Trial account

Please note that the trial account enables you to test all the functions of the disgenet2r package, but the queries to DISGENET database have the following restrictions:

• Only the top-30 results ordered by descending DISGENET score are returned (pagination is not supported).

• Multiple-entity queries support at most 10 entities (genes, diseases, variants).

• The access to DISGENET with a TRIAL account will expire after 7 days from the day of activation.

Other plans

There are limits in place for the disgenet2r package to ensure smooth performance for all users. These limits apply to academics, advanced, and premium users, mirroring the limits of the DISGENET REST API.

Here’s a breakdown of the limitations:

• A maximum of 100 pages of results are returned.

• Multiple-entity queries support at most 100 entities (genes, diseases, variants).

Important Note: The package will display a warning message if you exceed these limits.

Recommendations for Efficient Use:

To improve performance and avoid exceeding limits, consider querying with smaller batches of entities. You can also use disgenet metrics and annotations to refine your search and reduce the number of returned results.

Searching by gene

The gene2disease function retrieves the GDAs in DISGENET for a given gene, or a for a list of genes. The gene(s) can be identified by either the NCBI gene identifier, or the official Gene Symbol, and the type of identifier used must be specified using the parameter vocabulary. By default, vocabulary = "HGNC". To switch to Entrez NCBI Gene identifiers, set vocabulary to ENTREZ.

The function also requires the user to specify the source database using the argument database. By default, all the functions in the disgenet2r package use as source database CURATED, which includes GDAs from PsyGeNET, ClinGen, ClinVar, MGD Human data, UniProt, and Orphanet.

The information can be filtered using the DISGENET score. The argument score consists of a range of score to perform the search. The score is entered as a vector which first position is the initial value of score, and the second argument is the final value of score. Both values will always be included. By default, score=c(0,1).

In the example, the query for the Leptin Receptor (Gene Symbol LEPR, and Entrez NCBI Identifier 3953) is performed in the curated data in DISGENET.

results <- gene2disease( gene = 3953, vocabulary = "ENTREZ",
                       database = "CURATED")

The function gene2disease produces an object DataGeNET.DGN that contains the results of the query.

class(results)

## [1] "DataGeNET.DGN"
## attr(,"package")
## [1] "disgenet2r"

Type the name of the object to display its attributes: the input parameters such as whether a single entity, or a list were searched (single or list), the type of entity (gene-disease), the selected database (CURATED), the score range used in the search (0-1), and the gene NCBI identifier (3953).

results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        gene-disease 
##  . Database:     CURATED 
##  . Score:        0-1 
##  . Term:        3953 
##  . Results:  67

To obtain the data frame with the results of the query

tab <- results@qresult
head( tab, 3 )

##   gene_symbol geneid       ensemblid   geneNcbiType geneDSI geneDPI    genepLI
## 1        LEPR   3953 ENSG00000116678 protein-coding   0.412   0.957 8.8607e-05
## 2        LEPR   3953 ENSG00000116678 protein-coding   0.412   0.957 8.8607e-05
## 3        LEPR   3953 ENSG00000116678 protein-coding   0.412   0.957 8.8607e-05
##       uniprotids protein_classid protein_class_name
## 1 Q4G138, P48357    DTO_05007599          Signaling
## 2 Q4G138, P48357    DTO_05007599          Signaling
## 3 Q4G138, P48357    DTO_05007599          Signaling
##                    disease_name diseaseType diseaseUMLSCUI
## 1                       Obesity     disease       C0028754
## 2 Adult-Onset Diabetes Mellitus     disease       C0011860
## 3             Diabetes Mellitus     disease       C0011849
##                                                                            diseaseClasses_MSH
## 1 Nutritional and Metabolic Diseases (C18), Pathological Conditions, Signs and Symptoms (C23)
## 2                   Nutritional and Metabolic Diseases (C18), Endocrine System Diseases (C19)
## 3                   Nutritional and Metabolic Diseases (C18), Endocrine System Diseases (C19)
##       diseaseClasses_UMLS_ST
## 1 Disease or Syndrome (T047)
## 2 Disease or Syndrome (T047)
## 3 Disease or Syndrome (T047)
##                                        diseaseClasses_DO
## 1                        disease of metabolism (0014667)
## 2 genetic disease (630), disease of metabolism (0014667)
## 3 genetic disease (630), disease of metabolism (0014667)
##                                                                           diseaseClasses_HPO
## 1                                                                 Growth abnormality (01507)
## 2 Abnormality of metabolism/homeostasis (01939), Abnormality of the endocrine system (00818)
## 3 Abnormality of metabolism/homeostasis (01939), Abnormality of the endocrine system (00818)
##   numCTsupportingAssociation
## 1                          7
## 2                          0
## 3                          1
##                                                                                                                                                                                                                                                                                                                                                                              chemicalsIncludedInEvidence
## 1 C0041984, C0039601, C0014942, C0245514, C0028128, C0019392, C0076275, C0017986, C0039286, C0045811, C0002006, C1145760, C1135174, uridine, testosterone, estrone, troglitazone, nitric oxide, hesperidin, orlistat, glycyrrhetinic acid, tamoxifen, 2-amino-1-methyl-6-phenylimidazo(4,5-b)pyridine, aldosterone, treprostinil, H-Indol-2-one, 3-((3,5-dimethyl-1H-pyrrol-2-yl)methylene)-1,3-dihydro-
## 2                                                                                                                                                                                                                                                                                                                        C0041984, C1504945, C0038432, C0021936, uridine, INO-1001, streptozocin, inulin
## 3                                                                                                                                                                                                                                                         C0039601, C0025598, C0038432, C0028193, C0001041, C1307704, testosterone, metformin, streptozocin, nitroprusside, acetylcholine, RUBOXISTAURIN
##                                                                                                                                                                                                                    numberPmidsWithChemsIncludedInEvidenceBySource
## 1 ALL, CURATED, INFERRED, MODELS, PSYGENET, ORPHANET, CLINGEN, UNIPROT, HPO, GWASCAT, CLINVAR, TEXTMINING_HUMAN, PHEWASCAT, TEXTMINING_MODELS, MGD_HUMAN, MGD_MOUSE, RGD_HUMAN, RGD_RAT, CLINICALTRIALS, 11, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 6, 0, 1, 2, 0, 0
## 2  ALL, CURATED, INFERRED, MODELS, PSYGENET, ORPHANET, CLINGEN, UNIPROT, HPO, GWASCAT, CLINVAR, TEXTMINING_HUMAN, PHEWASCAT, TEXTMINING_MODELS, MGD_HUMAN, MGD_MOUSE, RGD_HUMAN, RGD_RAT, CLINICALTRIALS, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0
## 3  ALL, CURATED, INFERRED, MODELS, PSYGENET, ORPHANET, CLINGEN, UNIPROT, HPO, GWASCAT, CLINVAR, TEXTMINING_HUMAN, PHEWASCAT, TEXTMINING_MODELS, MGD_HUMAN, MGD_MOUSE, RGD_HUMAN, RGD_RAT, CLINICALTRIALS, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0
##   score yearInitial yearFinal evidence_level evidence_index diseaseid
## 1   1.0        1986      2023             NA      0.8634538  C0028754
## 2   0.9        2010      2016             NA      0.9112903  C0011860
## 3   0.9        2003      2003             NA      0.8260870  C0011849

