5.1.1 Single gene

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:  76

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 uniprotids protein_classid
## 1        LEPR   3953 ENSG00000116678 protein-coding   0.432   0.875 8.8607e-05     P48357    DTO_05007599
## 2        LEPR   3953 ENSG00000116678 protein-coding   0.432   0.875 8.8607e-05     P48357    DTO_05007599
## 3        LEPR   3953 ENSG00000116678 protein-coding   0.432   0.875 8.8607e-05     P48357    DTO_05007599
##   protein_class_name                             disease_name diseaseType diseaseUMLSCUI
## 1          Signaling                                  Obesity   [disease]       C0028754
## 2          Signaling Diabetes Mellitus, Non-Insulin-Dependent   [disease]       C0011860
## 3          Signaling                              Hyperphagia [phenotype]       C0020505
##                                                                            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                                           Pathological Conditions, Signs and Symptoms (C23)
##       diseaseClasses_UMLS_ST                                      diseaseClasses_DO
## 1 Disease or Syndrome (T047)                        disease of metabolism (0014667)
## 2 Disease or Syndrome (T047) genetic disease (630), disease of metabolism (0014667)
## 3             Finding (T033)                                                       
##                                                                           diseaseClasses_HPO disease_prevalence_class
## 1                                                                 Growth abnormality (01507)                         
## 2 Abnormality of metabolism/homeostasis (01939), Abnormality of the endocrine system (00818)                         
## 3                                                  Abnormality of the nervous system (00707)                         
##   disease_prevalence_geo_area disease_prevalence_type disease_inheritance numDBSNPsupportingAssociation
## 1                                                                                                     3
## 2                                                                                                     2
## 3                                                                                                     0
##   numCTsupportingAssociation numPMIDs chemsIncludedInEvidenceBySource numChemsIncludedInEvidences
## 1                         19       17                              NA                          NA
## 2                          4        6                              NA                          NA
## 3                          1        3                              NA                          NA
##   numPMIDSWithChemsIncludedInEvidences numNCTSWithChemsIncludedInEvidences score yearInitial yearFinal
## 1                                   NA                                  NA  1.35        1986      2025
## 2                                   NA                                  NA  1.00        2010      2025
## 3                                   NA                                  NA  0.95        1986      2007
##                                                                                                                                                                                    scoreBreakdown
## 1 1.35, CLINPGX, CLINVAR, RGD, MGD_HUMAN, 4, 0.6, CLINICALTRIALS, 1, 0.1, GWASCAT, HPO, 2, 0.15, TEXTMINING_MODELS, RGD_RAT, MGD_MOUSE, 3, 0.1, NA, TEXTMINING_HUMAN, TEXTMINING_MODELS, 388, 0.4
## 2                           1, RGD, MGD_HUMAN, 2, 0.45, CLINICALTRIALS, 1, 0.1, GWASCAT, 1, 0.05, TEXTMINING_MODELS, RGD_RAT, MGD_MOUSE, 3, 0.1, NA, TEXTMINING_HUMAN, TEXTMINING_MODELS, 48, 0.3
## 3                                                   0.95, RGD, 1, 0.4, CLINICALTRIALS, 1, 0.1, HPO, 1, 0.05, TEXTMINING_MODELS, RGD_RAT, 2, 0.1, NA, TEXTMINING_HUMAN, TEXTMINING_MODELS, 22, 0.3
##   normalized_score evidence_index evidence_level diseaseid
## 1        0.8709677      0.8832599           <NA>  C0028754
## 2        0.6451613      0.9578947           <NA>  C0011860
## 3        0.6129032      1.0000000           <NA>  C0020505

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:  440

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:  440

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:  45

In Table 5.1 are shown the top 10 diseases associated to the LEPR gene

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score","normalized_score", "yearInitial", "yearFinal")] )
tab$normalized_score  <- round(tab$normalized_score, digits = 2)
knitr::kable(tab[1:10,], caption = "Top diseases associated to LEPR" ) 

5.1.1.1 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 5.1). 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 5.1: The Gene-Disease Network for the Leptin Receptor gene

Use interactive = TRUE to display an interactive plot (Figure 5.2).

