Loading example data

A subset of the HPO is provided within the ReDaMoR package. We can read some of the tables as follow:

library(readr)
hpo_data_dir <- system.file("examples/HPO-subset", package="ReDaMoR")

The HPO_hp table gathers human phenotype identifiers, names and descriptions:

HPO_hp <- readr::read_tsv(
   file.path(hpo_data_dir, "HPO_hp.txt")
)
HPO_hp

## # A tibble: 500 × 4
##    id      name                                              description   level
##    <chr>   <chr>                                             <chr>         <dbl>
##  1 0000002 Abnormality of body height                        Deviation fr…     3
##  2 0000009 Functional abnormality of the bladder             Dysfunction …     6
##  3 0000014 Abnormality of the bladder                        An abnormali…     5
##  4 0000017 Nocturia                                          Abnormally i…     7
##  5 0000019 Urinary hesitancy                                 Difficulty i…     7
##  6 0000021 Megacystis                                        Dilatation o…     8
##  7 0000022 Abnormality of male internal genitalia            An abnormali…     6
##  8 0000024 Prostatitis                                       The presence…     8
##  9 0000025 Functional abnormality of male internal genitalia <NA>              6
## 10 0000030 Testicular gonadoblastoma                         The presence…     9
## # ℹ 490 more rows

The HPO_diseases table gathers disease identifiers and labels from different disease database.

HPO_diseases <- readr::read_tsv(
   file.path(hpo_data_dir, "HPO_diseases.txt")
)
HPO_diseases

## # A tibble: 1,903 × 3
##    db           id label                                                        
##    <chr>     <dbl> <chr>                                                        
##  1 DECIPHER     15 NF1-microdeletion syndrome                                   
##  2 DECIPHER     45 Xq28 (MECP2) duplication                                     
##  3 DECIPHER     65 ATR-16 syndrome                                              
##  4 OMIM     100050 AARSKOG SYNDROME, AUTOSOMAL DOMINANT                         
##  5 OMIM     100650 ALDEHYDE DEHYDROGENASE 2 FAMILY                              
##  6 OMIM     101800 ACRODYSOSTOSIS 1, WITH OR WITHOUT HORMONE RESISTANCE; ACRDYS1
##  7 OMIM     102500 HAJDU-CHENEY SYNDROME; HJCYS                                 
##  8 OMIM     102510 ACROPECTOROVERTEBRAL DYSPLASIA, F-FORM OF                    
##  9 OMIM     102700 SEVERE COMBINED IMMUNODEFICIENCY, AUTOSOMAL RECESSIVE, T CEL…
## 10 OMIM     102800 ADENOSINE TRIPHOSPHATASE DEFICIENCY, ANEMIA DUE TO           
## # ℹ 1,893 more rows

The HPO_diseaseHP table indicates which phenotype is triggered by each disease.

HPO_diseaseHP <- readr::read_tsv(
   file.path(hpo_data_dir, "HPO_diseaseHP.txt")
)
HPO_diseaseHP

## # A tibble: 2,594 × 3
##    db           id hp     
##    <chr>     <dbl> <chr>  
##  1 ORPHA    140976 0000002
##  2 ORPHA       432 0000002
##  3 DECIPHER     45 0000009
##  4 OMIM     300076 0000009
##  5 ORPHA    100996 0000009
##  6 ORPHA    100997 0000009
##  7 ORPHA      2571 0000009
##  8 ORPHA    391487 0000009
##  9 ORPHA    488594 0000009
## 10 ORPHA     71211 0000009
## # ℹ 2,584 more rows

Creating a data model with ReDaMoR

The ReDaMoR package can be used for drafting a data model from a set of table:

mhpo_dm <- ReDaMoR::df_to_model(HPO_hp, HPO_diseases, HPO_diseaseHP)
if(igraph_available){
   mhpo_dm %>%
      ReDaMoR::auto_layout(lengthMultiplier=80) %>% 
      plot()
}else{
   mhpo_dm %>%
      plot()
}

