genBart (version 1.0.1)

genModScores: Generate modular (gene set) maps for plotting

Description

Generate modular (gene set) maps for plotting

Usage

genModScores(meta, gene.sets, sd.lim = 2, annotations = NULL)

Arguments

meta

list returned by metaData

gene.sets

list of gene sets. See genModelResults for additional formatting details.

sd.lim

number of standard deviations away from the mean of the reference samples. Default is 2.

annotations

A data frame of additional annotations for the gene sets. Default is NULL. See genModelResults for additional details.

Value

scores.ctrl data frame of module scores for all samples with respect to controls.

scores.base data frame of module scores for all time point samples with respect to their baseline.

gene.sets List of gene sets provided through gene.sets

Details

This function calculates module scores for individual samples. In cross sectional studies with controls, the control samples are used to determine an upper and lower threshold (mean of controls +/- 2 sd). The module proportion for each sample is then calculated based on the percentage of genes within a module that are above or below this threshold. For example, if 40% of the genes within a module are above the threshold and 15% are below it, the final module score would be 25% up (40-15). In longitudinal settings, module scores are calculated with respect to controls and baseline samples. In cross sectional studies without controls, genModules cannot be used, since there are no reference samples with which to calculate a threshold.

Examples

# NOT RUN {
# Example data
data(tb.expr)
data(tb.design)
data(modules)

# Demonstrate on first 100 probes
dat <- tb.expr[1:100, ]

# Create desInfo object
meta.data <- metaData(y = tb.expr, design = tb.design, data.type = "microarray", 
                    columnname = "columnname", long = TRUE, sample.id = "sample_id",
                    subject.id = "monkey_id", time.var = "timepoint",
                    baseline.var = "timepoint", baseline.val = 0)

# Generate module maps                                      
mods <- genModScores(meta.data, modules)
# }