Module_Enrich {DDPNA}

Module_Enrich

Description

Enrichment analysis of a sets of proteins in all modules. The function offered two enrichment methods:ORA and FCS.

Usage

Module_Enrich(module, classifiedID, enrichtype = "FCS",
              coln = "new.ID", datainf = NULL, p.adj.method = "BH")

Arguments

module

module information which is getted in Module_inf function.

classifiedID

a sets of protein IDs which is ordered by change value/ p value and so on.

enrichtype

enrichment method. This must be (an abbreviation of) one of the strings "FCS","ORA". "FCS" means analyzes step-by-step a proteins list which is ordered by change ratio/ p value and so on. "ORA" means analyzes a proteins list by Fisher's extract test.

coln

column name of module contains protein IDs. it could be matched with "classifiedID"

datainf

proteomic data protein ID information. The default value is "NULL". which is means that the "classifiedID" come from proteomic information is the same as the module construction proteomic information. If they are different, proteomic data information should be given.

p.adj.method

p adjust methods of multiple comparisons. it can seen in p.adjust.methods.

Value

a list contains classifiedID enrichment information.

Counts

the counts of classifiedID in module.

module.size

the number of module ID

module.name

module name

precent

counts divided module.size

p

enrichment p value in each module

p.adj

enrichment p.adj value in each module

Z.score

Z score is -log2 P value.

Author(s)

Kefu Liu

Examples

data(net)
data(imputedData)
data <- imputedData
logD <- data$log2_value
rownames(logD) <- data$inf$ori.ID
group <- gsub("[0-9]+","", colnames(logD))
Module <- Module_inf(net, data$inf)
up <- changedID(logD, group, vs.set2 = "ad",vs.set1 = "ctl",
              rank = "foldchange",anova = FALSE, Padj = "none",cutoff = 1,
              datatype = "log2",fctype = "up")
FCSenrich <- Module_Enrich(Module, up, coln="ori.ID")