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

Figure 5.2: 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 5.3). 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 diseases that do not have annotations to MeSH disease class will be shown as a warning.

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

Figure 5.3: The Disease Class Network for the Leptin Receptor Gene

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:  26

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)

5.1.2 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 5.3) 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:  16

In Table 5.4, the top 10 diseases associated to the list of genes belonging to the voltage-gated potassium channel family.

tab <- results@qresult[, c("gene_symbol", "disease_name", "score",
                           "normalized_score", "yearInitial", "yearFinal")] %>%
  unique() %>%
  mutate(normalized_score = round(normalized_score, 2)) %>%
  arrange(desc(score), yearInitial)

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

5.1.2.1 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 5.5).

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

Figure 5.5: 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 5.6).

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

Figure 5.6: 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 5.7), 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 = 50, 
      interactive =T, 
      verbose = T)

Figure 5.7: 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 5.8). 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 5.8: 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 5.9).

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

Figure 5.9: 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 29 pages.

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

results <- gene2evidence(gene     = c("MMP1", "MMP2", "MMP3", "MMP9", "MMP10"), 
                       database = "CLINICALTRIALS", verbose  = TRUE )

## Your query has 13 pages.

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

5.1.3 Filtering chemical

You can search GDAs by chemicals by specifying a chemical identifier using the chemical filter in the gene2disease function. Table 5.5 shows the diseases associated to LEPR associated to metformin.

results <- gene2disease( gene = "LEPR", vocabulary = "HGNC",
                       database = "TEXTMINING_HUMAN", 
                       chemical = "CHEMBL_CHEMBL1431" )
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, normalized_score) %>%     mutate(normalized_score = round(normalized_score, 2))  %>%
    arrange(desc(score))
knitr::kable(tab, caption = "GDAs for LEPR and metformin") 

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:  15

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" ) 

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

5.2.1 Single disease

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( 0.5,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.5-1 
##  . Term:        UMLS_C0036341 
##  . Results:  153

In Table 5.7, the top 10 genes associated to UMLS CUI C0036341.

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score","normalized_score", "yearInitial", "yearFinal")] )  %>%
    mutate(normalized_score = round(normalized_score, 2)) %>%
  arrange(desc(score), yearInitial)
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( 0.5,1  ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.5-1 
##  . Term:        MESH_D012559 
##  . Results:  153

results <- disease2gene( disease  = "OMIM_181500",  
                          database = "CURATED",
                          score    = c(  0.5,1  ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.5-1 
##  . Term:        OMIM_181500 
##  . Results:  153

results <- disease2gene( disease  = "ICD9CM_295",  
                          database = "CURATED",
                          score    = c( 0.5,1  ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.5-1 
##  . Term:        ICD9CM_295 
##  . Results:  153

results <- disease2gene( disease  = "NCI_C3362", 
                          database = "CURATED",
                          score    = c(  0.5,1  ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.5-1 
##  . Term:        NCI_C3362 
##  . Results:  153

results <- disease2gene( disease  = "HPO_HP:0100753", 
                          database = "CURATED",
                         score    = c(  0.5,1  ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-gene 
##  . Database:     CURATED 
##  . Score:        0.5-1 
##  . Term:        HPO_HP:0100753 
##  . Results:  153

results <- disease2evidence( disease  = "UMLS_C0036341",
                           type = "GDA",
                          database = "CURATED",
                          score    = c( 0.6,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-evidence 
##  . Database:     CURATED 
##  . Score:        0.6-1 
##  . Term:        UMLS_C0036341 
##  . Results:  107

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"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-evidence 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        UMLS_C0036341 
##  . Results:  497

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" )  

5.2.2 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 5.10 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(0.6,1),
  verbose  = TRUE )

## Your query has 1 page.

In table 5.11, the top 10 genes associated to the list of diseases.