This data model is minimal: only the name of the tables, their fields and their types are documented. There is no additional constrain regarding the uniqueness or the completeness of the fields. Also there is no information regarding the relationships between the different tables. The model_relational_data() can be used to improve the documentation of the dataset according to what we know about it. This function raises a graphical interface for manipulating and modifying the data model (see ReDaMoR documentation).

mhpo_dm <- ReDaMoR::model_relational_data(mhpo_dm)

Below is the model we get after completing it using the function above.

plot(mhpo_dm)

In this model, we can see that:

• id is the primary key of the HPO_hp table, and therefore this field must be unique;
• db/id form the primary key of the HPO_diseases table and must also be unique when taken together;
• all the fields excepted description (in the HPO_hp table) are complete (they cannot be NA);
• the HPO_diseaseHP table refers to the HPO_hp table using its HPO_hp fields and to the HPO_diseases table using its db and id fields (such details are shown when putting cursor over the edges).

Moreover, some comments are added at the table and at the field level to give a better understanding of the data (shown when putting the cursor over the tables).

Binding the model to the data in an MDB object

The data model can be explicitly bound to the data in an MDB (Modeled DataBase) object as shown below. However, when trying to build the object with the tables we’ve read and the data model we have edited, we get the following error message.

mhpo_db <- memoMDB(
   dataTables=list(
      HPO_hp=HPO_hp, HPO_diseases=HPO_diseases, HPO_diseaseHP=HPO_diseaseHP
   ),
   dataModel=mhpo_dm,
   dbInfo=list(name="miniHPO")
)

Indeed, according to the edited model (not the very first one automatically created by ReDaMoR), the HPO_hp$level field should contain integer values and the HPO_diseases$id and HPO_diseaseHP$id fields should contain character values. The type of the data is among the data model features that are automatically checked when building an MDB object (along with uniqueness or NA values for example).

To avoid this error, we can either change the type of the columns of the data tables:

HPO_hp <- mutate(HPO_hp, level=as.integer(level))
HPO_diseases <- mutate(HPO_diseases, id=as.character(id))
HPO_diseaseHP <- mutate(HPO_diseaseHP, id=as.character(id))
mhpo_db <- memoMDB(
   dataTables=list(
      HPO_hp=HPO_hp, HPO_diseases=HPO_diseases, HPO_diseaseHP=HPO_diseaseHP
   ),
   dataModel=mhpo_dm,
   dbInfo=list(name="miniHPO")
)

Or we can use the data model to read the data in a fileMDB object:

f_mhpo_db <- read_fileMDB(
   path=hpo_data_dir,
   dbInfo=list(name="miniHPO"),
   dataModel=mhpo_dm
)

## miniHPO
## SUCCESS
## 
## Check configuration
##    - Optional checks: 
##    - Maximum number of records: 10

The read_fileMDB() function identifies the text files to read in path according to the dataModel. It uses the types documented in the data model to read the files. By default, the field delimiter is \t, but another can be defined by writing a delim slot in the dbInfo parameter (e.g. dbInfo=list(name="miniHPO", delim="\t")).

As shown in the message above, by default, read_fileMDB() does not perform optional checks (unique fields, not nullable fields, foreign keys) and it only checks data on the 10 first records. Also, the fileMDB data are not loaded in memory until requested by the user. The object is then smaller than the memoMDB object even if they gather the same information.

print(object.size(mhpo_db), units="Kb")

## 691.9 Kb

print(object.size(f_mhpo_db), units="Kb")

## 23.4 Kb

compare_MDB(former=mhpo_db, new=f_mhpo_db) %>% 
   DT::datatable(
      rownames=FALSE,
      width="75%",
      options=list(dom="t", pageLength=nrow(.))
   )

Adding information about an MDB

In the table above we can see that several pieces of information are expected in an MDB object even if not mandatory (title, description, url, version, maintainer, timestamp). They can be provided in the dbInfo parameter of the MDB creator function (e.g. memoMDB()) or added afterward:

• title, description and url are used to give more details about the scope of the data and their origin.

db_info(mhpo_db)$title <- "Very small extract of the human phenotype ontology"
db_info(mhpo_db)$description <- "For demonstrating ReDaMoR and TKCat capabilities, a very few information from the HPO (human phenotype ontology) has been extracted"
db_info(mhpo_db)$url <- "https://hpo.jax.org/"

• version and maintainer are related to db information and the data model whereas timestamp should be used to document the data themselves.

db_info(mhpo_db)$version <- "0.1"
db_info(mhpo_db)$maintainer <- "Patrice Godard"
db_info(mhpo_db)$timestamp <- Sys.time()

All this information is displayed when printing the object:

mhpo_db

## memoMDB miniHPO (version 0.1, Patrice Godard): Very small extract of the human phenotype ontology
##    - 3 tables with 10 fields
## 
## No collection member
## 
## For demonstrating ReDaMoR and TKCat capabilities, a very few information from the HPO (human phenotype ontology) has been extracted
## (https://hpo.jax.org/)
## 
## Timestamp: 2024-11-03 04:06:32.30324
## 

Documenting collection members

In the HPO example, one table regards human phenotypes (HPO_hp) and another human diseases (HPO_diseases). These concepts are general and referenced in many other knowledge or data resources (e.g. database providing information about disease genetics). Therefore, documenting formally such concepts will help to identify how to connect the HPO example to other resources referencing the same or related concepts.

In TKCat, these central concepts are referred as members of collections. Collections are pre-defined and members must be documented according to this definition. There are currently two collections provided within the TKCat package:

list_local_collections()

## # A tibble: 2 × 2
##   title     description                                  
##   <chr>     <chr>                                        
## 1 BE        Collection of biological entity (BE) concepts
## 2 Condition Collection of condition concepts

Additional collections can be defined by users according to their needs. Further information about collections implementation is provided in the appendix.

So far, there is no collection member documented in the HPO example described above, as indicated by the “No collection member” statement displayed when printing the object:

mhpo_db

## memoMDB miniHPO (version 0.1, Patrice Godard): Very small extract of the human phenotype ontology
##    - 3 tables with 10 fields
## 
## No collection member
## 
## For demonstrating ReDaMoR and TKCat capabilities, a very few information from the HPO (human phenotype ontology) has been extracted
## (https://hpo.jax.org/)
## 
## Timestamp: 2024-11-03 04:06:32.30324
## 

However, as just discussed, the HPO_hp table refers to human phenotypes and the HPO_diseases table to human diseases. These concept corresponds to conditions and those tables can be documented as member of the Condition collection.

Condition members are documented calling the add_collection_member() function on the MDB object. The two other main arguments are the name of the collection and the name of the table in the MDB object. The other arguments to be provided depend on the collection. For Condition members, three additional arguments must be provided:

• condition indicate the type of the condition (“Phenotype” or “Disease”)
• source a reference source of the condition identifier
• identifier a condition identifier

The functions get_local_collection() and show_collection_def() can be used together to identify valid arguments:

get_local_collection("Condition") %>%
   show_collection_def()

## Condition collection: Collection of condition concepts
## Arguments (non-mandatory arguments are between parentheses):
##    - condition:
##       + static: logical
##       + value: character
##    - source:
##       + static: logical
##       + value: character
##    - identifier:
##       + static: logical
##       + value: character

When calling add_collection_member(), these arguments must be provided as a list with 2 elements named “value” (a character) and “static” (a logical). If “static” is TRUE, “value” corresponds to the information shared by all the rows of the table. If “static” is FALSE, “value” indicates the name of the column which provides this information for each row.