The same query can be performed using the Gene Symbol (LEPR) and the data source (TEXTMINING_HUMAN). Notice how the number of diseases associated to the Leptin Receptor has increased.

results <- gene2disease( gene = "LEPR",
                        vocabulary = "HGNC",
                       database = "TEXTMINING_HUMAN" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        gene-disease 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        LEPR 
##  . Results:  420

The same query can be performed using the ENSEMBL gene identifier of the LEPR gene (ENSG00000116678) by setting the vocabulary to ENSEMBL.

results <- gene2disease( gene = "ENSG00000116678",
                        vocabulary = "ENSEMBL",
                       database = "TEXTMINING_HUMAN" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        gene-disease 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        ENSG00000116678 
##  . Results:  420

Additionally, a minimum threshold for the score can be defined. In the example, a cutoff of score=c(0.3,1) is used. Notice how the number of diseases associated to the Leptin Receptor drops when the score is restricted.

results <- gene2disease( gene = "LEPR",
                        vocabulary = "HGNC",
                       database = "ALL",
                       score =c(0.3,1))
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        gene-disease 
##  . Database:     ALL 
##  . Score:        0.3-1 
##  . Term:        LEPR 
##  . Results:  93

In Table 2 are shown the top 20 diseases associated to the LEPR gene

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score", "yearInitial", "yearFinal")] )
knitr::kable(tab[1:10,], caption = "Top diseases associated to LEPR" ) 

Visualizing the diseases associated to a single gene

The disgenet2r package offers two options to visualize the results of querying a single gene in DISGENET: a network showing the diseases associated to the gene of interest (Gene-Disease Network), and a network showing the MeSH Disease Classes of the diseases associated to the gene (Gene-Disease Class Network). These graphics can be obtained by changing the class argument in the plot function.

By default, the plot function produces a Gene-Disease Network on a DataGeNET.DGN object (Figure 1). In the Gene-Disease Network the blue nodes are diseases, the pink nodes are genes, and the width of the edges is proportional to the score of the association. The prop parameter allows to adjust the size of the nodes, while the eprop parameter adjusts the width of the edges while keeping the proportionality to the score.

plot( results,
      type = "Network",
      prop = 20, eprop =5, verbose = T)

Figure 1: The Gene-Disease Network for the Leptin Receptor gene

Use interactive = TRUE to display an interactive plot (Figure 2).

plot( results,
      type = "Network",
       interactive = TRUE)

Figure 2: The interactive Gene-Disease Network for the Leptin Receptor gene

The results can also be visualized in a network in which diseases are grouped by the MeSH Disease Class if the class argument is set to DiseaseClass (Gene-Disease Class Network, Figure 3). In the Gene-Disease Class Network, the node size of is proportional to the fraction of diseases in the disease class, with respect to the total number of diseases with disease classes associated to the gene. In the example, the Leptin Receptor is associated mainly to Nutritional and Metabolic Diseases. There are 2 diseases in the example that do not have annotations to MeSH disease class (shown as a warning).

plot( results,
      class = "DiseaseClass",
       interactive=T, verbose = T)

Figure 3: The Disease Class Network for the Leptin Receptor Gene

results <- gene2attribute( gene  = "3953", vocabulary = "ENTREZ"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        gene 
##  . Database:     ALL 
##  . Score:         
##  . Term:        3953

tab <-results@qresult
knitr::kable(tab, caption = "Gene attributes for LEPR") 

results <- gene2evidence( gene = "LEPR", vocabulary = "HGNC",
                        disease ="UMLS_C3554225", database = "ALL")
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        gene-evidence 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        LEPR 
##  . Results:  18

tab <- results@qresult
tab <-  tab %>%
  filter(reference_type == "PMID") %>%
  select(reference, associationType, pmYear, sentence) %>% arrange(desc(pmYear))

tab <- tab %>% dplyr::rename(Year=pmYear, Sentence = sentence, pmid = reference)

tab %>%  dplyr::mutate(  pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) ) ) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Evidences supporting the association between LEPR & LEPTIN RECEPTOR DEFICIENCY" ) 

results <- gene2evidence( gene = "LEPR", vocabulary = "HGNC",
                        database = "ALL", score=c(0.7,1) )
plot(results, type="Points",   interactive=T, limit=10000)

Searching multiple genes

The gene2disease function can also receive as input a list of genes, either as Entrez NCBI Gene Identifiers or Gene Symbols. In the example, we show how to create a vector with the Gene Symbols of several genes belonging to the family of voltage-gated potassium channels (Table 5) and then, we apply the function gene2disease.

Creating the vector with the list of genes belonging to the voltage-gated potassium channel family.

myListOfGenes <- c( "KCNE1", "KCNE2", "KCNH1", "KCNH2", "KCNG1")

The gene2disease function also requires the user to specify the source database using the argument database, and optionally, the DISGENET score can also be applied to filter the results.

results <- gene2disease(
  gene     = myListOfGenes,
 database = "ALL",
 score =c(0.5, 1),
  verbose  = TRUE
)

## Your query has 1 page.

## Warning in gene2disease(gene = myListOfGenes, database = "ALL", score = c(0.5, : 
##  One or more of the genes in the list is not in DISGENET ( 'ALL' ):
##    - KCNG1

results

## Object of class 'DataGeNET.DGN'
##  . Search:      list 
##  . Type:        gene-disease 
##  . Database:     ALL 
##  . Score:        0.5-1 
##  . Term:       KCNE1 ... KCNH2 
##  . Results:  42

In Table 6, the top 20 diseases associated to the list of genes belonging to the voltage-gated potassium channel family.