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score",
                           "normalized_score", "yearInitial", "yearFinal")] %>%
    mutate(normalized_score = round(normalized_score, 2)) %>%
    arrange(desc(score), yearInitial)) 
knitr::kable(tab[1:10,], caption = "Top Genes associated to a list of diseases") 

5.2.2.1 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 5.15).

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

Figure 5.15: The Gene-Disease Network associated to a list of diseases

To visualize the results as a Gene-Disease Heatmap (Figure 5.16) 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 =65,
      cutoff=0.95, interactive=TRUE)

## [1] "Dataframe of 97 rows has been reduced to 65 rows."

Figure 5.16: The Gene-Disease Heatmap for genes associated to a list of diseases

A third visualization option is a Protein Class-Disease Heatmap (Figure 5.17), 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 5.17: The Protein Class-Disease Heatmap for genes associated to a list of diseases

A Protein Class-Disease Network visualization is also possible (Figure 5.18).

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

Figure 5.18: 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:  1585

To visualize the results use the argument Points (Figure 5.19).

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

Figure 5.19: The Evidences plot for a list of diseases

5.2.3 Filtering by 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_C0678222", chemical = "CHEMBL_CHEMBL83",
                          database = "ALL" , n_pags = 1 )

## Notice that your query has a maximum of 8 pages.
## By indicating n_pags = 1, your query of 8 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_C0678222 
##  . Results:  100

tab <- unique(results@qresult[  ,c("gene_symbol", "disease_name","score","normalized_score", "chemical_name", "chemicalid")] )%>%mutate(normalized_score = round(normalized_score, 2)) %>% dplyr::arrange(desc(score))
knitr::kable(tab[1:10,], caption = "Top GDAs associated to Breast Carcinoma") 

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:  38

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" ) 

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

5.2.3.2 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 5.14)

results <- disease2gene( disease  = "UMLS_C0030567",
                          database = "TEXTMINING_HUMAN",
                          chemical = "CHEMBL_CHEMBL1009")
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-gda 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        UMLS_C0030567 
##  . Results:  72

tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol, disease_name, chemical_name, score, normalized_score) %>% mutate(normalized_score = round(normalized_score, 2)) %>% dplyr::arrange(desc(score)) 
knitr::kable(tab[1:10,], caption = "Top GDAs associated to Parkinson and levodopa") 

Table 5.14: Top GDAs associated to Parkinson and levodopa

gene_symbol	disease_name	chemical_name	score	normalized_score
SNCA	Parkinson Disease	Levodopa	1.55	1.00
GBA1	Parkinson Disease	Levodopa	1.45	0.94
PRKN	Parkinson Disease	Levodopa	1.35	0.87
LRRK2	Parkinson Disease	Levodopa	1.35	0.87
PINK1	Parkinson Disease	Levodopa	1.20	0.77
MAOB	Parkinson Disease	Levodopa	1.10	0.71
DRD2	Parkinson Disease	Levodopa	1.05	0.68
TH	Parkinson Disease	Levodopa	1.05	0.68
BDNF	Parkinson Disease	Levodopa	1.00	0.65
DDC	Parkinson Disease	Levodopa	1.00	0.65

To visualize the results use the function plot (Figure 5.21)

plot( results, interactive= T )

Figure 5.21: The Gene Disease Chemical Network for Parkinson and levodopa

5.2.3.2.1 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))
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-chemical 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0.5-1 
##  . Term:        UMLS_C0030567 
##  . Results:  273

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" ) 