The example below shows how the HPO_hp table is documented as a member of the Condition collection.

mhpo_db$HPO_hp

## # A tibble: 500 × 4
##    id      name                                              description   level
##    <chr>   <chr>                                             <chr>         <int>
##  1 0000002 Abnormality of body height                        Deviation fr…     3
##  2 0000009 Functional abnormality of the bladder             Dysfunction …     6
##  3 0000014 Abnormality of the bladder                        An abnormali…     5
##  4 0000017 Nocturia                                          Abnormally i…     7
##  5 0000019 Urinary hesitancy                                 Difficulty i…     7
##  6 0000021 Megacystis                                        Dilatation o…     8
##  7 0000022 Abnormality of male internal genitalia            An abnormali…     6
##  8 0000024 Prostatitis                                       The presence…     8
##  9 0000025 Functional abnormality of male internal genitalia <NA>              6
## 10 0000030 Testicular gonadoblastoma                         The presence…     9
## # ℹ 490 more rows

mhpo_db <- add_collection_member(
   mhpo_db, collection="Condition", table="HPO_hp",
   condition=list(value="Phenotype", static=TRUE),
   source=list(value="HP", static=TRUE),
   identifier=list(value="id", static=FALSE)
)

All rows in this table correspond to a condition of type “Phenotype” (condition=list(value="Phenotype", static=TRUE)). The phenotype identifiers are all taken from the same source, “HP” (source=list(value="HP", static=TRUE)). The phenotype identifiers are provided in the “id” column of the table (identifier=list(value="id", static=FALSE)).

The example below shows how the HPO_disease table is documented also as a member of the Condition collection. In this case, the source of disease identifier can be different from one row to the other and is provided in the “db” column (source=list(value="db", static=FALSE)).

mhpo_db <- add_collection_member(
   mhpo_db, collection="Condition", table="HPO_diseases",
   condition=list(value="Disease", static=TRUE),
   source=list(value="db", static=FALSE),
   identifier=list(value="id", static=FALSE)
)

Now, the existence of collection members is shown when printing the MDB object:

mhpo_db

## memoMDB miniHPO (version 0.1, Patrice Godard): Very small extract of the human phenotype ontology
##    - 3 tables with 10 fields
## 
## Collection members: 
##    - 2 Condition members
## 
## For demonstrating ReDaMoR and TKCat capabilities, a very few information from the HPO (human phenotype ontology) has been extracted
## (https://hpo.jax.org/)
## 
## Timestamp: 2024-11-03 04:06:32.30324
## 

And the documented collection members of an MDB can be displayed as following:

collection_members(mhpo_db)

## # A tibble: 6 × 9
##   collection cid                   resource   mid table field static value type 
##   <chr>      <chr>                 <chr>    <int> <chr> <chr> <lgl>  <chr> <chr>
## 1 Condition  miniHPO_Condition_1.0 miniHPO      1 HPO_… cond… TRUE   Phen… <NA> 
## 2 Condition  miniHPO_Condition_1.0 miniHPO      1 HPO_… sour… TRUE   HP    <NA> 
## 3 Condition  miniHPO_Condition_1.0 miniHPO      1 HPO_… iden… FALSE  id    <NA> 
## 4 Condition  miniHPO_Condition_1.0 miniHPO      2 HPO_… cond… TRUE   Dise… <NA> 
## 5 Condition  miniHPO_Condition_1.0 miniHPO      2 HPO_… sour… FALSE  db    <NA> 
## 6 Condition  miniHPO_Condition_1.0 miniHPO      2 HPO_… iden… FALSE  id    <NA>

The use of collection members to link or integrate different MDBs will be described later in this document

Writing an MDB in files

Once an MDB has been created and documented in can be written in a directory:

tmpDir <- tempdir()
as_fileMDB(mhpo_db, path=tmpDir, htmlModel=FALSE)

The structure of the created directory is the following:

## miniHPO                                         
##  ¦--DESCRIPTION.json                            
##  ¦--data                                        
##  ¦   ¦--HPO_diseaseHP.txt.gz                    
##  ¦   ¦--HPO_diseases.txt.gz                     
##  ¦   °--HPO_hp.txt.gz                           
##  °--model                                       
##      ¦--Collections                             
##      ¦   °--Condition-miniHPO_Condition_1.0.json
##      °--miniHPO.json

All the data are in the data folder whereas the data model and collection members are written in json files in the model folder. The DESCRIPTION.json file gather db information and information about how to read the data files (i.e. delim, na).