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score", "yearInitial", "yearFinal")] )

knitr::kable(tab[1:10,], caption = "Top GDAs for the list of genes belonging to the voltage-gated potassium channel family") 

Visualizing the diseases associated to multiple genes

By default, plotting a DataGeNET.DGN resulting of the query with a list of genes produces a Gene-Disease Network where the blue nodes are diseases, the pink nodes are genes, and the width of the edges is proportional to the score of the association (Figure 5).

plot( results,
      type = "Network",
      prop = 10, verbose = T)

Figure 5: The Gene-Disease Network for a list of genes belonging to the voltage-gated potassium channel family

Set the argument interactive = TRUE to see an interactive network (Figure 6).

plot( results,
      type = "Network",
      prop = 10,  interactive=TRUE)

Figure 6: The interactive Gene-Disease Network for a list of genes belonging to the voltage-gated potassium channel family

Setting the argument type to Heatmap produces a Gene-Disease Heatmap (Figure 7), where the scale of colors is proportional to the score of the GDA. The argument limit can be used to limit the number of rows to the top scoring GDAs. The argument nchars can be used to limit the length of the name of the disease. By default, the plot shows the 50 highest scoring GDAs.

plot( results,
      type  ="Heatmap",
      limit  = 100, nchars = 60, interactive =T, verbose = T)

Figure 7: The Gene-Disease Heatmap for a list of genes belonging to the voltage-gated potassium channel family

These results can also be visualized as a Gene-Disease Class Heatmap by setting the argument type to Heatmap and class to DiseaseClass (Figure 8). In this case, diseases are grouped by the their MeSH disease classes, and the color scale is proportional to the percentage of diseases in each MeSH disease class. In the example, genes are associated mainly to Cardiovascular Diseases, and to Congenital, Hereditary, and Neonatal Diseases and Abnormalities.

plot( results, type="Heatmap",
      class="DiseaseClass", nchars=60, interactive =T)

Figure 8: The Gene-Disease Class Heatmap for a list of genes belonging to the voltage-gated potassium channel family

Alternative, set the arguments type to Network and class to DiseaseClass to generate a Gene-Disease Class Network (Figure 9).

plot( results, type="Network",
      class="DiseaseClass", nchars=60, interactive =T)

Figure 9: The Gene-Disease Class Network for a list of genes belonging to the voltage-gated potassium channel family

results <- gene2evidence(gene     = myListOfGenes, 
                       database = "TEXTMINING_HUMAN", verbose  = TRUE)

## Your query has 24 pages.

plot(results, type="Points",   interactive=T, limit=10000)

results <- gene2evidence(gene     = c("IL3", "IL4", "IL5", "IL6", "IL0"), 
                       database = "CLINICALTRIALS", verbose  = TRUE )

## Your query has 106 pages.
## Notice that your query has a maximum of 106 pages.
## By using the default n_pags (100), your query of 106 pages has been reduced to 100 pages.

## Warning in gene2evidence(gene = c("IL3", "IL4", "IL5", "IL6", "IL0"), database = "CLINICALTRIALS", : 
##  One or more of the genes in the list is not in DISGENET ( 'CLINICALTRIALS' ):
##    - IL0

plot(results, type="Points",   interactive=T, limit=10000)

Searching by gene and chemical

You can search GDAs by chemicals by specifying a chemical identifier using the chemical filter in the gene2disease function. Table 7 shows the diseases associated to LEPR associated to metformin.

results <- gene2disease( gene = "LEPR", vocabulary = "HGNC",
                       database = "TEXTMINING_HUMAN", 
                       chemical = "C0025598" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        gene-disease 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        LEPR 
##  . Results:  4

tab <- results@qresult
tab <-tab%>% dplyr::select(chemical_name, gene_symbol, disease_name,  score)
knitr::kable(tab, caption = "GDAs for LEPR and metformin") 

Retrieving the chemicals associated to a gene

For GDAs that have a chemical annotation, we can perform a query with a gene, or list of genes, to retrieve the chemicals annotated to this associations.

results <- gene2chemical( gene  = "PDGFRA", 
                        vocabulary = "HGNC",
                          database = "TEXTMINING_HUMAN" , score = c(0.8,1))
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        gene-chemical 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0.8-1 
##  . Term:        PDGFRA 
##  . Results:  14

tab <- results@qresult
tab <-tab %>% dplyr::filter(reference_type == "PMID") %>%   dplyr::select(disease_name, chemical_name, chemical_effect,sentence, 
                           reference, pmYear)
tab <- tab %>% dplyr::rename(  Disease = disease_name, 
                             Chemical = chemical_name, `Chemical effect` =  chemical_effect,
                             Year=pmYear, Sentence = sentence, pmid = reference) %>% dplyr::arrange(desc(Year))

tab[1:10,] %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid )  )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Selection of chemicals associated to PDGFRA" ) 

To visualize the results use the plot function.

plot(results, type="Network",   interactive=T, limit=10000)

Searching by disease

The disease2gene function allows to retrieve the genes associated to a disease, or a list of diseases. The function uses as input the disease, or list of diseases of interest (each disease should have the format: IDENT_ID where IDENT is one of UMLS, ICD9CM, ICD10, MESH, OMIM, DO, EFO, NCI, HPO, MONDO, or ORDO), ID is the identifier in the vocabulary, and the database (by default, CURATED). A threshold value for the score can be set, like in the gene2disease function.

In the example, we will use the disease2gene function to retrieve the genes associated to the UMLS CUI C0036341. This function also receives as input the database, in the example, CURATED, and a score range, in the example, from 0.8 to 1.

results <- disease2gene( disease  = "UMLS_C0036341",
                          database = "CURATED",
                          score    = c( 0.8,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.8-1 
##  . Term:        UMLS_C0036341 
##  . Results:  130

In Table 9, the top 20 genes associated to UMLS CUI C0036341.

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score", "yearInitial", "yearFinal")] )
knitr::kable(tab[1:10,], caption = "Top 10 genes associated to Schizophrenia") 

plot ( results,
       prop = 10, interactive=TRUE)

plot( results,
      class="ProteinClass",
      interactive=TRUE)

results <- disease2gene( disease  = "MESH_D012559",  
                          database = "CURATED",
                          score    = c( 0.8,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.8-1 
##  . Term:        MESH_D012559 
##  . Results:  130

results <- disease2gene( disease  = "OMIM_181500",  
                          database = "CURATED",
                          score    = c( 0.8,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.8-1 
##  . Term:        OMIM_181500 
##  . Results:  130

results <- disease2gene( disease  = "ICD9CM_295",  
                          database = "CURATED",
                          score    = c( 0.8,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.8-1 
##  . Term:        ICD9CM_295 
##  . Results:  130

results <- disease2gene( disease  = "NCI_C3362", 
                          database = "CURATED",
                          score    = c( 0.8,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.8-1 
##  . Term:        NCI_C3362 
##  . Results:  130

results <- disease2gene( disease  = "HPO_HP:0100753", 
                          database = "CURATED",
                         score    = c( 0.8,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.8-1 
##  . Term:        HPO_HP:0100753 
##  . Results:  130

Searching by disease and chemical

You can filter the results to find associations that are mentioned in the context of a chemical, like the example below.

results <- disease2gene( disease  = "UMLS_C0006142", chemical = "C0039286",
                          database = "ALL" , n_pags = 1 )

## Notice that your query has a maximum of 9 pages.
## By indicating n_pags = 1, your query of 9 pages has been reduced to 1 pages.

results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-gda 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        UMLS_C0006142 
##  . Results:  100

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score", "chemical_name", "chemicalid")] )%>% dplyr::arrange(desc(score))
knitr::kable(tab[1:10,], caption = "Top GDAs associated to breast cancer") 

