FDR

Description

compute the false discovery rate (FDR) of the hypergeometric p values of genes mapping to gene ontology (GO) categories

Usage

FDR(sampleList, tablePop3, hyper, GOGOA3, nrand, ontology, subd, opt = 0)

Arguments

Value

returns a list with FDR information

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
fdr<-FDR(x_sampleList1,x_tablePop31,x_hyper1,GOGOA3,3,"biological_process",tempdir(),0)

## End(Not run)

GoMiner data set

Description

GoMiner data set

Usage

data(GOGOA3small)

GOenrich3

Description

compute the gene enrichment in a GO category

Usage

GOenrich3(tableSample3, tablePop3)

Arguments

Value

returns a matrix with columns c("SAMPLE","POP","ENRICHMENT")

Examples

m<-GOenrich3(x_tableSample3,x_tablePop3)

GOheatmap

Description

generate a matrix to be used as input to a heat map

Usage

GOheatmap(sampleList, x, thresh, fdrThresh = 0.105, verbose)

Arguments

Value

returns a matrix to be used as input to a heat map

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
heatmap<-GOheatmap(cluster52,GOGOA3$ontologies[["biological_process"]],x_thresh,verbose=1)

## End(Not run)

GOhypergeometric

Description

compute the hypergeometric p value for gene enrichment in a GO category

Usage

GOhypergeometric3(tableSample3, tablePop3)

Arguments

Value

returns a matrix with columns c("x","m","n","k","p")

Examples

hyper<-GOhypergeometric3(x_tableSample3,x_tablePop3)

GOtable3

Description

tabulate number of geneList mappings to GO categories

Usage

GOtable3(hgncList, DB)

Arguments

Value

returns a list whose components are c("DB","table","ngenes") where 'DB' is the GO DB subsetted to the desired ONTOLOGY, and 'table' is tabulation of number of occurrences of each GO category name within the desired ONTOLOGY, and ngenes is the total number of hgncList genes mapping to GOGOA

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
x<-GOtable3(cluster52,GOGOA3$ontologies[["biological_process"]])

## End(Not run)

GOthresh

Description

retrieve lines of m that meet both enrichThresh and countThresh

Usage

GOthresh(m, sampleFDR, enrichThresh, countThresh, pvalThresh, fdrThresh)

Arguments

Value

returns a subset of matrix (m joined with fdr$sampleFDR) with entries meeting all thresholds

Examples

thresh<-GOthresh(x_m,x_fdr$sampleFDR,enrichThresh=2,countThresh=2,pvalThresh=0.1,fdrThresh=0.100)

GoMiner

Description

driver to generate heatmap

Usage

GoMiner(
  title = NULL,
  dir,
  sampleList,
  GOGOA3,
  ontology,
  enrichThresh = 2,
  countThresh = 5,
  pvalThresh = 0.1,
  fdrThresh = 0.1,
  nrand = 100,
  mn = 2,
  mx = 200,
  opt,
  verbose = 1
)

Arguments

Details

modes of FDR estimation: opt=0 use original database with randomized geneLists opt=1 use original geneList with internally scrambled genes databases (uses randomGODB())

databases that can be used with the real geneList: these are explicitly passed as parameter to GoMiner() (1) original GOGOA3 (2) randomized version of GOGOSA GOGOA3R<-randomGODB(GOGOA3) (3) database containing a subset of the big hitters genes (randomGODB2driver()) attempts to compensate for the over-annotation of some genes, that might lead to false positive if gene G has a lot of mappings to categories, randomly sample G/category pairs to retain a reasonable number of them. e.g., reduce G from 100 category mappings to 7 category mappings, by omitting 93 of the mappings G/category mappings

Value

returns a matrix suitable to generate a heatmap

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
l<-GoMiner("Cluster52",tempdir(),cluster52,
 GOGOA3=GOGOA3,ontology="biological_process",enrichThresh=2,
 countThresh=5,pvalThresh=0.10,fdrThresh=0.10,nrand=2,mn=2,mx=200,opt=0,verbose=1)
 