This folder can be shared and it’s then easy to get all the data and the corresponding documentation from it back in R:

read_fileMDB(file.path(tmpDir, "miniHPO"))

## miniHPO
## SUCCESS
## 
## Check configuration
##    - Optional checks: 
##    - Maximum number of records: 10

## fileMDB miniHPO (version 0.1, Patrice Godard): Very small extract of the human phenotype ontology
##    - 3 tables with 10 fields
## 
## Collection members: 
##    - 2 Condition members
## 
## For demonstrating ReDaMoR and TKCat capabilities, a very few information from the HPO (human phenotype ontology) has been extracted
## (https://hpo.jax.org/)
## 
## Timestamp: 2024-11-03 04:06:32
## 

Also writing these data and related information in text files make them convenient to share with people using them in other analytical environments than R.

Loading example data

To illustrate how MDBs can be used, some example data are provided within the ReDaMoR and the TKCat package. The following paragraphs show how to load them in the R session.

file_hpo <- read_fileMDB(
   path=system.file("examples/HPO-subset", package="ReDaMoR"),
   dataModel=system.file("examples/HPO-model.json", package="ReDaMoR"),
   dbInfo=list(
      "name"="HPO",
      "title"="Data extracted from the HPO database",
      "description"=paste(
         "This is a very small subset of the HPO!",
         "Visit the reference URL for more information."
      ),
      "url"="http://human-phenotype-ontology.github.io/"
   )
)

## HPO
## SUCCESS
## 
## Check configuration
##    - Optional checks: 
##    - Maximum number of records: 10

plot(data_model(file_hpo))

file_hpo <- file_hpo %>% 
   add_collection_member(
      collection="Condition", table="HPO_hp",
      condition=list(value="Phenotype", static=TRUE),
      source=list(value="HP", static=TRUE),
      identifier=list(value="id", static=FALSE)
   ) %>% 
   add_collection_member(
      collection="Condition", table="HPO_diseases",
      condition=list(value="Disease", static=TRUE),
      source=list(value="db", static=FALSE),
      identifier=list(value="id", static=FALSE)
   )

file_clinvar <- read_fileMDB(
   path=system.file("examples/ClinVar", package="TKCat")
)

## ClinVar
## SUCCESS
## 
## Check configuration
##    - Optional checks: 
##    - Maximum number of records: 10

file_clinvar

## fileMDB ClinVar (version 0.9, Patrice Godard <[email protected]>): Data extracted from the ClinVar database
##    - 21 tables with 86 fields
## 
## Collection members: 
##    - 1 BE member
##    - 2 Condition members
## 
## ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. This is a very small subset of ClinVar! Visit the reference URL for more information.
## (https://www.ncbi.nlm.nih.gov/clinvar/)
## 
## 

file_chembl <- read_fileMDB(
   path=system.file("examples/CHEMBL", package="TKCat")
)

## CHEMBL
## SUCCESS
## 
## Check configuration
##    - Optional checks: 
##    - Maximum number of records: 10

file_chembl

## fileMDB CHEMBL (version 0.2, Liesbeth François <[email protected]>): Data extracted from the CHEMBL database
##    - 10 tables with 61 fields
## 
## Collection members: 
##    - 1 BE member
##    - 1 Condition member
## 
## CHEMBL is a manually curated chemical database of bioactive molecules with drug-like properties. This is a very small subset of CHEMBL! Visit the reference URL for more information.
## (https://www.ebi.ac.uk/chembl/)
## 
## 

MDB implementations

There are 3 main implementations of MDBs:

• fileMDB objects keep the data in files and load them only when requested by the user. These implementation is the first one which is used when reading MDB as demonstrated in the examples above.