Retrieving the chemicals associated to a disease

For GDAs that have a chemical annotation, we can perform a query with a disease, or list of disease, to retrieve the chemicals annotated to this associations.

results <- disease2chemical( disease = "UMLS_C0010674", 
                           database = "TEXTMINING_MODELS" , score = c(0.8,1))
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-chemical 
##  . Database:     TEXTMINING_MODELS 
##  . Score:        0.8-1 
##  . Term:        UMLS_C0010674 
##  . Results:  19

tab <- results@qresult
tab <-tab %>% dplyr::filter(reference_type =="PMID") %>% dplyr::select(gene_symbol, chemical_name,chemical_effect ,sentence, reference, pmYear) 
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Chemical = chemical_name,
                          `Chemical Effect`=chemical_effect ,   Year=pmYear, Sentence = sentence, pmid = reference)   %>% dplyr::arrange(desc(Year))

tab[1:10,] %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid)    )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Top chemicals associated to Cystic Fibrosis" ) 

To visualize the results use the plot function.

plot(results, type="Network",   interactive=T, limit=50)

results <- disease2attribute( disease  = "UMLS_C0036341"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease 
##  . Database:     ALL 
##  . Score:         
##  . Term:        UMLS_C0036341 
##  . Results:  12

tab <- unique(results@qresult )
knitr::kable(tab[1:10,], caption = "Disease attributes for Schizophrenia") 

Retrieving the UMLS CUIs via other vocabularies

It is possible to obtain the CUIs that map to an identifier of interest (example, ICD9CM, MSH, or OMIM) using the the get_umls_from_vocabulary function.

results <- get_umls_from_vocabulary(
            disease  = "MSH_D012559",  vocabulary = "MSH" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease 
##  . Database:     ALL 
##  . Score:         
##  . Term:        MSH_D012559 
##  . Results:  2

The results are shown in Table 13.

tab <-results@qresult
knitr::kable(tab, caption = "Retrieving the UMLS CUI from MeSH", row.names=F) 

Finding the CUI associated to the name of a disease of interest

It is possible to obtain the CUIS that correspond to a disease(s) of interest using the the get_umls_from_vocabulary function. For that, we should specify the parameter vocabulary = "NAME". Use the the parameter limit to change the number of CUIs that are retrieved.

results <- get_umls_from_vocabulary(
  disease  = "long QT",  vocabulary = "NAME" ,  limit =10)
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease 
##  . Database:     ALL 
##  . Score:         
##  . Term:        long QT 
##  . Results:  10

The results are shown in Table 14.

tab <-results@qresult
knitr::kable(tab, caption = "List of CUIs that map to long QT", row.names = F) 

results <- disease2evidence( disease  = "UMLS_C0036341",
                           type = "GDA",
                          database = "CURATED",
                          score    = c( 0.8,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-evidence 
##  . Database:     CURATED 
##  . Score:        0.8-1 
##  . Term:        UMLS_C0036341 
##  . Results:  369

tab <- results@qresult
tab <-tab[tab$reference_type == "PMID" & tab$pmYear > 2013 & tab$source =="PSYGENET", ] 
tab <- tab[ order(-tab$pmYear), c("gene_symbol","source", "associationType", "sentence", "reference", "pmYear")][1:5,]
tab <- tab %>% dplyr::rename(Gene = gene_symbol,  Year=pmYear, Sentence = sentence, pmid = reference)

tab %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid)    )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Evidences supporting the association for Schizophrenia" )  

results <- disease2evidence( disease  = "UMLS_C0036341",
                           gene = c("DRD2", "DRD3"),
                           type = "GDA",
                          database = "ALL",
                          score    = c( 0.5,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-evidence 
##  . Database:     ALL 
##  . Score:        0.5-1 
##  . Term:        UMLS_C0036341 
##  . Results:  565

tab <- results@qresult
tab <-  tab %>%
    filter(reference_type == "PMID") %>%
    select(gene_symbol, associationType, reference, sentence, pmYear) %>% arrange(desc(pmYear)) %>% head(10)
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Year=pmYear, Sentence = sentence, pmid = reference)
tab %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Evidences supporting the association between C0036341 & DRD2,DRD3" )  

Searching multiple diseases

The disease2gene function also accepts as input a list of diseases (each disease should have the format: IDENT_ID where IDENT is one of UMLS, ICD9CM, ICD10, MESH, OMIM, DO, EFO, NCI, HPO, MONDO, or ORDO), the database (by default, CURATED), and optionally, a value range for the score. In the example, we have selected a list of 10 diseases. Table 17 shows the UMLS CUIs and the corresponding disease names.

Creating the vector with the list of diseases.

diseasesOfInterest <- paste0("UMLS_",c("C0036341", "C0002395", "C0030567","C0005586"))

In the example, we will search in CURATED data, using a score range of 0.8-1.

results <- disease2gene(
  disease = diseasesOfInterest,
  database = "CURATED",
  score =c(0.8,1),
  verbose  = TRUE )

## Your query has 3 pages.

In table 18, the top 20 genes associated to the list of diseases.

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score", "yearInitial", "yearFinal")] ) %>% dplyr::arrange(desc(score),(yearInitial))
knitr::kable(tab[1:10,], caption = "Top Genes associated to a list of diseases") 

Visualizing the genes associated to multiple diseases

The default plot of the results of querying DISGENET with a list of diseases produces a Gene-Disease Network where the blue nodes are diseases, the pink nodes are genes, and the width of the edges is proportional to the score of the association (Figure 16).

plot( results,
      type = "Network",
      prop = 10, interactive=T)

Figure 16: The Gene-Disease Network associated to a list of diseases

To visualize the results as a Gene-Disease Heatmap (Figure 17) change the argument class to “Heatmap”. In this plot, the scale of colors is proportional to the score of the GDA. The argument limit can be used to limit the number of rows to the top scoring GDAs when the results are large. By default, the plot shows the 50 highest scoring GDAs.

plot( results,
      type="Heatmap",
      limit =20,
      cutoff=0.2, interactive=TRUE)

## [1] "Dataframe of 290 rows has been reduced to 20 rows."

Figure 17: The Gene-Disease Heatmap for genes associated to a list of diseases

A third visualization option is a Protein Class-Disease Heatmap (Figure 18), in which genes are grouped by protein class. This plot is obtained by setting the class argument to “ProteinClass”. In this case, the color of the heatmap is proportional to the percentage of genes for each disease in each protein class. This heatmap displays the protein classes associated to each disease.

plot( results,
      class="ProteinClass", type = "Heatmap", interactive=TRUE)

Figure 18: The Protein Class-Disease Heatmap for genes associated to a list of diseases

A Protein Class-Disease Network visualization is also possible (Figure 19).

plot( results,
      class="ProteinClass", type = "Network", interactive=TRUE)

