| Version: | 1.1 |
| Date: | 2025-05-18 |
| Title: | Two Dimensional High Throughput 'GoMiner' |
| Maintainer: | Barry Zeeberg <barryz2013@gmail.com> |
| Author: | Barry Zeeberg [aut, cre] |
| Depends: | R (≥ 4.2.0) |
| Imports: | minimalistGODB, GoMiner, HTGM, grDevices, stats, gplots, jaccard, vprint, randomGODB, HGNChelper |
| LazyData: | true |
| LazyDataCompression: | xz |
| Description: | The Gene Ontology (GO) Consortium https://geneontology.org/ organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I developed a two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'). I generate a 2D heat map whose axes are any two of BP, MF, or CC, and the value within a picture element of the heat map reflects the Jaccard metric p-value for the number of genes in common for the corresponding pair. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| VignetteBuilder: | knitr |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| RoxygenNote: | 7.3.2 |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2025-05-18 20:03:40 UTC; barryzeeberg |
| Repository: | CRAN |
| Date/Publication: | 2025-05-18 20:30:02 UTC |
HTGM2D data set
Description
HTGM2D data set
Usage
data(GOGOA3small)
HTGM2D
Description
run 2D version of GoMiner
Usage
HTGM2D(dir, geneList, ontologies, GOGOA3)
Arguments
dir |
character string full path name to the directory acting as result repository |
geneList |
character vector of user-supplied genes of interest |
ontologies |
character vector of 2 ontologies e.g. c("biological_process","cellular_component") |
GOGOA3 |
return value of subsetGOGOA() |
Value
returns the return value of Jaccard()
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")
subd<-tempdir()
geneList<-cluster52
ontologies<-c("biological_process","cellular_component")
mat<-HTGM2D(subd,geneList,ontologies,GOGOA3)
## End(Not run)
HTGM2Ddriver
Description
driver to invoke GoMiner and HTGM2D, and compare the results
Usage
HTGM2Ddriver(
dir,
geneList,
ontologies,
GOGOA3,
enrichThresh = 2,
countThresh = 5,
pvalThresh = 0.1,
fdrThresh = 0.1,
nrand = 100,
mn,
mx,
opt = 0,
verbose = 1
)
Arguments
dir |
character string full path name to the directory acting as result repository |
geneList |
character vector of user-supplied genes of interest |
ontologies |
character vector of 2 ontologies e.g. c("biological_process","cellular_component") |
GOGOA3 |
return value of subsetGOGOA() |
enrichThresh |
numerical acceptance threshold for enrichment passed to GoMiner |
countThresh |
numerical acceptance threshold for gene count passed to GoMiner |
pvalThresh |
numerical acceptance threshold for pval |
fdrThresh |
numerical acceptance threshold for fdr |
nrand |
numeric number of randomizations to compute FDR |
mn |
integer param passed to trimGOGOA3, min size threshold for a category |
mx |
integer param passed to trimGOGOA3, max size threshold for a category |
opt |
integer 0:1 parameter used to select randomization method |
verbose |
integer vector representing classes |
Value
returns no value, but saves hyperlinked SVG heatmap files to a results directory
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")
geneList<-cluster52
ontologies<-c("biological_process","cellular_component")
dir<-tempdir()
HTGM2Ddriver(dir,geneList,ontologies,GOGOA3,enrichThresh=2,
countThresh=5,pvalThresh=0.10,fdrThresh=0.10,nrand=100,mn=2,mx=2000)
## End(Not run)
HTGM2D data set
Description
HTGM2D data set
Usage
data(Housekeeping_Genes)
Jaccard
Description
create the heat map data that is needed as input to JaccardHeatMap()
Usage
Jaccard(dir, m1, m2, thresh1 = 2, thresh2 = 3, B = 100)
Arguments
dir |
character string full pathname to the directory acting as result repository |
m1 |
return value of catGenes |
m2 |
return value of catGenes |
thresh1 |
integer acceptance threshold for the number of genes in a cat |
thresh2 |
integer acceptance threshold for the number of common genes in 2 cats |
B |
integer a total bootstrap iteration |
Value
returns a numerical matrix containing number of genes and associated p value in the intersection of 2 categories
Examples
#load("data/x_m1.RData")
#load("data/x_m2.RData")
mat<-Jaccard(dir=tempdir(),x_m1,x_m2)
JaccardHeatMap
Description
use the Jaccard metric to construct 2D heat map
Usage
JaccardHeatMap(dir, mat)
Arguments
dir |
character string containing path name of output directory |
mat |
return value of Jaccard() |
Value
returns a Jaccard matrix of cat1 vs cat2 FDR, and also saves hyperlinked SVG heatmap files to a results directory
Examples
#load("data/x_jmat.RData")
dir<-tempdir()
jHeatMap<-JaccardHeatMap(dir,x_jmat)
catGenes
Description
match up genes in gene list with categories in GOGOA3 database
Usage
catGenes(geneList, GOGOA3, ontology)
Arguments
geneList |
character vector of user-supplied genes of interest |
GOGOA3 |
return value of subsetGOGOA() |
ontology |
c("molecular_function","cellular_component","biological_process") |
Value
returns a matrix of 1's and 0's indicating the presence or absence of gene-category pairs
Examples
#load("data/GOGOA3small.RData")
geneList<-cluster52
m1<-catGenes(geneList,GOGOA3small,"biological_process")
HTGM2D data set
Description
HTGM2D data set
Usage
data(cluster52)
compareGoMinerHTGM2D
Description
compare the results of GoMiner and HTGM2D
Usage
compareGoMinerHTGM2D(subd, mat, l, ontologies)
Arguments
subd |
character string full path name to the output subdirectory |
mat |
return value of Jaccard() |
l |
of return values of GoMiner() |
ontologies |
character vector of 2 ontologies e.g. c("biological_process","cellular_component") |
Value
returns no value, but saves files that list category difference between GoMiner and HTG2D
Examples
ontologies<-c("biological_process","cellular_component")
#load("data/x_l.Rdata")
#load("data/x_mat.Rdata")
subd<-tempdir()
compareGoMinerHTGM2D(subd,x_mat,x_l,ontologies)
HTGM2D data set
Description
HTGM2D data set
Usage
data(x_jmat)
HTGM2D data set
Description
HTGM2D data set
Usage
data(x_l)
HTGM2D data set
Description
HTGM2D data set
Usage
data(x_m1)
HTGM2D data set
Description
HTGM2D
Usage
data(x_m2)
HTGM2D data set
Description
HTGM2D
Usage
data(x_mat)