Table 5.15: Top Chemicals associated to Parkinson

Gene	Chemical	Chemical Effect	Sentence	pmid	Year
SNCA	Trolamine	other|other|other|other	Neuroprotective Effects of the Combination of Green Tea, Saffron, Docosahexaenoic Acid, and α-Lipoic Acid in an In Vitro Model of Parkinson’s Disease. Parkinson’s disease (PD) is a neurodegenerative disorder characterised by dopamine deficiency and the accumulation of α-synuclein (α-syn), which aggregates into pathological inclusions known as Lewy bodies and Lewy neurites, distributed across multiple brain regions, with a particular prevalence in dopaminergic neurons.	41832920	2026
SNCA	Crocin	other|other|other|other	Neuroprotective Effects of the Combination of Green Tea, Saffron, Docosahexaenoic Acid, and α-Lipoic Acid in an In Vitro Model of Parkinson’s Disease. Parkinson’s disease (PD) is a neurodegenerative disorder characterised by dopamine deficiency and the accumulation of α-synuclein (α-syn), which aggregates into pathological inclusions known as Lewy bodies and Lewy neurites, distributed across multiple brain regions, with a particular prevalence in dopaminergic neurons.	41832920	2026
SNCA	Doconexent	other|other|other|other	Neuroprotective Effects of the Combination of Green Tea, Saffron, Docosahexaenoic Acid, and α-Lipoic Acid in an In Vitro Model of Parkinson’s Disease. Parkinson’s disease (PD) is a neurodegenerative disorder characterised by dopamine deficiency and the accumulation of α-synuclein (α-syn), which aggregates into pathological inclusions known as Lewy bodies and Lewy neurites, distributed across multiple brain regions, with a particular prevalence in dopaminergic neurons.	41832920	2026
SNCA	LIPOIC ACID, ALPHA	other|other|other|other	Neuroprotective Effects of the Combination of Green Tea, Saffron, Docosahexaenoic Acid, and α-Lipoic Acid in an In Vitro Model of Parkinson’s Disease. Parkinson’s disease (PD) is a neurodegenerative disorder characterised by dopamine deficiency and the accumulation of α-synuclein (α-syn), which aggregates into pathological inclusions known as Lewy bodies and Lewy neurites, distributed across multiple brain regions, with a particular prevalence in dopaminergic neurons.	41832920	2026
SNCA	Nicotine	other|other	Nicotine protects astrocytes expressing alpha-synuclein against aminochrome cytotoxicity: Implications for Parkinson’s disease.	42147199	2026
SNCA	Nicotine	other|other	Nicotine protects astrocytes expressing alpha-synuclein against aminochrome cytotoxicity: Implications for Parkinson’s disease.	42147199	2026
SNCA	Dopamine	therapeutic	Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic (DA) neurons and the pathological aggregation of α-synuclein.	42180537	2026
SNCA	Dopamine	therapeutic	Parkinson’s Disease (PD), the second most common neurodegenerative disorder, is primarily characterized by neuroinflammation and progressive degeneration of dopaminergic (DA) neurons in the substantia nigra pars compacta, triggered by the pathological aggregation of α-synuclein (α-syn).	41831074	2026
SNCA	Betulinic Acid	other	Betulinic acid exacerbates biomolecular condensation of α-synuclein: possible role in Parkinson’s disease.	41801138	2026
SNCA	THIOFLAVIN T	other	Altogether, our study shows that α-syn amyloid fibrils are capable of undergoing structural evolution of a type that alters their ThT binding, highlights the role of fragmentation in expediating such maturation processes, and points out a putative connection between propensity of structural conversion, decreased fibril stability, and early onset of Parkinson’s disease.	42159605	2026

To visualize the results use the function plot

plot( results )

Figure 5.22: The Network plot for chemicals associated to Parkinson Disease

6.1.1 Single 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(0.2,1)) 
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-disease 
##  . Database:     CURATED 
##  . Score:        0.2-1 
##  . Term:        rs113488022 
##  . Results:  13

The results are shown in Table 6.1.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score", "normalized_score", "yearInitial", "yearFinal")] ) %>% mutate(normalized_score = round(normalized_score, 2)) %>% dplyr::arrange(desc(score), yearInitial, 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)

6.1.1.2 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" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-evidence 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        rs10795668 
##  . Results:  15

The results are shown in table 6.2.

