated through user-defined functions.

Usage

safe(X.mat, y.vec, C.mat = NULL, Z.mat = NULL,
     method = "permutation", platform = NULL, 
     annotate = NULL, min.size = 2, max.size = Inf, 
     by.gene = FALSE, local = "default", global = "default", 
     args.local = NULL, args.global = list(one.sided = FALSE), 
     Pi.mat = NULL, error = "FDR.BH", parallel=FALSE, alpha = NA, 
     epsilon = 10^(-10), print.it = TRUE, ...)

Arguments

X.mat	A matrix or data.frame of expression data of size m by n where each row corresponds to a gene feature and each column to a sample. Data should be properly normalized and cannot contain missing values.
y.vec	A numeric, integer or character vector of length n containing the response of interest. For examples of the acceptable forms y.vec can take, see the vignette.
C.mat	A matrix containing the gene category assignments. Each column represents a category and should be named accordingly. For each column, values of 1 (TRUE) and 0 (FALSE) indicate whether the features in the corresponding rows of X.mat are contained in the category. This can also be a list containing a sparse matrix and names as created by getCmatrix.
Z.mat	A data.frame of size n by p, with p covariates as numeric or factors.
method	Type of hypothesis test can be specified as "permutation", "bootstrap.t", and "bootstrap.q". "express" calls the dependent package safeExpress. See vignette for details.
platform	If C.mat is unspecified, a character string of a Bioconductor annotation package can be used to build gene categories. See vignette for details and examples.
annotate	If C.mat is unspecified, a character string to specify the type of gene categories to build from annotation packages. "GO.MF", "GO.BP", "GO.CC", and "GO.ALL" (default) specify one or all Gene Ontologies. "KEGG" specifies pathways, and "PFAM" homologous families from the respective sources.
min.size	Optional minimum category size in building C.mat.
max.size	Optional maximum category size in building C.mat.
by.gene	Logical argument (default = FALSE) specifying whether multiple features to a single gene should be down-weighted.
local	Specifies the gene-specific statistic from the following options: "t.Student", "t.Welch", and "t.paired", for 2-sample designs, "f.ANOVA" for 1-way ANOVAs, and "t.LM" for simple linear regressions. "default" will choose between "t.Student", "f.ANOVA", and "t.LM" based on the form of y.vec. User-defined local statistics can also be used; details are provided in the vignette.
global	Specifies the global statistic for a gene categories. By default, the Wilcoxon rank sum ("Wilcoxon") is used. Else, a Fisher's Exact test statistic ("Fisher"), a Pearson's chi-squared type statistic ("Pearson") or t-statistic for average difference ("AveDiff") is available. User-defined global statistics can also be implemented.
args.local	An optional list to be passed to user-defined local statistics that require additional arguments. By default args.local = NULL.
args.global	An optional list to be passed to global statistics that require additional arguments. For two-sided local statistics, args.global = list(one.sided=F) allows bi-directional differential expression to be considered.
Pi.mat	Either an integer, or a matrix or data.frame containing the permutations. See getPImatrix for the acceptable form of a matrix or data.frame. If Pi.mat is an integer, B, then safe will generate B resamples of X.mat.
error	Specifies the method for computing error rate estimates. By default, Benjamini-Hochberg step down ("FDR.BH") FDR estimates are computed. A Bonferroni ("FWER.Bonf") and Holm's step-up ("FWER.Holm") adjustment can also be specified. Under permutation, "FDR.YB" computes the Yekutieli-Benjamini FDR estimate, and "FWER.WY" computes the Westfall-Young FWER estimate. The user can also specify "none" if no error rates are desired.
parallel	Logical argument (default = FALSE) specifying whether hypothesis test of method should be conducted with parallel processing. Only compatible with error = "none", "FWER.Bonf", or FDR.BH. See vignette for details.
alpha	The threshold for significant results to return. By default, alpha will be 0.05 for nominal p-values (error = "none" ), and 0.1 for adjusted p-values.
epsilon	Numeric argument sets the minimum difference for ranking local and global statistics, correcting a numerical precision issue when computing empirical p-values in small data sets (n < 15). The default value is 10^(-10).
print.it	Logical argument (default = TRUE) specifying whether to print progress updates to the log for permutation and bootstrap calculations.
...	Allows arguments from version 2.0 to be ignored.

Details

safe utilizes a general framework for testing differential expression across gene categories that allows it to be used in various experimental designs. Through structured resampling of the data, safe accounts for the unknown correlation among genes, and enables proper estimation of error rates when testing multiple categories. safe also provides statistics and empirical p-values for the gene-specific differential expression.

Value

The function returns an object of class SAFE. See help for SAFE-class for more details.

Author(s)

William T. Barry: [e-mail]

References

W. T. Barry, A. B. Nobel and F.A. Wright, 2005, Significance Analysis of functional categories in gene expression studies: a structured permutation approach, Bioinformatics 21(9) 1943–1949.

See also the vignette included with this package.

See Also

safeplot, safe.toptable, gene.results, getCmatrix, getPImatrix.

Examples

## Simulate a dataset with 1000 genes and 20 arrays in a 2-sample design.
## The top 100 genes will be differentially expressed at varying levels

g.alt <- 100
g.null <- 900
n <- 20

data<-matrix(rnorm(n*(g.alt+g.null)),g.alt+g.null,n)
data[1:g.alt,1:(n/2)] <- data[1:g.alt,1:(n/2)] + 
                         seq(2,2/g.alt,length=g.alt)
dimnames(data) <- list(c(paste("Alt",1:g.alt),
                         paste("Null",1:g.null)),
                       paste("Array",1:n))

## A treatment vector 
trt <- rep(c("Trt","Ctr"),each=n/2)

## 2 alt. categories and 18 null categories of size 50

C.matrix <- kronecker(diag(20),rep(1,50))
dimnames(C.matrix) <- list(dimnames(data)[[1]],
    c(paste("TrueCat",1:2),paste("NullCat",1:18)))
dim(C.matrix)

results <- safe(data,trt,C.mat = C.matrix,Pi.mat = 100)
results

## SAFE-plot made for the first category
if (interactive()) { 
safeplot(results,"TrueCat 1")
}

lgeistlinger/safe documentation built on Aug. 4, 2023, 7:34 p.m.