2.1.1 Extract Tables

Now that the information about the genes of interest was download from CTDbase we can access to it using the method get_table. Method extract allows to access to different tables according to the origin of the object. For a created from genes the accessible tables are:

Example of how to extract one of this tables follows:

get_table( ctd_genes , index_name = "diseases" )[ 1:2, 1:3 ]

## DataFrame with 2 rows and 3 columns
##                             Disease.Name   Disease.ID  Direct.Evidence
##                              <character>  <character>      <character>
## 1 Chemical and Drug Induced Liver Injury MESH:D056486 marker/mechanism
## 2                        Atherosclerosis MESH:D050197 marker/mechanism

The information stored in each table can be see in the following code, were the names of the columns of each table is shown:

colnames( get_table( ctd_genes, index_name = "chemical interactions" ) )

## [1] "Chemical.Name"       "Chemical.ID"         "CAS.RN"             
## [4] "Interaction"         "Interaction.Actions" "Reference.Count"    
## [7] "Organism.Count"      "GeneSymbol"          "GeneID"

colnames( get_table( ctd_genes, index_name = "diseases" ) )

## [1] "Disease.Name"      "Disease.ID"        "Direct.Evidence"  
## [4] "Inference.Network" "Inference.Score"   "Reference.Count"  
## [7] "GeneSymbol"        "GeneID"

colnames( get_table( ctd_genes, index_name = "gene-gene interactions" ) )

##  [1] "Source.Gene.Symbol" "Source.Gene.ID"     "Target.Gene.Symbol"
##  [4] "Target.Gene.ID"     "Source.Organism"    "Target.Organism"   
##  [7] "Assay"              "Interaction.Type"   "Throughput"        
## [10] "Reference.Authors"  "Reference.Citation" "PubMed.ID"         
## [13] "GeneSymbol"         "GeneID"

colnames( get_table( ctd_genes, index_name = "kegg pathways" ) )

## [1] "Pathway"    "Pathway.ID" "GeneSymbol" "GeneID"

colnames( get_table( ctd_genes, index_name = "go terms" ) )

## [1] "Ontology"             "Qualifiers"           "GO.Term.Name"        
## [4] "GO.Term.ID"           "Organisms..Evidence." "GeneSymbol"          
## [7] "GeneID"

2.1.2 Plotting Gene Created CTDdata Objects

The generic plot function has the same mechanism that get_table. Using the argument index_name we select the table to plot. Then, the arguments subset.gene and subset.* (being * chemicals, diseases, pathways and go) allows to filter the X-axis and Y-axis. Depending the table to be plotted, the argument field.score can be used to select the field to plotted (that can takes "Inference" or "Reference" values). Then argument filter.score can be used to filter entries of the table. Finally, the argument max.length is in charge to reduce the characters of the labels.

The following plot shows the number of reference that cites the association between the APOE-like genes and chemicals.

plot( ctd_genes, index_name = "chemical interactions", filter.score = 3 )

Then, next plot shows shows the inference score that associates the APOE-like genes with diseases according to CTDbase.

plot( ctd_genes, index_name = "disease", filter.score = 115 )

The plot to explore the gene-gene interactions is based in a network representation. The genes from the original set are dark-coloured, while the other genes are light-coloured.

plot( ctd_genes, index_name = "gene-gene interactions", 
    representation = "network", main = "APOE-like gene-gene interactions" )

Finally both KEG pathways and GO terms related to the given set of genes can be also visually explored. for KEGG pathways:

plot( ctd_genes, index_name = "kegg pathways", 
    representation = "network", main = "KEGG pathways related to APOE genes" )

With idea to allow user to create clean networks, arguments subset.gene and subset.pathway can be used to filter genes and KEGG pathways. For GO term we use the same structure:

plot( ctd_genes, index_name = "go terms",
    representation = "network", main = "GO terms related to APOE genes",
    ontology = "Molecular Function" )

The argument ontology can take values "Biological Process", "Cellular Component" and "Molecular Function". One of them or any combination. This helps to filter the relation that will be plotted in the network. With the same idea, arguments subset.gene and subset.go can be used to filter genes and GO terms.

2.2.1 Extract Tables

Now that the information about the chemicals of interest was download from CTDbase we can access to it using the method get_table. Method extract allows to access to different tables according to the origin of the object. For a created from chemicals the accessible tables are:

Example of how to extract one of this tables follows:

get_table( ctd_chem , index_name = "diseases" )[ 1:2, 1:6 ]

## DataFrame with 2 rows and 6 columns
##   Chemical.Name Chemical.ID      CAS.RN                  Disease.Name
##     <character> <character> <character>                   <character>
## 1          Zinc     D015032   7440-66-6 Liver Cirrhosis, Experimental
## 2          Zinc     D015032   7440-66-6              Breast Neoplasms
##     Disease.ID  Direct.Evidence
##    <character>      <character>
## 1 MESH:D008106 marker/mechanism
## 2 MESH:D001943 marker/mechanism

The information stored in each table can be see in the following code, were the names of the columns of each table is shown:

colnames( get_table( ctd_chem, index_name = "gene interactions" ) )

##  [1] "Chemical.Name"       "Chemical.ID"         "CAS.RN"             
##  [4] "Gene.Symbol"         "Gene.ID"             "Interaction"        
##  [7] "Interaction.Actions" "Reference.Count"     "Organism.Count"     
## [10] "ChemicalName"        "ChemicalID"

colnames( get_table( ctd_chem, index_name = "diseases" ) )

