| Title: | Judging Quality of Clustering Methods using Mutual Information |
|---|---|
| Description: | ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. |
| Authors: | Adrian Pasculescu |
| Maintainer: | Adrian Pasculescu <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.29.0 |
| Built: | 2024-11-29 04:40:25 UTC |
| Source: | https://github.com/bioc/ClusterJudge |
ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview.
The DESCRIPTION file:
| Package: | ClusterJudge |
| Type: | Package |
| Title: | Judging Quality of Clustering Methods using Mutual Information |
| Version: | 1.29.0 |
| Date: | 2022-02-06 |
| Author: | Adrian Pasculescu |
| Maintainer: | Adrian Pasculescu <[email protected]> |
| Description: | ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. |
| Depends: | R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite |
| Suggests: | yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt |
| License: | Artistic-2.0 |
| biocViews: | Software, StatisticalMethod, Clustering, GeneExpression, GO |
| LazyLoad: | yes |
| LazyData: | yes |
| VignetteBuilder: | knitr |
| Config/pak/sysreqs: | libjpeg-dev libpng-dev libssl-dev |
| Repository: | https://bioc.r-universe.dev |
| RemoteUrl: | https://github.com/bioc/ClusterJudge |
| RemoteRef: | HEAD |
| RemoteSha: | e8c9f1db5ad37e32b939c4721a03d61a6470fdc9 |
Index of help topics:
ClusterJudge-package Judging Quality of Clustering Methods using
Mutual Information
Yeast.GO.assocs Gene Ontology attributes associated to Yeast
Gene entities
attribute_mut_inf calculates the mutual information between each
attribute of the entity.attribute pairs
clusterJudge judges clustering using an entity.attribute
table
clusterJudge_z_score calculates the 'cluster judge' z-score
consolidate_entity_attribute
removes the redundant attributes based on the
mutual information between attributes
convert_Yeast_SGDId_2_systematic
converts the SGD Gene Id into the systematic
name of the gene
download_Yeast_GO_mapping
downloads the Gene Ontology attributes mapped
to Yeast genes
download_Yeast_Go_terms
downloads the description of Gene Ontology
attributes
entities_attribute_stats
presents basic statistics on the number of
entities per attribute
mi.GO.Yeast precalculated mutual information between Gene
Ontology attributes of Yeast genes
validate_association validates the associations between entities and
attributes
ClusterJudge is a way to judge the quality of clustering performed elsewhere on some entities. This judgement is based on some additional entitity-attribute information. For example, using Gene Ontology annotated attributes offered by Saccharomyces Genome Database (SGD), it can judge the clusters of Yeast genes (i.e. entities) resulted from experiments related to the mitotic Yeast cell cycle. This is done by evaluating the mutual information between a gene membership in a cluster, and the attributes it possesses.
Adrian Pasculescu
Maintainer: Adrian Pasculescu <[email protected]>
Gibbons, F.D. and Roth F.P., (2002) Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation. Genome Research, vol. 12, pp1574-1581.
Tavazoie, et al. (1999) Systematic determination of genetic network architecture. Nature Genetics, 22, 281-285
Cho, et al. (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell,2, 65-73.
Cover,T.M. and Thomas,J.A. 1991. Elements of information theory (ed. D.L. Schilling). Wiley-Interscience,New York.
calculates the mutual information based on the entropy. The mutual information of the pair of attributes A,B is mi(A,b) = H(A)+H(B) -H(cbind(A,B)) where H is the entropy.
attribute_mut_inf(entity.attribute, show.progress = FALSE, alternative.calc = FALSE)attribute_mut_inf(entity.attribute, show.progress = FALSE, alternative.calc = FALSE)
entity.attribute |
data frame or matrix with 2 columns The assumption is that first column represent some 'entities' like gene names or gene ids. And the second column represents ‘attributes' of entities (for example Gene Ontology ID ’GO:0007260' which is 'tyrosine phosphorylation of STAT protein') |
show.progress |
if set to TRUE will try to show periodically the estimated percentage of completion. Note that calculation of mutual information between Gene Ontology (GO) attributes takes a long time. We already have precalculated the GO mutual information for Yeast |
alternative.calc |
logical that if set to TRUE will try to use a direct calculation of the entropy from its definition and bypass the functions defined in the infotheo R package |
a matrix having as rownames and columnnames the attribute names from the input entity.attribute structure. The values are the mutual informations between the attributes in the region above the main diagonal. All the values below the diagonal are NA or zero.
