preprocess: A preprocessing method for objects of class GSCA or NWA

Description Usage Arguments Details Value See Also Examples

Description

This is a generic function. When implemented as the S4 method for objects of class GSCA-class or NWA-class, this function filters out invalid data, removes duplicated genes, converts annotations to Entrez identifiers, etc.

Usage

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## S4 method for signature 'GSCA'
preprocess(object, species = "Hs", initialIDs = "SYMBOL",
  keepMultipleMappings = TRUE, duplicateRemoverMethod = "max",
  orderAbsValue = FALSE, verbose = TRUE)

## S4 method for signature 'NWA'
preprocess(object, species = "Hs",
  duplicateRemoverMethod = "max", initialIDs = "SYMBOL",
  keepMultipleMappings = TRUE, verbose = TRUE)

Arguments

object

A GSCA-class or NWA-class object.

species

A single character value specifying the species of the input. It supports all the species of OrgDb objects in AnnotationDbi. The format should be an abbreviation of the organism as setted by AnnotationDbi. For example, the commonly used ones are "Dm" ("Drosophila_melanogaster"), "Hs" ("Homo_sapiens"), "Rn" ("Rattus_norvegicus"), "Mm" ("Mus_musculus"), "Ce" ("Caenorhabditis_elegans"), and etc.

initialIDs

A single character value specifying the type of initial identifiers for input 'geneList'. The valid terms need match with the keytypes of species db such as keytypes(org.Hs.eg.db).

keepMultipleMappings

A single logical value. If TRUE, the function keeps the entries with multiple mappings (first mapping is kept). If FALSE, the entries with multiple mappings will be discarded.

duplicateRemoverMethod

A single character value specifying the method to remove the duplicates. See help(duplicateRemover) for details.

orderAbsValue

A single logical value indicating whether the values should be converted to absolute values and then ordered (if TRUE), or ordered as they are (if FALSE). This argument is only for class GSCA-class.

verbose

A single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE).

Details

This function will do the following preprocessing steps:

1:filter out p-values (the slot pvalues of class NWA), phenotypes (the slot phenotypes of class NWA) and data for enrichment (the slot geneList of class GSCA) with NA values or without valid names, and invalid gene names (the slot hits of class GSCA);

2:invoke function duplicateRemover to remove duplicated genes in the slot pvalues, phenotypes of class NWA, and the slot geneList and hits of class GSCA;

3:invoke function annotationConvertor to convert annotations from initialIDs to Entrez identifiers. Please note that the slot hits and the names of the slot geneList of class GSCA, the names of the slot pvalues and the names of the slot phenotypes of class NWA must have the same type of gene annotation specified by initialIDs;

4:order the data for enrichment decreasingly for objects of class GSCA.

See the function duplicateRemover for more details about how to remove duplicated genes.

See the function annotationConvertor for more details about how to convert annotations.

Value

In the end, this function will return an updated object of class GSCA-class or NWA-class.

See Also

duplicateRemover, annotationConvertor

Examples

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# ===========================================================
# GSCA class
library(org.Hs.eg.db)
library(GO.db)
library(KEGGREST)
## load data for enrichment analyses
data(d7)
phenotype <- as.vector(d7$neg.lfc)
names(phenotype) <- d7$id

## select hits if you also want to do GSOA, otherwise ignore it
hits <-  names(phenotype[which(abs(phenotype) > 2)])

## set up a list of gene set collections
GO_MF <- GOGeneSets(species="Hs", ontologies=c("MF"))
PW_KEGG <- KeggGeneSets(species="Hs")
ListGSC <- list(GO_MF=GO_MF, PW_KEGG=PW_KEGG)

## create an object of class 'GSCA'
gsca <- GSCA(listOfGeneSetCollections = ListGSC, geneList = phenotype, hits = hits)

## do preprocessing
gsca1 <- preprocess(gsca, species="Hs", initialIDs="SYMBOL", keepMultipleMappings=TRUE,
                   duplicateRemoverMethod="max", orderAbsValue=FALSE)

## print gsca1
gsca1

# ===========================================================
# NWA class
library(org.Hs.eg.db)
library(GO.db)
## load data for subnetwork analyses
data(d7)
pvalues <- d7$neg.p.value
names(pvalues) <- d7$id

## input phenotypes if you want to color nodes by it
phenotypes <- as.vector(d7$neg.lfc)
names(phenotypes) <- d7$id

## create an object of class 'NWA' with phenotypes
nwa <- NWA(pvalues=pvalues, phenotypes=phenotypes)

## do preprocessing
nwa1 <- preprocess(nwa, species="Hs", initialIDs="SYMBOL", keepMultipleMappings=TRUE,
                   duplicateRemoverMethod="max")

CityUHK-CompBio/HTSanalyzeR2 documentation built on Aug. 28, 2018, 1:19 a.m.