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 6.2: Evidences supporting the association between C0009402 & rs10795668

associationType	pmid	Year	Sentence
Susceptibility Mutation	40479638	2025	Our findings validated rs10795668 (LOC10537640), rs4939827 (SMAD7), rs6066825 (PREX1), and rs6983267 (CCAT2) polymorphisms in CRC risk among Brazilians and suggest that lower Asian and African ancestries might influence CRC susceptibility.
Genetic Variation	36653562	2023	FinnGen provides genetic insights from a well-phenotyped isolated population.
Susceptibility Mutation	34676053	2021	Increasing risk of CRC was noted for rs10795668 in log-additive model (OR = 1.45, 95% CI: 1.05-1.99, p = 0.023); for rs1035209 in log-additive model (OR = 1.79, 95% CI: 1.18-2.72, p = 0.003); for rs11190164 in log-additive model (OR = 1.67, 95% CI: 1.17-2.38, p = 0.004).
Susceptibility Mutation	24801760	2015	The CRC SNPs accounted for 4.3% of the variation in multiple adenoma risk, with three SNPs (rs6983267, rs10795668, rs3802842) explaining 3.0% of the variation.
Susceptibility Mutation	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.
Susceptibility Mutation	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.
Susceptibility Mutation	22235025	2012	In conclusion, variants at 10p14 (rs10795668), 11q23.1 (rs3802842) and 15q13.3 (rs4779584) may have a predominant role in predisposition to early-onset CRC.
Susceptibility Mutation	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).
Biomarker	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.
Susceptibility Mutation	20530476	2010	These results suggest that rs6983267, rs4939827, rs10795668, rs3802842, and rs961253 SNPs are associated with the risk of CRC in the Chinese population individually and jointly.

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" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-evidence 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        rs1800629 
##  . Results:  1788

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

Figure 6.3: The Evidence plot for the variant rs1800629

6.1.2 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 6.3, the top 10 diseases associated to the list of variants.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score","normalized_score", "yearInitial", "yearFinal")] )%>% mutate(normalized_score = round(normalized_score, 2)) %>% 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=45)

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

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

6.2.1 Single 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:  172

In Table 6.4, the variants associated to Timothy syndrome according to ClinVar database.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score","normalized_score", "yearInitial", "yearFinal")] ) %>%  mutate(normalized_score = round(normalized_score, 2)) %>% dplyr::arrange(desc(score), yearInitial, desc(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:  100

In Table 6.5, the deleterious variants associated to Timothy syndrome repored in ClinVar database.

tab <- unique(results@qresult[  ,c("variantid", "disease_name","score", "normalized_score", "polyphen_score", "sift_score", "yearInitial", "yearFinal")] ) %>% mutate(normalized_score = round(normalized_score, 2)) %>% dplyr::arrange(desc(score), yearInitial, desc(yearFinal))

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

6.2.1.1 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 6.9).

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

Figure 6.9: 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 6.10).

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

Figure 6.10: 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:  56

results <- results@qresult
results <- results %>% dplyr::filter(reference_type =="PMID") %>%
    select(reference, associationType, pmYear, sentence) %>% dplyr::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( 0.5,1 ) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        disease-evidence 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0.5-1 
##  . Term:        UMLS_C1832916 
##  . Results:  24

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

6.2.2 Multiple diseases

results <- disease2variant(
              disease = paste0("UMLS_",c("C3150943",  "C1859062", "C1832916", "C4015695")),
              database = "CURATED", 
              score = c(0.5, 1) )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      list 
##  . Type:        disease-variant 
##  . Database:     CURATED 
##  . Score:        0.5-1 
##  . Term:       UMLS_C3150943 ... UMLS_C4015695 
##  . Results:  163

Table 6.8 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","normalized_score", "yearInitial", "yearFinal")] ) %>% mutate(normalized_score = round(normalized_score, 2)) %>% dplyr::arrange(desc(score), yearInitial, desc(yearFinal))
tab[is.na(tab)] <- ""
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", 
      limit = 100, 
      interactive=T)

6.3.1 Visualizing the variant-disease associations retrieved for a gene

The results of querying DISGENET variant information with a gene can be visualized as a Variant-Disease Network, or as a Variant-Disease Heatmap (Figure 6.13), if the input is a list of genes, by changing the class argument from Network to Heatmap. The genes can be shown by setting the showGenes argument to “TRUE”.