Calculation of mutual information can be time consuming when many attributes are present for many entities
Adrian Pasculescu
Gibbons, F.D. and Roth F.P., (2002) Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation. Genome Research, vol. 12, pp1574-1581.
Cover,T.M. and Thomas,J.A. 1991. Elements of information theory (ed. D.L. Schilling). Wiley-Interscience,New York.
mi.GO.Yeast
consolidate_entity_attribute
data(mi.GO.Yeast) ### is a precalculated example of mutual information for the Yeast genes ### and their Gene Ontollogy (GO) attributes str(mi.GO.Yeast) #### the following example will generate the mutual information #### of 100 sampled pairs of Yeast , GO associations #### (otherwise it might take take minutes or hours to calculate the mutual information) data(Yeast.GO.assocs) ### entity.attribute.sampled <- Yeast.GO.assocs[sample(1:nrow(Yeast.GO.assocs),100),] mi.GO.Yeast.sampled <- attribute_mut_inf(entity.attribute.sampled, show.progress=TRUE)data(mi.GO.Yeast) ### is a precalculated example of mutual information for the Yeast genes ### and their Gene Ontollogy (GO) attributes str(mi.GO.Yeast) #### the following example will generate the mutual information #### of 100 sampled pairs of Yeast , GO associations #### (otherwise it might take take minutes or hours to calculate the mutual information) data(Yeast.GO.assocs) ### entity.attribute.sampled <- Yeast.GO.assocs[sample(1:nrow(Yeast.GO.assocs),100),] mi.GO.Yeast.sampled <- attribute_mut_inf(entity.attribute.sampled, show.progress=TRUE)
calculates the sum of mutual information between clusters and each of the attributes For example if we note mutual information between Clusters and each Attribute i as: MI(C,Ai) (where C is the variable that has the cluster ids) then, for all attributes (assuming independence) it records the sum: MI(C,A) = Sum(MI(C,Ai)) = n*E(C)+sum(E(Ai))-sum(E(C,Ai))
Then swaps elements between a random pair of clusters and repeats the above calculations. The swapping is repeated 2 times, 4 times, 8 times ... up to 2^12 times Finaly a complete shuffle of clustering is applied. The resulting variation of the total mutual information as a fraction of the initial value is ploted against the increasing number of swaps (and the final suffling) A god clustering should have a pronounced decrease of the total mutual information when the of number of swaps is increased.
clusterJudge(clusters, entity.attribute, plot.notes = "", plot.saveRDS.file=NULL)clusterJudge(clusters, entity.attribute, plot.notes = "", plot.saveRDS.file=NULL)
clusters |
a named vectors of integers (or a factor). The names (or the levels of the factor) must match some (as many as possible) of the entities of the entity.attribute structure. |
entity.attribute |
data frame or matrix with 2 columns The assumption is that first column represent some 'entities' like gene names or gene ids. And the second column represents ‘attributes' of entities (for example Gene Ontology ID ’GO:0007260' which is 'tyrosine phosphorylation of STAT protein') Usually this is a consolidated entity.attribute where the attributes with very low number of entities or with very low mutual information have been removed (see consolidate_entity_attribute and the definition of Uncertainty on attributes mutual information) |
plot.notes |
a string that will be added to the plot as explanation of what clustering represents. |
plot.saveRDS.file |
if not NULL must be a string represented a file location where the plot will be saved as an RDS object. The plot can be then retrieved at any time using readRDS function. |
a data.frame
with the number of swapps between clusters used for randomization
and the total mutual information calculated after each of the sets of swaps
The last value is for the full shuffle of the clusters.