 # try out yeast database!
 load("/Users/barryzeeberg/personal/GODB_RDATA/sgd/GOGOA3_sgd.RData")
 # make sure this is in fact the database for the desired species
 GOGOA3$species
 # use database to find genes mapping to an interesting category
 cat<-"GO_0042149__cellular_response_to_glucose_starvation"
 w<-which(GOGOA3$ontologies[["biological_process"]][,"GO_NAME"]==cat)
 geneList<-GOGOA3$ontologies[["biological_process"]][w,"HGNC"]
 l<-GoMiner("YEAST",tempdir(),geneList,
  GOGOA3,ontology="biological_process",enrichThresh=2,
  countThresh=3,pvalThresh=0.10,fdrThresh=0.10,nrand=2,mn=2,mx=200,opt=0)

## End(Not run)

GoMiner data set

Description

GoMiner data set

Usage

data(HCCS66)

GoMiner data set

Description

GoMiner data set

Usage

data(Housekeeping_Genes)

RCPD

Description

prepare a cpd of p values from randomized gene sets

Usage

RCPD(GOGOA3, tablePop, geneList, nrand, ontology, hyper, subd, opt)

Arguments

Details

the cpd of the randomizations is to be used for estimating the false discovery rate (FDR) of the real sampled genes

Value

returns a histogram of log10(p)

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
rcpd<-RCPD(GOGOA3,x_tablePop31,10,3,"biological_process",x_hyper1,tempdir(),0)

## End(Not run)

checkGeneListVsDB

Description

determine if gene list and database contain compatible identifiers

Usage

checkGeneListVsDB(geneList, ontology, GOGOA3, thresh = 0.5, verbose = FALSE)

Arguments

Value

returns no value, but may have side effect of aborting the computation

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
checkGeneListVsDB(geneList=cluster52,ontology="biological_process",
 GOGOA3,thresh=0.5,verbose=TRUE)

# supposed to generate error message
load("/Users/barryzeeberg/personal/GODB_RDATA/sgd/GOGOA3_sgd.RData")
checkGeneListVsDB(geneList=xenopusGenes,ontology="biological_process",
 GOGOA3,thresh=0.5,verbose=TRUE)

## End(Not run)

GoMiner data set

Description

GoMiner data set

Usage

data(cluster52)

hitterBeforeAfterDriver

Description

driver to invoke hitters2() and trimGOGOA3()

Usage

hitterBeforeAfterDriver(GOGOA3, mn = 20, mx = 200, verbose)

Arguments

Value

returns the return value of trimGOGOA3()

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# This example is given in full detail in the package vignette.
# You can generate GOGOA3.RData using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO
dir<-"/Users/barryzeeberg/personal/GODB_RDATA/goa_human/"
load(sprintf("%s/%s",dir,"GOGOA3_goa_human.RData"))
geneList<-GOGOA3$ontologies[["biological_process"]][1:10,"HGNC"]
GOGOA3tr<-hitterBeforeAfterDriver(GOGOA3,mn=20,mx=200,1)

## End(Not run)

hitters2

Description

determine the number of mappings for the top several genes

Usage

hitters2(GOGOA3, verbose = 1)

Arguments

Value

returns no value, but has side effect of printing information

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# This example is given in full detail in the package vignette.
# You can generate GOGOA3.RData using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO
dir<-"/Users/barryzeeberg/personal/GODB_RDATA/goa_human/"
load(sprintf("%s/%s",dir,"GOGOA3_goa_human.RData"))
geneList<-GOGOA3$ontologies[["biological_process"]][1:10,"HGNC"]
hitters2(GOGOA3,1)

## End(Not run)

human

Description

determine if database represents human species

Usage

human(GOGOA3, verbose = TRUE)

Arguments

Value

returns Boolean TRUE if species is human

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
hum<-human(GOGOA3)

load("/Users/barryzeeberg/personal/GODB_RDATA/sgd/GOGOA3_sgd.RData")
hum<-human(XENOPUS,1)