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

6.3.2 Filtering by chemical

results <- variant2disease( variant   = "rs121434568",
                          database = "TEXTMINING_HUMAN",
                          chemical = "CHEMBL_CHEMBL1173655")
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-disease 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        rs121434568 
##  . Results:  6

tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, disease_name, chemical_name, score , normalized_score) %>% mutate(normalized_score = round(normalized_score, 2)) %>% dplyr::arrange(desc(score))

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

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

results <- variant2chemical( variant =  "rs1801133",
                          database = "TEXTMINING_HUMAN" , score = c(0.3,1))
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        variant-chemical 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0.3-1 
##  . Term:        rs1801133 
##  . Results:  19

tab <- results@qresult
tab <-tab%>% dplyr::select( disease_name, chemical_name, chemical_effect, sentence, reference, pmYear)
tab <- tab[1:10, ] %>% 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" ) 

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

7.2.1 Exploring the GDAs of a chemical

The chemical2gda function allows to retrieve the GDAS for a specific chemical, or list of chemicals.

results <- chemical2gda( chemical  = "CHEMBL_CHEMBL809", database = "ALL"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-gda 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        CHEMBL809 
##  . Results:  228

tab <- results@qresult

tab <-tab%>% dplyr::select(gene_symbol, disease_name, chemical_name, score,normalized_score, pmids_chemical) %>% mutate(normalized_score = round(normalized_score, 2))
knitr::kable(tab[1:10,], caption = "GDAs for sertraline ") 

To visualize the results use the plot function.

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

7.2.2 Exploring the VDAs of a chemical

The chemical2vda function allows to retrieve the VDAS for a specific chemical, or list of chemicals.

results <- chemical2vda( chemical  = "CHEMBL_CHEMBL2010601", database = "ALL"  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-vda 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        CHEMBL2010601 
##  . Results:  20

The chemical2vda function can also receive as a parameter sift and polyphen scores to restrict the results to variants predicted as probably deleterious.

results <- chemical2vda( chemical  = "CHEMBL_CHEMBL2010601", 
                         database = "ALL", 
                         sift = c(0,0.05) , polyphen = c(0.9,1)  )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-vda 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        CHEMBL2010601 
##  . Results:  16

tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, disease_name, chemical_name, score,normalized_score, pmids_chemical) %>% mutate(normalized_score = round(normalized_score, 2)) 
knitr::kable(tab[1:10,], caption = "VDAs associated ivacaftor") 

To visualize the results use the plot function.

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

7.2.3 Exploring the GDA evidences of a chemical

The chemical2evidence function allows to retrieve the evidences for the GDAS or VDAs for a specific chemical, or list of chemicals.

results <- chemical2evidence( chemical  = "CHEMBL_CHEMBL1069", type = "GDA" , database = "ALL" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-gda 
##  . Database:     ALL 
##  . Score:        0-1 
##  . Term:        CHEMBL1069 
##  . Results:  632

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 Valsartan" ) 

To visualize the results use the plot function.

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

7.2.4 Exploring the VDA evidences of a chemical

results <- chemical2evidence( chemical  = "CHEMBL_CHEMBL502", type = "VDA" , database = "TEXTMINING_HUMAN" )
results

## Object of class 'DataGeNET.DGN'
##  . Search:      single 
##  . Type:        chemical-vda 
##  . Database:     TEXTMINING_HUMAN 
##  . Score:        0-1 
##  . Term:        CHEMBL502 
##  . Results:  5

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  %>%  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 Donepezil" ) 

To visualize the results use the plot function.

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

8.1.1 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_1 = "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:  13

Table 8.1 shows the diseases that share at least a gene with Cystic Fibrosis (UMLS_C0010674) in DISGENET curated.

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