Since the shuffle is using the base sample function witout setting a random seed, the last value will vary.
a dot is printed on the console after each of the 12 sets of swaps
Adrian Pasculescu
Gibbons, F.D. and Roth F.P., (2002) Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation. Genome Research, vol. 12, pp1574-1581.
consolidate_entity_attribute
help
library('yeastExpData') data(ccyclered) clusters <- ccyclered$Cluster ### convert from Gene names to the new standard of Saccharomyces Genome Database (SGD) gene ids ccyclered$SGDID <- sub('^S','S00',ccyclered$SGDID) names(clusters) <- ccyclered$SGDID str(clusters) data(Yeast.GO.assocs) str(Yeast.GO.assocs) ### get the pre-calculated mutual information between all pairs of Gene Ontology (GO) attributes of Yeast genes data(mi.GO.Yeast) str(mi.GO.Yeast) ### consolidate (reduce) the number of attributes by removing attributes with very few entities ### and removing ones very corelated (i.e. with high mutual information) Yeast.GO.assocs.cons <- consolidate_entity_attribute(entity.attribute = Yeast.GO.assocs , min.entities.per.attr =3 , mut.inf=mi.GO.Yeast , U.limit = c(0.8, 0.1, 0.001)) #### apply clusterJudge for enntity attributes having a very low uncertainty (0.001) mi.by.swaps<-clusterJudge(clusters, entity.attribute=Yeast.GO.assocs.cons[["0.001"]] , plot.notes='Yeast clusters judged at uncertainty level 0.001 - Ref: Tavazoie S,& all `Systematic determination of genetic network architecture. Nat Genet. 1999`' , plot.saveRDS.file= 'cj.rds') ### save the plot for later use p <- readRDS('cj.rds') ### retrieve the previous plot pdf('cj.pdf'); plot(p); dev.off() ### plot on another devicelibrary('yeastExpData') data(ccyclered) clusters <- ccyclered$Cluster ### convert from Gene names to the new standard of Saccharomyces Genome Database (SGD) gene ids ccyclered$SGDID <- sub('^S','S00',ccyclered$SGDID) names(clusters) <- ccyclered$SGDID str(clusters) data(Yeast.GO.assocs) str(Yeast.GO.assocs) ### get the pre-calculated mutual information between all pairs of Gene Ontology (GO) attributes of Yeast genes data(mi.GO.Yeast) str(mi.GO.Yeast) ### consolidate (reduce) the number of attributes by removing attributes with very few entities ### and removing ones very corelated (i.e. with high mutual information) Yeast.GO.assocs.cons <- consolidate_entity_attribute(entity.attribute = Yeast.GO.assocs , min.entities.per.attr =3 , mut.inf=mi.GO.Yeast , U.limit = c(0.8, 0.1, 0.001)) #### apply clusterJudge for enntity attributes having a very low uncertainty (0.001) mi.by.swaps<-clusterJudge(clusters, entity.attribute=Yeast.GO.assocs.cons[["0.001"]] , plot.notes='Yeast clusters judged at uncertainty level 0.001 - Ref: Tavazoie S,& all `Systematic determination of genetic network architecture. Nat Genet. 1999`' , plot.saveRDS.file= 'cj.rds') ### save the plot for later use p <- readRDS('cj.rds') ### retrieve the previous plot pdf('cj.pdf'); plot(p); dev.off() ### plot on another device
calculates the 'cluster judge' z-score as defined in the reference The z-score is based on shuffling the clusters at random and calculating the total mutual information relative to the entity.attribute table. After the selecetd number of randomizations the mean MR and standard deviation SDR of the mutual information is used in the definition of the z.score = (MI- MIR)/SDR where MI is the mutual information of the original clustering. The higher the z.score the better the clustering. A box-and-wisker plot is generated that shows how far is the clustering versus random clustering based on the mutual information to the selected entitity.attribute