Figure 19: The Protein Class-Disease Network for genes associated to a list of diseases

To explore the evidences supporting the associations, use the function disease2evidence.

results <- disease2evidence( disease  = diseasesOfInterest,
                           type = "GDA",
                           score=c(0.5,1),
                          database = "CURATED" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      list 
##  . Type:        disease-evidence 
##  . Database:     CURATED 
##  . Score:        0.5-1 
##  . Term:       UMLS_C0036341 ... UMLS_C0005586 
##  . Results:  3404

To visualize the results use the argument Points (Figure 20).

plot( results,  
      type = "Points", limit=10000 )

Figure 20: The Evidences plot for a list of diseases

Searching by disease and chemical

The disease2gene function can also be used to retrieve genes mentioned in the context of a specific disease and chemical (Table 19)

results <- disease2gene( disease  = "UMLS_C0030567",
                          database = "TEXTMINING_HUMAN",
                          chemical = "C0023570")
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-gda 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        UMLS_C0030567 
##  . Results:  105

tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol, disease_name, chemical_name, score) %>% dplyr::arrange(desc(score))
knitr::kable(tab[1:10,], caption = "Top GDAs associated to Parkinson and levodopa") 

To visualize the results use the function plot (Figure 20)

plot( results, interactive= T )

Retrieving the chemicals associated to a disease

To retrieve the chemicals mentioned in the GDAs involving a specific disease, we can use the disease2chemical function.

results <- disease2chemical( disease  = "UMLS_C0030567",
                          database = "TEXTMINING_HUMAN" , score = c(0.5,1))

## Notice that your query has a maximum of 136 pages.
## By using the default n_pags (100), your query of 136 pages has been reduced to 100 pages.

results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-chemical 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0.5-1 
##  . Term:        UMLS_C0030567 
##  . Results:  118

tab <- results@qresult
tab <-tab%>% dplyr::filter(reference_type == "PMID")  %>% dplyr::select(gene_symbol, chemical_name, chemical_effect, sentence, reference, pmYear)
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Chemical = chemical_name,
                    `Chemical Effect` = chemical_effect,   Year=pmYear, Sentence = sentence, pmid = reference)   %>% dplyr::arrange(desc(Year))

tab[1:10,] %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid))) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Top Chemicals associated to Parkinson" ) 

To visualize the results use the function plot

plot( results )

Figure 22: The Evidences plot for a list of diseases

Searching by variant

The variant2disease function receives a variant, or a list of variants as input, identified by the dbSNP identifier. It produces an object DataGeNET.DGN, with Type = "variant-disease".

results <- variant2disease( variant= "rs113488022",
                         database = "CURATED", score = c(0.7,1)) 
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-disease 
##  . Database:     CURATED 
##  . Score:        0.7-1 
##  . Term:        rs113488022 
##  . Results:  16

The results are shown in Table 21.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score", "yearInitial", "yearFinal")] ) %>% dplyr::arrange(desc(score), desc(yearFinal))

knitr::kable(tab[1:10,], caption = "Top diseases associated to variant rs113488022") 

plot( results, 
      type = "Network", interactive=T,
      prop  = 10)

plot(results, class="DiseaseClass" , interactive=T)

Exploring the evidences associated to a variant

You can extract the evidences associated to a particular variant using the function variant2evidence. Additionally, you can explore the evidences for a specific variant-disease pair by specifying the argument disease.

results <- variant2evidence( variant = "rs10795668",
                disease ="UMLS_C0009402",
                       database = "ALL",
                       score =c(0,1))
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-evidence 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        rs10795668 
##  . Results:  23

The results are shown in table 22.

results <- results@qresult
results <- results %>% dplyr::filter(reference_type =="PMID") %>% select(associationType, reference, pmYear, sentence) %>% head(10)
results <- results %>% dplyr::rename(Year=pmYear, Sentence = sentence, pmid=reference) %>% dplyr::arrange(desc(Year))
results %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption ="Evidences supporting the association between C0009402 & rs10795668")  

Table 22: Evidences supporting the association between C0009402 & rs10795668

associationType	pmid	Year	Sentence
GeneticVariation	26101521	2015	We focused on 6 SNPs (rs380284, rs4464148, rs4779584, rs4939827, rs6983267, and rs10795668), already described as risk markers, and tested their possible independent and combined contribution to CRC predisposition.
GeneticVariation	23712746	2013	In conclusion, CRC susceptibility variants rs9929218 and rs10795668 may exert some influence in modulating patient’s survival and they deserve to be further tested in additional CRC cohorts in order to confirm their potential as prognosis or predictive biomarkers.
GeneticVariation	23717594	2013	Results from our case-control study and the followed meta-analysis confirmed the significant association of rs10795668 with CRC risk.
GeneticVariation	24066093	2013	We genotyped four variants previously associated with CRC: rs10795668, rs16892766, rs3802842 and rs4939827.
GeneticVariation	22363440	2012	We observed an association between the low colorectal cancer risk allele (A) for rs10795668 at 10p14 and increased expression of ATP5C1 (q = 0.024) and between the colorectal cancer high risk allele (C) for rs4444235 at 14q22.2 and increased expression of DLGAP5 (q = 0.041), both in tumor samples.
GeneticVariation	22235025	2012	Risk allele carriers for rs3802842 [Odds ratio (OR) = 1.5, 95% confidence interval (CI) 1.1-2.05, P = 0.0096, dominant model) and rs4779584 (OR = 1.39, 95% CI 1.02-1.9, P = 0.0396, dominant model) were more frequent in the CRC<50 group, whereas homozygotes for rs10795668 risk allele were also more frequent in the early-onset CRC (P = 0.02, codominant model).
GeneticVariation	23359760	2012	However, no associations with CRC risk were detected for six other loci (rs9929218, rs10411210, rs12701937, rs7014346, rs6983267, and rs10795668), and one SNP, rs16892766, was not polymorphic in any of the study participants.
GeneticVariation	22045029	2012	Recent genome-wide association studies have identified single-nucleotide polymorphisms at 16 genetic loci associated with colorectal cancer risk: rs6691170 (1q41), rs10936599 (3q26.2), rs16892766 (8q23.3), rs6983267 (8q24.21), rs10795668 (10p14), rs3802842 (11q23.1), rs11169552 (12q13.13), rs4444235, rs1957636 (14q22.2), rs4779584 (15q13.3), rs9929218 (16q22.1), rs4939827 (18q21.1), rs10411210 (19q13.11), rs961253 and rs4813802 (20p12.3) and rs4925386 (20q13.33).
GeneticVariation	21402474	2011	Our data suggested that rs10795668, a CRC susceptibility variant identified by GWA studies, might be used as a biomarker to identify CRC patients with high risk of recurrence after chemotherapy.
GeneticVariation	21071539	2011	We studied the generalizability of the associations with 11 risk variants for CRC on 8q23 (rs16892766), 8q24 (rs6983267), 9p24 (rs719725), 10p14 (rs10795668), 11q23 (rs3802842), 14q22 (rs4444235), 15q13 (rs4779584), 16q22 (rs9929218), 18q21 (rs4939827), 19q13 (rs10411210), and 20p12 (rs961253) in a multiethnic sample of 2,472 CRC cases, 839 adenoma cases and 4,466 controls comprised of European American, African American, Native Hawaiian, Japanese American, and Latino men and women.