• memoMDB objects have all the data loaded in memory. These objects are very easy to use but can take time to load and can use a lot of memory.

• chMDB objects get the data from a ClickHouse database providing a catalog of MDBs as described in the dedicated section.

The different implementations can be converted to each others using as_fileMDB(), as_memoMDB() and as_chMDB() functions.

memo_clinvar <- as_memoMDB(file_clinvar)
object.size(file_clinvar) %>% print(units="Kb")

## 155.2 Kb

object.size(memo_clinvar) %>% print(units="Kb")

## 760.5 Kb

A fourth implementation is metaMDB which combines several MDBs glued together with relational tables (see the Merging with collections part).

Most of the functions described below work with any MDB implementation, and a few functions are specific to each implementation.

Exploring information

General information can be retrieved (and potentialy updated) using the db_info() function.

db_info(file_clinvar)

## $name
## [1] "ClinVar"
## 
## $title
## [1] "Data extracted from the ClinVar database"
## 
## $description
## [1] "ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. This is a very small subset of ClinVar! Visit the reference URL for more information."
## 
## $url
## [1] "https://www.ncbi.nlm.nih.gov/clinvar/"
## 
## $version
## [1] "0.9"
## 
## $maintainer
## [1] "Patrice Godard <[email protected]>"
## 
## $timestamp
## [1] NA

As shown above the data model of an MDB can be retrieved and plot the following way.

plot(data_model(file_clinvar))

Tables names can be listed with the names() function and potentially renamed with names()<- or rename() functions (the tables have been renamed here to improve the readability of the following examples).

names(file_clinvar)

##  [1] "ClinVar_ReferenceClinVarAssertion" "ClinVar_rcvaVariant"              
##  [3] "ClinVar_ClinVarAssertions"         "ClinVar_rcvaInhMode"              
##  [5] "ClinVar_rcvaObservedIn"            "ClinVar_rcvaTraits"               
##  [7] "ClinVar_clinSigOrder"              "ClinVar_revStatOrder"             
##  [9] "ClinVar_variants"                  "ClinVar_cvaObservedIn"            
## [11] "ClinVar_cvaSubmitters"             "ClinVar_traits"                   
## [13] "ClinVar_varEntrez"                 "ClinVar_varAttributes"            
## [15] "ClinVar_varCytoLoc"                "ClinVar_varNames"                 
## [17] "ClinVar_varSeqLoc"                 "ClinVar_varXRef"                  
## [19] "ClinVar_traitCref"                 "ClinVar_traitNames"               
## [21] "ClinVar_entrezNames"

file_clinvar <- file_clinvar %>% 
   set_names(sub("ClinVar_", "", names(.))) 
names(file_clinvar)

##  [1] "ReferenceClinVarAssertion" "rcvaVariant"              
##  [3] "ClinVarAssertions"         "rcvaInhMode"              
##  [5] "rcvaObservedIn"            "rcvaTraits"               
##  [7] "clinSigOrder"              "revStatOrder"             
##  [9] "variants"                  "cvaObservedIn"            
## [11] "cvaSubmitters"             "traits"                   
## [13] "varEntrez"                 "varAttributes"            
## [15] "varCytoLoc"                "varNames"                 
## [17] "varSeqLoc"                 "varXRef"                  
## [19] "traitCref"                 "traitNames"               
## [21] "entrezNames"

The different collection members of an MDBs are listed with the collection_members() function.

collection_members(file_clinvar)