clusterJudge_z_score(clusters, entity.attribute, nmb.randomizations = 30, plot.saveRDS.file=NULL)clusterJudge_z_score(clusters, entity.attribute, nmb.randomizations = 30, plot.saveRDS.file=NULL)
clusters |
a named vectors of integers (or a factor). The names (or the levels of the factor) must match some (as many as possible) of the rownames of the entity.attribute table. |
entity.attribute |
data frame or matrix with 2 columns The assumption is that first column represent some 'entities' like gene names or gene ids. And the second column represents ‘attributes' of entities (for example Gene Ontology ID ’GO:0007260' which is 'tyrosine phosphorylation of STAT protein') Usually this is a consolidated entity.attribute where the attributes with very low number of entities or with very low mutual information have been removed (see consolidate_entity_attribute and the definition of Uncertainty on attributes mutual information) |
nmb.randomizations |
number of randomization iterations |
plot.saveRDS.file |
if not NULL must be a string represented a file location where the plot will be saved as an RDS object. The plot can be then retrieved at any time using readRDS function. |
a data.frame with the number of randomization shuffles and the total mutual information calculated after each of the shuffles
a dot is printed on the console after each randomization (shuffling) step
Adrian Pasculescu
Gibbons, F.D. and Roth F.P., (2002) Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation. Genome Research, vol. 12, pp1574-1581.
library('yeastExpData') data(ccyclered) clusters <- ccyclered$Cluster ### convert from Gene names to the new standard of Saccharomyces Genome Database (SGD) gene ids ccyclered$SGDID <- sub('^S','S00',ccyclered$SGDID) names(clusters) <- ccyclered$SGDID data(Yeast.GO.assocs) #### obtain associations and consolidate them at uncertainty level 0.001 Yeast.GO.assocs.cons <- consolidate_entity_attribute(entity.attribute = Yeast.GO.assocs , min.entities.per.attr =3 , mut.inf=mi.GO.Yeast , U.limit = c(0.001)) #### calculate z.scores for the associations consolidated at 0.001 Uncertainty level z.scores <- clusterJudge_z_score(clusters , entity.attribute = Yeast.GO.assocs.cons[["0.001"]] , nmb.randomizations=30)library('yeastExpData') data(ccyclered) clusters <- ccyclered$Cluster ### convert from Gene names to the new standard of Saccharomyces Genome Database (SGD) gene ids ccyclered$SGDID <- sub('^S','S00',ccyclered$SGDID) names(clusters) <- ccyclered$SGDID data(Yeast.GO.assocs) #### obtain associations and consolidate them at uncertainty level 0.001 Yeast.GO.assocs.cons <- consolidate_entity_attribute(entity.attribute = Yeast.GO.assocs , min.entities.per.attr =3 , mut.inf=mi.GO.Yeast , U.limit = c(0.001)) #### calculate z.scores for the associations consolidated at 0.001 Uncertainty level z.scores <- clusterJudge_z_score(clusters , entity.attribute = Yeast.GO.assocs.cons[["0.001"]] , nmb.randomizations=30)
Calculates the mutual information between every pair of attributes and the uncertainty level (as defined in the reference ...) Plots the distribution of the uncertainty and the selected levels of uncertainty that are used as filteres.
