ma.summarize: Microarray summarization
In fboulnois/made: Microarray Analysis of Differential Expression

Description Usage Arguments Details Value References Examples

Summarize a microarray experiment at the level of genes.

1	ma.summarize(config, eset)

`config`	Character string consisting of the path to the configuration file generated using the `write.yaml.config` function or parsed configuration list associated with a microarray experiment.
`eset`	Expression set object describing microarray experiment at the level of probes.

Transforms an expression set (eset) which describes the microarray experiment data at the probe level into a list of genes and their associated log-fold changes and statistical values. The statistical values include the p-values, the False Discovery Rate (FDR) adjusted p-values (q-values), and the 95% confidence intervals for the log-fold change. Expression sets which are not log-transformed are log-transformed for the purpose of this function. Batch effects are adjusted using Surrogate Variable Analysis (SVA) and gene-level summarization is assessed using the empirical Bayes function from the limma package. If the package GOstats is installed, Gene Ontology (GO) term enrichment is performed to determine biologically relevant terms in each group comparison. Similarly, if the ReactomePA package is installed, pathway analysis is performed using the Reactome database to determine which biological pathways are involved in each group comparison.

A summary of all the data in the expression set for each group comparison.

top.tables: A list of genes, associated log-fold changes, and other statistical values of interest.
go.terms: A list of biological terms associated with the expression set.
reactome: A list of biological pathways associated with the expression set.
limma.model: An MArrayLM fitted model object containing all of the statistical information relating to the expression set produced by the eBayes function in the limma package.
design.matrix: The experimental design matrix which represents the associations between samples and groups and is used to help fit the model. This matrix may have been modified by the sva function if batch effects were adjusted.

Benjamini, Yoav, and Yosef Hochberg. "Controlling the false discovery rate: a practical and powerful approach to multiple testing." Journal of the Royal Statistical Society. Series B (Methodological) (1995): 289-300.

Leek, Jeffrey T., and John D. Storey. "Capturing heterogeneity in gene expression studies by surrogate variable analysis." PLoS Genet 3, no. 9 (2007): 1724-1735.

Ritchie, Matthew E., Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, and Gordon K. Smyth. "limma powers differential expression analyses for RNA-sequencing and microarray studies." Nucleic acids research (2015): gkv007.

Falcon, Seth, and Robert Gentleman. "Using GOstats to test gene lists for GO term association." Bioinformatics 23, no. 2 (2007): 257-258.

Croft, David, Gavin O'Kelly, Guanming Wu, Robin Haw, Marc Gillespie, Lisa Matthews, Michael Caudy et al. "Reactome: a database of reactions, pathways and biological processes." Nucleic acids research (2010): gkq1018.

if(require(madeData))
{
  dataPath <- system.file("extdata", package = "madeData")
  config <- file.path(dataPath, "config.yaml")
  eset <- readRDS(file.path(dataPath, "eset.rds"))
  ma.summarize(config, eset)
}