## # A tibble: 10 × 9
##    collection cid                  resource   mid table field static value type 
##    <chr>      <chr>                <chr>    <int> <chr> <chr> <lgl>  <chr> <chr>
##  1 Condition  ClinVar_conditions_… ClinVar      2 trai… cond… TRUE   Dise… <NA> 
##  2 Condition  ClinVar_conditions_… ClinVar      2 trai… iden… FALSE  id    <NA> 
##  3 Condition  ClinVar_conditions_… ClinVar      2 trai… sour… TRUE   Clin… <NA> 
##  4 Condition  ClinVar_conditions_… ClinVar      1 trai… cond… TRUE   Dise… <NA> 
##  5 Condition  ClinVar_conditions_… ClinVar      1 trai… iden… FALSE  id    <NA> 
##  6 Condition  ClinVar_conditions_… ClinVar      1 trai… sour… FALSE  db    <NA> 
##  7 BE         ClinVar_BE_1.0       ClinVar      1 entr… be    TRUE   Gene  <NA> 
##  8 BE         ClinVar_BE_1.0       ClinVar      1 entr… iden… FALSE  entr… <NA> 
##  9 BE         ClinVar_BE_1.0       ClinVar      1 entr… orga… TRUE   Homo… Scie…
## 10 BE         ClinVar_BE_1.0       ClinVar      1 entr… sour… TRUE   Entr… <NA>

The following functions are use to get the number of tables, the number of fields per table and the number of records.

length(file_clinvar)        # Number of tables

## [1] 21

lengths(file_clinvar)       # Number of fields per table

## ReferenceClinVarAssertion               rcvaVariant         ClinVarAssertions 
##                         8                         2                         4 
##               rcvaInhMode            rcvaObservedIn                rcvaTraits 
##                         2                         6                         3 
##              clinSigOrder              revStatOrder                  variants 
##                         2                         2                         3 
##             cvaObservedIn             cvaSubmitters                    traits 
##                         4                         3                         2 
##                 varEntrez             varAttributes                varCytoLoc 
##                         3                         5                         2 
##                  varNames                 varSeqLoc                   varXRef 
##                         3                        18                         4 
##                 traitCref                traitNames               entrezNames 
##                         4                         3                         3

count_records(file_clinvar) # Number of records per table

## ReferenceClinVarAssertion               rcvaVariant         ClinVarAssertions 
##                       166                       166                       409 
##               rcvaInhMode            rcvaObservedIn                rcvaTraits 
##                        16                       337                       166 
##              clinSigOrder              revStatOrder                  variants 
##                        11                         2                       138 
##             cvaObservedIn             cvaSubmitters                    traits 
##                       412                       416                        18 
##                 varEntrez             varAttributes                varCytoLoc 
##                       145                      2262                       138 
##                  varNames                 varSeqLoc                   varXRef 
##                       188                       280                       244 
##                 traitCref                traitNames               entrezNames 
##                        50                        44                        20

The count_records() function can take a lot of time when dealing with fileMDB objects if the data files are very large. In such case it could be more efficient to list data file size instead.

data_file_size(file_clinvar, hr=TRUE)

## # A tibble: 21 × 3
##    table                     size   compressed
##    <chr>                     <chr>  <lgl>     
##  1 ReferenceClinVarAssertion 4.6 KB TRUE      
##  2 rcvaVariant               947 B  TRUE      
##  3 ClinVarAssertions         4.2 KB TRUE      
##  4 rcvaInhMode               152 B  TRUE      
##  5 rcvaObservedIn            1.4 KB TRUE      
##  6 rcvaTraits                788 B  TRUE      
##  7 clinSigOrder              145 B  TRUE      
##  8 revStatOrder              101 B  TRUE      
##  9 variants                  2.1 KB TRUE      
## 10 cvaObservedIn             1.8 KB TRUE      
## # ℹ 11 more rows

Pulling, subsetting and combining

There are several possible ways to pull data tables from MDBs. The following lines return the same result displayed below (only once).

data_tables(file_clinvar, "traitNames")[[1]]
file_clinvar[["traitNames"]]
file_clinvar$"traitNames"
file_clinvar %>% pull(traitNames)