The results can be visualized using the plot function with the argument Points. This will show the number of publications per year associated to this variant. It is important to set the parameter limit to 10,000 in order to include all the results in the plot.

results <- variant2evidence( variant = "rs1800629",
                       database = "ALL",
                       score =c(0,1))
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-evidence 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        rs1800629 
##  . Results:  1877

plot( results,  
      type = "Points", limit=10000 )

Figure 25: The Evidence plot for the variant rs1800629

results <- variant2attribute( variant= "rs113488022")

results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant 
##  . Database:     ALL 
##  . Score:         
##  . Term:        rs113488022

tab <- unique(results@qresult )
tab <- tab %>% dplyr::select(-threeletterID)
knitr::kable(tab, caption = "Attributes for variant rs113488022") 

Searching multiple variants

The variant2disease function retrieves the information in DISGENET for a list of variants identified by the dbSNP identifier. The function also requires the user to specify the source database using the argument database. By default, variant2disease function uses as source database CURATED.

results <- variant2disease(
         variant = c("rs121913013", "rs1060500621",
              "rs199472709", "rs72552293",
              "rs74315445", "rs199472795"),
         database = "ALL")
results

## Object of class 'DataGeNET.DGN'
##  . Search:      list 
##  . Type:        variant-disease 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:       rs121913013 ... rs199472795 
##  . Results:  21

In table 24, the top 20 diseases associated to the list of variants.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score", "yearInitial", "yearFinal")] )%>% dplyr::arrange(desc(score), desc(yearFinal))
knitr::kable(tab[1:10,], caption = "Top diseases associated to the list of variants")  

plot( results,
      type = "Network", interactive=T)

plot( results,
      type = "Network", 
      showGenes= T,
      interactive=T)

plot( results,
      type = "Heatmap",
      prop = 10, interactive = TRUE, nchar=50)

plot( results,
      class = "DiseaseClass", interactive=T)

plot( results,  type = "Heatmap",
      class = "DiseaseClass", interactive=T)

Searching variants associated to a disease

The disease2variant function allows to retrieve the variants associated to a disease, or a list of diseases. The function uses as input the disease, or list of diseases of interest (each disease should have the format: IDENT_ID where IDENT is one of UMLS, ICD9CM, ICD10, MESH, OMIM, DO, EFO, NCI, HPO, MONDO, or ORDO) and the database (by default, CURATED). A threshold value for the score can be set, like in the gene2disease function.

results <- disease2variant(disease = c("UMLS_C1832916"),
                       database = "CLINVAR" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-variant 
##  . Database:     CLINVAR 
##  . Score:        0-1 
##  . Term:        UMLS_C1832916 
##  . Results:  152

In Table 25, the variants associated to Timothy syndrome according to ClinVar database.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score", "yearInitial", "yearFinal")] )

knitr::kable(tab[1:10,], caption = " Variants associated to Timothy syndrome according to ClinVar") 

The results can be further restricted to keep variants predicted to be deleterious by SIFT and PolyPhen scores, by passing ranges of these scores to the function, using sift and polyphen arguments, like in the example below. Remember that genetic variants with SIFT scores smaller than 0.05 are predicted to be deleterious, while values of PolyPhen greater than 0.908 are classified as Probably Damaging.

results <- disease2variant(disease = c("UMLS_C1832916"),
                       database = "CLINVAR", sift = c(0,0.05), polyphen = c(0.9,1) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-variant 
##  . Database:     CLINVAR 
##  . Score:        0-1 
##  . Term:        UMLS_C1832916 
##  . Results:  84

In Table 26, the deleterious variants associated to Timothy syndrome repored in ClinVar database.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score", "polyphen_score", "sift_score", "yearInitial", "yearFinal")] )

knitr::kable(tab[1:10,], caption = "Deleterious variants associated to Timothy syndrome according to ClinVar") 

Visualizing the variants associated to a single disease

The results of querying DISGENET variant information with a list of diseases can be visualized as a Variant-Disease Network (Figure 31).

plot( results,     
      type = "Network", interactive=T)

Figure 31: The Variant-Disease Network for a single disease

The Variant-Disease Network can be displayed as a Variant-Disease-Gene Network, by setting the showGenes parameter to TRUE (Figure 32).

plot( results, 
      type = "Network",
      showGenes = T)

Figure 32: The Variant-Gene-Disease Network for a single disease

results <- disease2evidence( disease  = "UMLS_C1832916",
                           type = "VDA",
                          database = "ALL",
                          score    = c( 0.5,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-evidence 
##  . Database:     ALL 
##  . Score:        0.5-1 
##  . Term:        UMLS_C1832916 
##  . Results:  236

results <- results@qresult
results <- results %>% dplyr::filter(reference_type =="PMID") %>%
    select(reference, associationType, pmYear, sentence) %>% arrange(desc(pmYear)) %>% head(10)
results <- results %>% dplyr::rename(Year=pmYear, Sentence = sentence, pmid = reference)
results %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption ="Evidences supporting associations") 

results <- disease2evidence( disease  = "UMLS_C1832916",
                   gene = "775", vocabulary = "ENTREZ",
                   type = "VDA",  database = "TEXTMINING_HUMAN",
                   score    = c( 0.7,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-evidence 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0.7-1 
##  . Term:        UMLS_C1832916 
##  . Results:  24

results <- results@qresult
results <- results %>% dplyr::filter(reference_type =="PMID")%>%
    select(reference, associationType, pmYear, sentence) %>% arrange(desc(pmYear))%>% head(10)

results <- results %>% dplyr::rename(Year=pmYear, Sentence = sentence, pmid = reference)
results %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption ="Selection of evidences supporting associations between C0036341 & CACNA1C") 

Searching multiple diseases

results <- disease2variant(
              disease = paste0("UMLS_",c("C3150943",  "C1859062", "C1832916", "C4015695")),
              database = "CURATED", 
              score = c(0.7, 1) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      list 
##  . Type:        disease-variant 
##  . Database:     CURATED 
##  . Score:        0.7-1 
##  . Term:       UMLS_C3150943 ... UMLS_C4015695 
##  . Results:  154

Table 29 shows the variants associated to a list of Long QT syndromes in the curated data in DISGENET.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score", "yearInitial", "yearFinal")] )

knitr::kable(tab[1:10,], caption = "Variants associated to a list of Long QT syndromes") 

plot( results,     
      type = "Network", interactive =TRUE)

plot( results,
      type = "Heatmap",
      interactive=T)

