Creates tables similar to the results tables of an mvGST object except for one gene set at a time. For each gene set selected, a table is produced with a single one and all zeroes in each column.
graphCell relies on tools in the Rgraphviz package. graphCell uses pickOut to get the GO terms in a specific cell of the results table. A GO graph is created from those GO terms, and can be interactive if desired. Also, if desired, a legend showing the names of the GO terms can be printed. If the graph is interactive, use esc to end interaction with graph.
mvGST provides platform-independent tools to identify GO terms (gene sets) that are differentially active (up or down) in multiple contrasts of interest. Given a matrix of one-sided p-values (rows for genes, columns for contrasts), mvGST uses meta-analytic methods to combine p-values for all genes annotated to each gene set, and then classify each gene set as being significantly more active (1), less active (-1), or not significantly differentially active (0) in each contrast of interest. With multiple contrasts of interest, each gene set is assigned to a profile (across contrasts) of differential activity. Tools are also provided for visualizing (in a GO graph) the gene sets classified to a given profile.
Takes a named numeric vector of raw p-values as input and returns the Short Focus Level adjusted p-values, where the adjustment is based on controlling the FWER at a specified level within the structure of the GO graph.
pickOut returns a character vector with the Gene Ontology ID's of the gene sets with a particular significance profile for a certain contrast (the gene sets in one cell of the results.table of an mvGST object).
profileTable takes a matrix of one-sided p-values with rows representing genes and columns representing contrasts. Rows (and p-values) are combined using Stouffer's method so that the new rows represent gene sets. P-values are then adjusted for multiple hypothesis testing using the Benjamini-Yekutieli adjustment and converted to 1 (p-value < alpha/2), -1 (alpha > 1-alpha/2) or 0 (not significant). Then each gene set is classified according to its significance profile (across one of the factors) for each of the remaining contrasts.
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