consolidate_entity_attribute(entity.attribute, min.entities.per.attr, mut.inf=NULL , U.limit = c(0.8, 0.6, 0.4, 0.2, 0.1, 0.01, 0.001) , plot.saveRDS.file=NULL)consolidate_entity_attribute(entity.attribute, min.entities.per.attr, mut.inf=NULL , U.limit = c(0.8, 0.6, 0.4, 0.2, 0.1, 0.01, 0.001) , plot.saveRDS.file=NULL)
entity.attribute |
data frame or matrix with 2 columns The assumption is that first column represent some 'entities' like gene names or gene ids. And the second column represents ‘attributes' of entities (for example Gene Ontology ID ’GO:0007260' which is 'tyrosine phosphorylation of STAT protein') Usually this is a consolidated entity.attribute where the attributes with very low number of entities or with very low mutual information have been removed (see consolidate_entity_attribute and the definition of Uncertainty on attributes mutual information) |
min.entities.per.attr |
a number or NULL |
mut.inf |
the mutual information square matrix before applying any filteres based on the uncertainty or NULL |
U.limit |
a numerical vector |
plot.saveRDS.file |
if not NULL must be a string represented a file location where the plot will be saved as an RDS object. The plot can be then retrieved at any time using readRDS function. |
a data frame (if mut.inf argument is null) or a list of data frames. The data frames are the 'consolidated' entity.attribute structures. Where the consolidation of attributes is based on a minimum number of entities per attribute or on the mutual information between attributes.
Adrian Pasculescu
Gibbons, F.D. and Roth F.P., (2002) Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation. Genome Research, vol. 12, pp1574-1581.
data(Yeast.GO.assocs) data(mi.GO.Yeast) Yeast.GO.assocs.cons <- consolidate_entity_attribute(entity.attribute = Yeast.GO.assocs , min.entities.per.attr =3 , mut.inf=mi.GO.Yeast ,U.limit = c(0.01, 0.001) ,plot.saveRDS.file='this_plot.rds') ### save also the plot str(Yeast.GO.assocs.cons) pdf('my_plot.pdf') ### place the plot saved by consolidate_entity_attribute into a pdf file plot(readRDS('this_plot.rds')) ### dev.off()data(Yeast.GO.assocs) data(mi.GO.Yeast) Yeast.GO.assocs.cons <- consolidate_entity_attribute(entity.attribute = Yeast.GO.assocs , min.entities.per.attr =3 , mut.inf=mi.GO.Yeast ,U.limit = c(0.01, 0.001) ,plot.saveRDS.file='this_plot.rds') ### save also the plot str(Yeast.GO.assocs.cons) pdf('my_plot.pdf') ### place the plot saved by consolidate_entity_attribute into a pdf file plot(readRDS('this_plot.rds')) ### dev.off()
converts the Saccharomyces Genome Database (SGD) Gene Id into the systematic name of the gene
convert_Yeast_SGDId_2_systematic(SGD.Ids = c("S000007287", "S000007287", "S000007287"))convert_Yeast_SGDId_2_systematic(SGD.Ids = c("S000007287", "S000007287", "S000007287"))
SGD.Ids |
a vector of characters. Each element is a string representing a gene. |
a matrix of characters containing the input SGD gene Ids as first column and the corresponding Yeast systematic gene names as seccond column.
it requires online access to http://llama.mshri.on.ca/cgi/synergizer/serv where the conversion is performed.
It uses the httr and jsonlite R packages.
Adrian Pasculescu
Yeast - Systematic Names Assignment in http://www.yeastgenome.org/help/community
GF Berriz and FP Roth. The Synergizer service for translating gene, protein, and other biological identifiers. Bioinformatics 24(19)-2272 (2008).
converted.genes <- convert_Yeast_SGDId_2_systematic( SGD.Ids = c("S000007287", "S000007287", "S000007287")) str(converted.genes)converted.genes <- convert_Yeast_SGDId_2_systematic( SGD.Ids = c("S000007287", "S000007287", "S000007287")) str(converted.genes)
The Gene Ontology attributes are provided for example by the Saccharomyces Genome Database (SGD) as a zipped file with a wealth of information. The function filters from the file only the minimal information necessary: THe Gene Ontology attribute id (GOID) and the Yeast Gene Id (SGDID) for which the attribute applies
download_Yeast_GO_mapping( yeast.GO.url = "http://downloads.yeastgenome.org/curation/literature/gene_association.sgd.gaf.gz")download_Yeast_GO_mapping( yeast.GO.url = "http://downloads.yeastgenome.org/curation/literature/gene_association.sgd.gaf.gz")
yeast.GO.url |
The web site address where the Yeast genes and their Gene Ontology attribute files is located. |
Only the unique associations between genes and their attributes are kept. In the original downloaded file there might be redundanies due to the different types of evidences used in the associations.