Searching by variant and chemical

results <- variant2disease( variant  = "rs121434568",
                          database = "TEXTMINING_HUMAN",
                          chemical = "C2987648")
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-disease 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        rs121434568 
##  . Results:  13

Table 30 shows the VDAs associated to rs121434568 and afatinib.

tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, disease_name, chemical_name, score)

knitr::kable(tab[1:10,], caption = "VDAs associated to rs121434568 and afatinib") 

To visualize the results use the plot function.

plot(results, type="Network",   interactive=T, limit=50)

Retrieving the chemicals associated to a variant

The variant2chemical function allows to retrieve the chemicals associated to a variant

results <- variant2chemical( variant =  "rs1801133",
                          database = "TEXTMINING_HUMAN" , score = c(0.8,1))
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-chemical 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0.8-1 
##  . Term:        rs1801133 
##  . Results:  5

tab <- results@qresult
tab <-tab%>% dplyr::select( disease_name, chemical_name, chemical_effect, sentence, reference, pmYear)
tab <- tab %>% dplyr::rename( Disease = disease_name, Chemical = chemical_name,
                        `Chemical Effect`=chemical_effect, Year=pmYear, Sentence = sentence, pmid = reference) %>% dplyr::arrange(desc(Year))

tab %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Chemicals associated to rs1801133" ) 

To visualize the results use the plot function.

plot(results, 
     type="Network",   
     interactive=T, limit=50)

Retrieving genes, variants, and diseases associated to chemicals

The chemical2gene function allows to retrieve the GDAS for a specific chemical, or list of chemicals.

results <- chemical2gene( chemical  = "C0023570" , database = "ALL" , n_pags = 5)

## Notice that your query has a maximum of 17 pages.
## By indicating n_pags = 5, your query of 17 pages has been reduced to 5 pages.

results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-gene 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        C0023570 
##  . Results:  93

tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol,gene_type , chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab[1:10,], caption = "Genes associated to levodopa") 

The results can be visualized as a Chemical-Gene Network (Figure 37).

plot( results,
      type = "Network", interactive=T)

The chemical2disease function allows to retrieve the diseases for a specific chemical, or list of chemicals, and the information cab be extracted from GDAs or VDAs. To specify from where, use the type parameter.

results <- chemical2disease( chemical  = "C0023570" , type = "GDA" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-disease 
##  . Database:     CURATED 
##  . Score:        0-1 
##  . Term:        C0023570 
##  . Results:  45

tab <- results@qresult
tab <-tab%>% dplyr::select(diseaseid, disease_name, chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab[1:10,], caption = "Diseases associated to levodopa, type GDA", align= "lllc") 

plot( results,
      type = "Network",
      interactive=T)

A DiseaseClass plot is also available.

plot( results,
      type = "Network",
      class = "DiseaseClass",
      interactive=T)

For VDAs

results <- chemical2disease( chemical  = "C0165032" , type = "VDA", database =  "ALL" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-disease 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        C0165032 
##  . Results:  5

tab <- results@qresult
tab <-tab%>% dplyr::select(diseaseid, disease_name, chemical_name, pmids_chemical)  %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab, caption = "Diseases associated to imiquimod, type VDA",  align= "lllc") 

plot( results,
      type = "Network", interactive=T)

The chemical2variant function allows to retrieve the variants for a specific chemical, or list of chemicals.

results <- chemical2variant( chemical  = "C0006949", database = "ALL"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-variant 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        C0006949 
##  . Results:  53

tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, gene_symbols, most_severe_consequence, chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab[1:10,], caption = "VDAs for carbamazepine", align= "llllc") 

The chemical2variant function can also receive as a parameter sift and polyphen scores to restrict the results to variants predicted as probably deleterious.

results <- chemical2variant( chemical  = "C0006949", database = "ALL", sift = c(0,0.05), polyphen = c(0.9,1)  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-variant 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        C0006949 
##  . Results:  14

tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, gene_symbols, sift_score, polyphen_score, chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab[1:10,], caption = "VDAs for carbamazepine", align= "llllc") 

plot( results,
      type = "Network", interactive=T)

Retrieving GDAs and VDAs associated to chemicals

results <- chemical2gda( chemical  = "C0074393", database = "ALL"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-gda 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        C0074393 
##  . Results:  151

tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol, disease_name, chemical_name, score, pmids_chemical)
knitr::kable(tab[1:10,], caption = "GDAs for sertraline") 

plot(results, type="Network",   interactive=T, limit=50)

results <- chemical2vda( chemical  = "C3264621"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-vda 
##  . Database:     CURATED 
##  . Score:        0-1 
##  . Term:        C3264621 
##  . Results:  370

results <- chemical2vda( chemical  = "C3264621", sift = c(0,0.05) , polyphen = c(0.9,1)  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-vda 
##  . Database:     CURATED 
##  . Score:        0-1 
##  . Term:        C3264621 
##  . Results:  146

tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, disease_name, chemical_name, score,pmids_chemical)
knitr::kable(tab[1:10,], caption = "VDAs associated ivacaftor") 

plot(results, type="Network",   interactive=T, limit=50)

results <- chemical2evidence( chemical  = "C0023570", type = "GDA"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-gda 
##  . Database:     CURATED 
##  . Score:        0-1 
##  . Term:        C0023570 
##  . Results:  113

tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol, disease_name, chemical_name, sentence,chemical_effect, reference, pmYear)
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Disease = disease_name, Chemical = chemical_name,  `Chemical Effect` =chemical_effect,    Year=pmYear, Sentence = sentence, pmid = reference)
tab <- tab[ order(-tab$Year),]
tab[1:10, ] %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Evidences for levodopa" ) 

plot(results, type="Network",   interactive=T, limit=50)

results <- chemical2evidence( chemical  = "C0042291", type = "VDA" , database = "TEXTMINING_HUMAN" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-vda 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        C0042291 
##  . Results:  222

tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, disease_name, chemical_name, sentence,chemical_effect, reference, pmYear)
tab <- tab %>% dplyr::rename( Disease = disease_name, Chemical = chemical_name,
                            `Chemical Effect` =chemical_effect,  Year=pmYear, Sentence = sentence, pmid = reference )
tab <- tab[ order(-tab$Year),]
tab[1:10,] %>%  dplyr::mutate(
    pmid = kableExtra::cell_spec(pmid,  link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>% 
  knitr::kable(format = 'markdown', row.names = F,  caption = "Evidences for valproic acid" ) 

plot(results, type="Network",   interactive=T, limit=50)

results <- chemical2attribute( chemical  = "C0023570"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical 
##  . Database:     ALL 
##  . Score:         
##  . Term:        C0023570 
##  . Results:  1

tab <-results@qresult
knitr::kable(tab, caption = "Attributes for levodopa") 

Searching DDAs by genes for a single disease

To obtain disease-disease associations, use the disease2disease function. This function uses as input a disease, in the same format that in disease2gene, the database to perform the search (by default, CURATED), and the argument relationship, to indicate the type of relationship of the disease pair. If the relationship is set to “has_shared_genes”, arguments such as min_genes, the minimum number of shared genes between the disease(s) of interest, and jg, the Jaccard Index for genes, can be defined. By default min_genes = 0. If the relationship is set to “has_shared_variants”, similar arguments to filter the results of the search can be defined.

