The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as snm, sva, and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.
To install the Bioconductor release version, open R and type:
if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("edge")
To install the development version, open R and type:
install.packages("devtools") library("devtools") install_github(c("jdstorey/qvalue","jdstorey/edge"), build_vignettes = TRUE)
Instructions on using edge can be viewed by typing:
To get started, first load the kidney dataset included in the package:
library(edge) data(kidney) names(kidney)
The kidney study is interested in determining differentially expressed genes with respect to age in kidney tissue. The
age variable is the age of the subjects and the
sex variable is whether the subjects were male or female. The expression values for the genes are contained in the
kidexpr <- kidney$kidexpr age <- kidney$age sex <- kidney$sex
Once the data has been loaded, the user has two options to create the experimental models:
build_study. If the experiment models are unknown to the user,
build_study can be used to create the models:
edge_obj <- build_study(data = kidexpr, adj.var = sex, tme = age, sampling = "timecourse") full_model <- fullModel(edge_obj) null_model <- nullModel(edge_obj)
sampling describes the type of experiment performed,
adj.var is the adjustment variable and
tme is the time variable in the study. If the experiment is more complex then type
?build_study for additional arguments.
If the alternative and null models are known to the user then
build_models can be used to make a deSet object:
library(splines) cov <- data.frame(sex = sex, age = age) null_model <- ~sex full_model <- ~sex + ns(age, df=4) edge_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model)
cov is a data frame of covariates, the
null.model is the null model and the
full.model is the alternative model. The input
cov is a data frame with the column names the same as the variables in the alternative and null models. Once the models have been generated, it is often useful to normalize the gene expression matrix using
apply_snm and/or adjust for unmodelled variables using
edge_norm <- apply_snm(edge_obj, int.var=1:ncol(exprs(edge_obj)), diagnose=FALSE) edge_sva <- apply_sva(edge_norm)
lrt function can be used on
edge_sva to implement either the optimal discovery procedure or the likelihood ratio test, respectively:
# optimal discovery procedure edge_odp <- odp(edge_sva, bs.its = 30, verbose=FALSE) # likelihood ratio test edge_lrt <- lrt(edge_sva)
To access the proportional of null p-values estimate, p-values, q-values and local false discovery rates for each gene, use the function
qval_obj <- qvalueObj(edge_odp) qvals <- qval_obj$qvalues pvals <- qval_obj$pvalues lfdr <- qval_obj$lfdr pi0 <- qval_obj$pi0
See the vignette for more detailed explanations of the edge package.
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