The downloaded file is expected to be in a tab delimited format with comment lines that start with the exclamation (!) caharacter. The commented lines are ignored. The expected column names can be obrtained by looking at the function code.
a data.frame with two columns: SGDID - the ids of the Yeast gene names (these are the 'entities') GOID - the ids of the Gene Ontology attributes corresponding to the above genes
since the download is time consuming, this package provides also as a dataset called Yeast.GO.assocs containing the associations already downloaded as of on Fenruary 2017.
For other species one can use specialized R and Bioconductor packages such as biomaRt (see the commented part in the example section).
Adrian Pasculescu
http://www.yeastgenome.org/ http://www.geneontology.org/
Yeast.GO.assocs
help, ~~~
Yeast.GO.assocs <- download_Yeast_GO_mapping() # For other species one can use specialized R and Bioconductor packages such as biomaRt # as in the following `toy` commented example: #library(biomaRt) #rn <- useDataset("rnorvegicus_gene_ensembl", mart=useMart("ensembl")) ### exemplify for a limited set of genes #rgd.symbol=c("As3mt", "Borcs7", "Cyp17a1", "Wbp1l", "Sfxn2", "Arl3") #entity.attr=getBM(attributes=c('rgd_symbol','go_id'), filters='rgd_symbol', values=rgd.symbol, mart=rn)Yeast.GO.assocs <- download_Yeast_GO_mapping() # For other species one can use specialized R and Bioconductor packages such as biomaRt # as in the following `toy` commented example: #library(biomaRt) #rn <- useDataset("rnorvegicus_gene_ensembl", mart=useMart("ensembl")) ### exemplify for a limited set of genes #rgd.symbol=c("As3mt", "Borcs7", "Cyp17a1", "Wbp1l", "Sfxn2", "Arl3") #entity.attr=getBM(attributes=c('rgd_symbol','go_id'), filters='rgd_symbol', values=rgd.symbol, mart=rn)
The full description of Gene Ontology attributes are provided for example by the Saccharomyces Genome Database (SGD).
download_Yeast_Go_terms( url.GO.terms = "http://downloads.yeastgenome.org/curation/literature/go_terms.tab")download_Yeast_Go_terms( url.GO.terms = "http://downloads.yeastgenome.org/curation/literature/go_terms.tab")
url.GO.terms |
The web site address where the description of Gene Ontology attribute files is located. |
The file is expected to be in tab delimited format having the following columns: 'GOID' ,'GO_Term', 'GO_Aspect', 'GO_Term_Definition'
a data.frame containing the information provided by the Gene Ontology consortium
the function is optional. It only helps user in understanding what is the meaning of the GO attributes for the Yeast examples.
Adrian Pasculescu
http://www.yeastgenome.org/ http://www.geneontology.org/
GO.terms <- download_Yeast_Go_terms() str(GO.terms)GO.terms <- download_Yeast_Go_terms() str(GO.terms)
Plots the denisty distribution of the number of entities per attribute and shows what is the number of attributes proposed to be igonored (and the number of attributes that will be kept)
entities_attribute_stats(entity.attribute , min.entities.per.attr = NULL , entity.space.name = "Yeast genes" , attribute.space.name = "Gene Ontology" , plot.saveRDS.file=NULL)entities_attribute_stats(entity.attribute , min.entities.per.attr = NULL , entity.space.name = "Yeast genes" , attribute.space.name = "Gene Ontology" , plot.saveRDS.file=NULL)
entity.attribute |
data frame or matrix with 2 columns The assumption is that first column represent some 'entities' like gene names or gene ids. And the second column represents ‘attributes' of entities (for example Gene Ontology ID ’GO:0007260' which is 'tyrosine phosphorylation of STAT protein') |
min.entities.per.attr |
a number : the minimum number of entities per attribute accepted |
entity.space.name |
a string that will be presented on the plot representing the meaning of the entities |
attribute.space.name |
a string that will be presented on the plot representing the meaning of the attributes |
plot.saveRDS.file |
if not NULL must be a string represented a file location where the plot will be saved as an RDS object. The plot can be then retrieved at any time using readRDS function. |
The attributes that appear only on once or just a in very few entities do not bring additional information. In general there are many such 'non-informative' attributes. Thus it's good to know the proportion of attributes that will be still kept if we impose a minimum number of entities per attribute.