The output is a DataGeNET.DGN object that contains the top diseases that share genes with the disease that has been searched.

The DataGeNET.DGN object produced by the disease2disease function also contains the Jaccard Index, also known as the Jaccard similarity coefficient for each disease pair. The Jaccard Coefficient is a similarity metric, computed as the size of the intersection divided by the size of the union of two sample sets, in this case, the genes associates to each disease:

We calculate a p value to estimate the significance of the Jaccard coefficient for a list of disease pairs. The p value is estimated using a Fisher exact test. The pvalue column displays the minus logarithm of the p value for the Jaccard Index, and is available for disease-disease associations by shared genes and by shared variants.

results <- disease2disease(
  disease  = "UMLS_C0010674", relationship = "has_shared_genes",
  database = "CURATED" ,   min_genes =2 )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-disease-gene 
##  . Database:     CURATED 
##  . Score:         
##  . Term:        UMLS_C0010674 
##  . Results:  12

Table 42 shows the diseases that share at least a gene with Cystic Fibrosis (UMLS_C0010674) in DISGENET curated.

tab <- unique(results@qresult[  ,c("disease1_Name", "disease2_Name","jaccard_genes","shared_genes", "pvalue_jaccard_genes")] )
knitr::kable(tab[1:10,], caption = "Diseases that share genes with Cystic Fibrosis") 

plot( results, 
      type = "Network",
      interactive=T )

Searching DDAs via genes for multiple diseases

The function disease2disease can also use as an input a list of diseases in any of the previously described vocabularies. It will retrieve the top diseases that share genes with each of the diseases in the input list.

Table 43 shows the disease list selected for illustrating the disease2disease function

diseasesOfInterest <-  paste0("UMLS_", c("C0162671", "C0023264", "C0917796"))
results <- disease2disease(
              disease = diseasesOfInterest, relationship = "has_shared_genes",
              database = "CURATED",
              min_genes  = 20, 
              order_by = "JACCARD_GENES" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      list 
##  . Type:        disease-disease-gene 
##  . Database:     CURATED 
##  . Score:         
##  . Term:       UMLS_C0162671 ... UMLS_C0917796 
##  . Results:  35

Table 44 shows the diseases that share at least 20 genes with the diseases of interest.

tab <- unique(results@qresult[  ,c("disease1_Name", "disease2_Name","jaccard_genes","shared_genes", "pvalue_jaccard_genes")] )
knitr::kable(tab[1:10,], caption = "Diseases that share at list 20 genes with the diseases of interest") 

To obtain the network, set the class argument of the plot function to Network(Figure 47). In this network, the nodes are the diseases of interest, and the node size is proportional to the number of genes associated with them. On the other hand, the edges size is proportional to the number of genes that are shared between the diseases they are connecting.

plot( results,
      type = "Network",
      interactive=TRUE)

Searching DDAs via shared variants for a single disease

To obtain disease-disease associations via shared genetic variants, use the disease2disease function with the argument relationship equal to “has_shared_variants”, the database to perform the search (by default, CURATED), and the argument min_vars, the minimum number of shared variants between the disease(s) of interest. By default min_vars = 0. The output is a DataGeNET.DGN object that contains the top diseases that share variants with the disease that has been searched.
In the example, we have specified a minimum value for the Jaccard Index computed from the shared variants (jv = 0.05).

results <- disease2disease(
  disease  = c("UMLS_C0011860", "UMLS_C0028754"),relationship = "has_shared_variants",
  database = "CURATED", jv = 0.01 )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      list 
##  . Type:        disease-disease-variant 
##  . Database:     CURATED 
##  . Score:         
##  . Term:       UMLS_C0011860 ... UMLS_C0028754 
##  . Results:  34

Table 45 shows the top diseases that share variants with Obesity and NIDDM.

tab <- unique(results@qresult[  ,c("disease1_Name", "disease2_Name","jaccard_variants","shared_variants", "pvalue_jaccard_variants")] )
tab <- tab[ order(-tab$shared_variants),]

knitr::kable(tab[1:10,], caption = "Top diseases that share variants with Obesity and NIDDM", row.names = F) 

The plot function applied to the DataGeNET.DGN object generated by the disease2disease function results in a Disease-Disease Network, where the node in dark blue is the disease of interest and nodes in light blue are the diseases that share variants with it (Figure 48). The node size is proportional to the number of variants associated to each disease.

plot( results, 
      type = "Network",
       interactive=T )

Searching DDAs via semantic relationships

To obtain disease-disease associations via semantic relationships, use the disease2disease function with the argument relationship equal to one of the following types of semantic relations: has_manifestation, has_associated_morphology, manifestation_of, associated_morphology_of, is_finding_of_disease, due_to, has_definitional_manifestation, has_associated_finding, definitional_manifestation_of, disease_has_finding, cause_of, associated_finding_of.

The output is a DataGeNET.DGN object that contains the diseases that have the type of relationship defined in the query with the query disease.

results <- disease2disease(
  disease  = c("UMLS_C0011860", "UMLS_C0028754"),relationship = "has_manifestation", min_sokal = 0.7, order_by = "SOKAL",
  database = "CURATED"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      list 
##  . Type:        disease-disease-rela 
##  . Database:     CURATED 
##  . Score:         
##  . Term:       UMLS_C0011860 ... UMLS_C0028754 
##  . Results:  20

Table 46 shows the diseases associated with Obesity and Diabetes Mellitus non Insulin dependent (NIDDM) by the relation type “has_manifestation”.

tab <- unique(results@qresult[  ,c("disease1_Name", "disease2_Name","ddaRelation","shared_genes", "pvalue_jaccard_genes")] )
knitr::kable(tab , caption = "Diseases associated with Obesity and NIDDM") 

Searching diseases similar to a disease of interest

It is possible to obtain the most similar diseases according to the Sokal-Sneath semantic similarity distance using the the get_similar_diseases function. The disease similarity between concepts is computed using the Sokal-Sneath semantic similarity distance (Sánchez and Batet 2011) on the taxonomic relations provided by the Unified Medical Language System Metathesaurus. Only the relationships of type is-a (which describe the taxonomy in any ontology) are taken into account. The get_similar_diseases function uses as input a disease, and as an optional argument min_sokal, a minimum value for the Sokal distance. By default min_sokal = 0.1.

results <- get_similar_diseases(
  disease  = "UMLS_C0011860",
    min_sokal = 0.6)
results