##  [1] "Chemical.Name"     "Chemical.ID"       "CAS.RN"           
##  [4] "Disease.Name"      "Disease.ID"        "Direct.Evidence"  
##  [7] "Inference.Network" "Inference.Score"   "Reference.Count"  
## [10] "ChemicalName"      "ChemicalID"

colnames( get_table( ctd_chem, index_name = "kegg pathways" ) )

## [1] "Pathway"                  "Pathway.ID"              
## [3] "P.value"                  "Corrected.P.value"       
## [5] "Annotated.Genes.Quantity" "Annotated.Genes"         
## [7] "Genome.Frequency"         "ChemicalName"            
## [9] "ChemicalID"

colnames( get_table( ctd_chem, index_name = "go terms" ) )

##  [1] "Ontology"                 "Highest.GO.Level"        
##  [3] "GO.Term.Name"             "GO.Term.ID"              
##  [5] "P.value"                  "Corrected.P.value"       
##  [7] "Annotated.Genes.Quantity" "Annotated.Genes"         
##  [9] "Genome.Frequency"         "ChemicalName"            
## [11] "ChemicalID"

2.2.2 Plotting Chemical Created CTDdata Objects

The generic plot function seen in the previous sections for gene queries also works with the chemical queries. The arguments that can be used are the same when plotting both types of queries.

The following plot shows the inference score for each chemical-gene association according to CTDbase.

plot( ctd_chem, index_name = "gene interactions", filter.score = 5 )

This associations, or relations, can be further inspected in a network representation that includes the effect of the chemical on the altered genes.

plot( ctd_chem, index_name = "gene interactions", representation = "network",
    filter.score = 3, main = "Gen-Chemical interaction for Zinc and Cadmium" )

Consequently, a heat-map with the inference score for the associations between chemicals and diseases can also be plotted.

plot( ctd_chem, index_name = "disease" )

The association between KEGG pathways and chemicals can be seen as a heat-map. The heat-map shows the P-Value of the association between each pathway and each chemical.

plot( ctd_chem, index_name = "kegg pathways", filter.score = 1e-40 )

The argument filter.score can be used to filter associations by their P-Value. Only the associations with a -Value lower or equal to the value given to filter.score are keep. Then, the heat-map calculates the terciles of the distribution of P-Values to create the legend (that has always four categories).

The heat-map for GO terms follows the same mechanic:

plot( ctd_chem, index_name = "go terms",
    representation = "network", filter.score = 1e-210 )

2.3.1 Extract Tables

Now that the information about the diseases of interest was download from CTDbase we can access to it using the method get_table. Method extract allows to access to different tables according to the origin of the object. For a created from diseases the accessible tables are:

get_table( ctd_diseases , index_name = "gene interactions" )[ 1:2, 1:5 ]

## DataFrame with 2 rows and 5 columns
##   Gene.Symbol   Gene.ID      Disease.Name   Disease.ID  Direct.Evidence
##   <character> <integer>       <character>  <character>      <character>
## 1         APP       351 Alzheimer Disease MESH:D000544 marker/mechanism
## 2       CASP3       836 Alzheimer Disease MESH:D000544 marker/mechanism

The information stored in each table can be see in the following code, were the names of the columns of each table is shown:

colnames( get_table( ctd_diseases, index_name = "gene interactions" ) )

## [1] "Gene.Symbol"       "Gene.ID"           "Disease.Name"     
## [4] "Disease.ID"        "Direct.Evidence"   "Inference.Network"
## [7] "Inference.Score"   "Reference.Count"

colnames( get_table( ctd_diseases, index_name = "chemical interactions" ) )

## [1] "Chemical.Name"     "Chemical.ID"       "CAS.RN"           
## [4] "Disease.Name"      "Disease.ID"        "Direct.Evidence"  
## [7] "Inference.Network" "Inference.Score"   "Reference.Count"

colnames( get_table( ctd_diseases, index_name = "kegg pathways" ) )

## [1] "Disease.Name"             "Disease.ID"              
## [3] "Pathway"                  "Pathway.ID"              
## [5] "Association.inferred.via"

2.3.2 Plotting Disease Created CTDdata Objects

As seen in the previous sections CTDquerier allows for basic visualization from disease-retrieved information from CTDbase. The function plot is also used on disease created CTDdata objects allowing to display the disease-gene interaction tables, the disease-chemical interaction table and the pathway related to disease table.

Disease created CTDdata objects have a heat-map visualization of the disease interaction with chemicals. This plot allows to select the CTDbase’s inference score or the number of reference to highlight the associations.

plot( ctd_diseases, index_name = "gene interactions", filter.score = 75 )

Then, the associations between diseases and chemicals can also be plotted. Usually this plot is a heat-map as previously seen for genes. Nevertheless the lot can result in a bar-plot. This happens when the argument filter.core that allows to filter the associations (selecting any of CTDbase’s inference score or number of reference for the association) is so string that only a single disease is kept.

plot( ctd_diseases, index_name = "chemical interactions", filter.score = 35 )

Finally a network for the KEGG pathways inferred for the diseases can also be obtained.

plot( ctd_diseases, index_name = "kegg pathways", 
    representation = "network", subset.disease = "Dementia" )