paths_to_go_ancestor: Create path results table with highest significant GO...
In hipathia: HiPathia: High-throughput Pathway Analysis

Description Usage Arguments Details Value Examples

Create table of results with the comparison of the paths together with the GO functional annotation and the highest significant GO ancestor (HSGOA).

1	paths_to_go_ancestor(pathways, comp_paths, comp_go, pval = 0.05)

`pathways`	Pathways object
`comp_paths`	Wilcoxon comparison of the matrix of pathways values as returned by `do_wilcoxon`.
`comp_go`	Wilcoxon comparison of the matrix of GO values as returned by `do_wilcoxon`.
`pval`	P-value cut-off. Default values is set to 0.05.

The table returns in each row: the name of a pathway and its Wilcoxon comparison information (direction, adjusted p-value), the GO term to which the path is related (not necessarily unique), the Wilcoxon comparison informationfor this GO (direction, adjusted p-value), the HSGOA of this GO and its Wilcoxon comparison information (direction, adjusted p-value).

The HSGOA is computed as the GO term with minimum level from all the significant (with respect to value pval) ancestors of a GO. The level of a GO term is computed as the number of nodes in the shortest path from this GO term to the term "GO:0008150". The ancestors of a node are defined as all the nodes from which a path can be defined from the ancestor to the node.

Table of comparisons with Highest common ancestors

data(comp)
data(go_vals)
data(brca_design)
data(path_vals)
sample_group <- brca_design[colnames(path_vals),"group"]
comp_go <- do_wilcoxon(go_vals, sample_group, g1 = "Tumor", g2 = "Normal")
## Not run: pathways <- load_pathways(species = "hsa", pathways_list =
c("hsa03320", "hsa04012"))
table <- paths_to_go_ancestor(pathways, comp, comp_go)
## End(Not run)