a number: wich is either the input value of the min.entities.per.attr or, in case min.entities.per.attr is null, a proposed min.entities.per.attr threshold. The assumption is that attributes characterizing juts one entity are the most frequent. The proposed threshold is the minimum number of entities per attribute whose frequency matches 1/3 of the above maximum frequency.
Adrian Pasculescu
Gibbons, F.D. and Roth F.P., (2002) Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation. Genome Research, vol. 12, pp1574-1581.
data(Yeast.GO.assocs) min.entities.per.attr <- entities_attribute_stats(entity.attribute= Yeast.GO.assocs , min.entities.per.attr=NULL , entity.space.name='Yeast genes' , attribute.space.name='Gene Ontology')data(Yeast.GO.assocs) min.entities.per.attr <- entities_attribute_stats(entity.attribute= Yeast.GO.assocs , min.entities.per.attr=NULL , entity.space.name='Yeast genes' , attribute.space.name='Gene Ontology')
precalculated mutual information between Gene Ontology attributes of Yeast genes
data("mi.GO.Yeast")data("mi.GO.Yeast")
The format is: num [1:2266, 1:2266] NA NA NA NA NA NA NA NA NA NA ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:2266] "GO:0000001" "GO:0000002" "GO:0000009" "GO:0000011" ... ..$ : chr [1:2266] "GO:0000001" "GO:0000002" "GO:0000009" "GO:0000011" ...
for convenienece this data set was pre-generated using attribute_mut_inf from this package
This data loads a symetric matrix of mutual information values calculated between pairs of Gene Ontology attributes of Yeast genes
Wikipedia: Mutual Information https://en.wikipedia.org/wiki/Mutual_information
data(mi.GO.Yeast) str(mi.GO.Yeast)data(mi.GO.Yeast) str(mi.GO.Yeast)
verifies if the input structure has two columns, if there are no NAs or NULLs and if thre are no duplicated associations
validate_association(entity.attribute, message = TRUE)validate_association(entity.attribute, message = TRUE)
entity.attribute |
data frame or matrix with 2 columns The assumption is that first column represent some 'entities' like gene names or gene ids. And the second column represents 'attributes' of entities (for example Gene Ontology ID 'GO:0007260' which is 'tyrosine phosphorylation of STAT protein') |
message |
a logical. If set to TRUE will print a message in case of succesfull validation. |
Returns TRUE and print "Validation OK!" message in case of scuccess. Otherwise stops with an explanatory message.
Adrian Pasculescu
Gibbons, F.D. and Roth F.P., (2002) Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation. Genome Research, vol. 12, pp1574-1581.
data(Yeast.GO.assocs) validate_association(Yeast.GO.assocs)data(Yeast.GO.assocs) validate_association(Yeast.GO.assocs)
Gene Ontology attributes associated to Yeast Gene entities
data("Yeast.GO.assocs")data("Yeast.GO.assocs")
A data frame with 70487 observations on the following 2 variables.
SGDIDa character vector
GOIDa character vector
This data loads a data frame with two columns: Yeast gene ids and their Gene Ontology ids
Ashburner et al. Gene ontology: tool for the unification of biology (2000) Nat Genet 25(1):25-9.
The Gene Ontology Consortium. Gene Ontology Consortium: going forward. (2015) Nucl Acids Res 43 Database issue D1049-D1056.
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