## End(Not run)

preprocessDB

Description

driver to perform several preprocessing steps: quick peek trim small and large categories is the database for human species validate validated HGNC symbols in sampleList determine up to date (ie, contains GOGOA3$species) or legacy version of human database

Usage

preprocessDB(sampleList, GOGOA3, ontology, mn, mx, thresh, verbose)

Arguments

Value

returns a list whose components are a trimmed version of GOGOA3 and (for human) a sampleList with validated HGNC symbols

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
pp<-preprocessDB(cluster52,GOGOA3,"biological_process",20,200,0.5,3)

## End(Not run)

randSubsetGeneList

Description

retrieve n unique random genes

Usage

randSubsetGeneList(geneList, ngenes)

Arguments

Value

returns a character vector of genes

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
genes<-randSubsetGeneList(GOGOA3$genes[["biological_process"]],20)

## End(Not run)
 

runGoMinerExamples

Description

driver to run GoMiner under several randomization procedures

Usage

runGoMinerExamples(
  title = NULL,
  dir,
  sampleList,
  GOGOA3,
  ontology,
  enrichThresh = 2,
  countThresh = 5,
  pvalThresh = 0.1,
  fdrThresh = 0.1,
  nrand = 2,
  mn = 2,
  mx = 200,
  verbose = 1
)

Arguments

Value

returns a list containing the return value of GoMiner()

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# you can generate it using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases
load("/Users/barryzeeberg/personal/GODB_RDATA/goa_human/GOGOA3_goa_human.RData")
ontology<-"biological_process"
t<-sort(table(GOGOA3$ontologies[[ontology]][,"HGNC"]),decreasing=TRUE)
dir<-tempdir()

sampleList<-names(t)[1:50]
title<-"hi_hitters"
hh<-runGoMinerExamples(title,dir,sampleList,GOGOA3,ontology,nrand=5)

sampleList<-names(t)[1001:1050]
title<-"hi_hitters5"
hh<-runGoMinerExamples(title,dir,sampleList,GOGOA3,ontology,nrand=5)

sampleList<-cluster52
title<-"cluster52"
hh<-runGoMinerExamples(title,dir,sampleList,GOGOA3,ontology,nrand=5)

## End(Not run)

trimGOGOA3

Description

remove categories from GOGOA3 that are too small or too large

Usage

trimGOGOA3(GOGOA3, mn, mx, verbose)

Arguments

Details

If a category is too small, it is unreliable for statistical evaluation Also, in the extreme case of size = 1, then that category is essentially equivalent to a gene rather than a category. Same is partially true for size = 2. If a category is too large, it is too generic to be useful for categorization. Finally, by trimming the database, analyses will run faster.

Value

returns trimmed version of GOGOA3

Examples

## Not run: 
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# This example is given in full detail in the package vignette.
# You can generate GOGOA3.RData using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO/tree/main/databases

GOGO3tr<-trimGOGOA3(GOGOA3,mn=2,mx=200,1)

## End(Not run)

validHGNCSymbols

Description

convert outdated HGNC symbols to current HGNC symbols

Usage

validHGNCSymbols(geneList)

Arguments

Details

removes NA and /// from output of checkGeneSymbols()

Value

returns list of mapping table and vector of current HGNC symbols

Examples

geneList<-c("FN1", "tp53", "UNKNOWNGENE","7-Sep",
 "9/7", "1-Mar", "Oct4", "4-Oct","OCT4-PG4", "C19ORF71",
  "C19orf71")
l<-validHGNCSymbols(geneList)

GoMiner data set

Description

GoMiner data set

Usage

data(x_fdr)

GoMiner data set

Description

GoMiner data set

Usage

data(x_hyper1)

GoMiner data set

Description

GoMiner data set

Usage

data(x_m)

GoMiner data set

Description

GoMiner data set

Usage

data(x_sampleList1)

GoMiner data set

Description

GoMiner data set

Usage

data(x_tablePop3)

GoMiner data set

Description

GoMiner data set

Usage

data(x_tablePop31)

GoMiner data set

Description

GoMiner data set

Usage

data(x_tableSample3)

GoMiner data set

Description

GoMiner data set

Usage

data(x_thresh)