## # A tibble: 44 × 3
##     t.id name                                                              type 
##    <int> <chr>                                                             <chr>
##  1   912 Chudley-McCullough syndrome                                       Pref…
##  2   912 Deafness, autosomal recessive 82                                  Alte…
##  3   912 Deafness, bilateral sensorineural, and hydrocephalus due to fora… Alte…
##  4   912 Deafness, sensorineural, with partial agenesis of the corpus cal… Alte…
##  5  1352 CTSD-Related Neuronal Ceroid-Lipofuscinosis                       Alte…
##  6  1352 Ceroid lipofuscinosis neuronal Cathepsin D-deficient              Alte…
##  7  1352 Neuronal ceroid lipofuscinosis 10                                 Pref…
##  8  1352 Neuronal ceroid lipofuscinosis due to Cathepsin D deficiency      Alte…
##  9  1481 Diabetes mellitus, neonatal, with congenital hypothyroidism       Pref…
## 10  1481 NDH SYNDROME                                                      Alte…
## # ℹ 34 more rows

MDBs can also be subset and combined. The corresponding functions ensure that the data model is fulfilled by the data tables.

file_clinvar[1:3]

## fileMDB ClinVar (version 0.9, Patrice Godard <[email protected]>): Data extracted from the ClinVar database
##    - 3 tables with 14 fields
## 
## No collection member
## 
## ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. This is a very small subset of ClinVar! Visit the reference URL for more information.
## (https://www.ncbi.nlm.nih.gov/clinvar/)
## 
## 

if(igraph_available){
   c(file_clinvar[1:3], file_hpo[c(1,5,7)]) %>% 
      data_model() %>% auto_layout(force=TRUE) %>% plot()
}else{
   c(file_clinvar[1:3], file_hpo[c(1,5,7)]) %>% 
      data_model() %>% plot()
}

The function c() concatenates the provided MDB after checking that tables names are not duplicated. It does not integrate the data with any relational table. This can achieved by merging the MDBs as described in the Merging with collections section.

Filtering and joining

An MDB can be filtered by filtering one or several tables based on field values. The filtering is propagated to other tables using the embedded data model.

In the example below, the file_clinvar object is filtered in order to focus on a few genes with pathogenic variants. The table below compares the number of rows before (“ori”) and after (“filt”) filtering.

filtered_clinvar <- file_clinvar %>%
   filter(
      entrezNames = symbol %in% c("PIK3R2", "UGT1A8")
   ) %>% 
   slice(ReferenceClinVarAssertion=grep(
      "pathogen",
      .$ReferenceClinVarAssertion$clinicalSignificance,
      ignore.case=TRUE
   ))
left_join(
   dims(file_clinvar) %>% select(name, nrow),
   dims(filtered_clinvar) %>% select(name, nrow),
   by="name",
   suffix=c("_ori", "_filt")
)

## # A tibble: 21 × 3
##    name                      nrow_ori nrow_filt
##    <chr>                        <dbl>     <int>
##  1 ReferenceClinVarAssertion      166         4
##  2 rcvaVariant                    166         4
##  3 ClinVarAssertions              409        15
##  4 rcvaInhMode                     16         0
##  5 rcvaObservedIn                 337        10
##  6 rcvaTraits                     166         4
##  7 clinSigOrder                    11         3
##  8 revStatOrder                     2         1
##  9 variants                       138         3
## 10 cvaObservedIn                  412        15
## # ℹ 11 more rows

The object returned by filter() or slice is a memoMDB: all the data are in memory.

Tables can be easily joined to get diseases associated to the genes of interest in a single table as shown below.

gene_traits <- filtered_clinvar %>% 
   join_mdb_tables(
      "entrezNames", "varEntrez", "variants", "rcvaVariant",
      "ReferenceClinVarAssertion", "rcvaTraits", "traits"
   )
gene_traits$entrezNames %>%
   select(symbol, name, variants.type, variants.name, traitType, traits.name)

## # A tibble: 4 × 6
##   symbol name                  variants.type variants.name traitType traits.name
##   <chr>  <chr>                 <chr>         <chr>         <chr>     <chr>      
## 1 PIK3R2 phosphoinositide-3-k… single nucle… NM_005027.4(… Disease   Megalencep…
## 2 PIK3R2 phosphoinositide-3-k… single nucle… NM_005027.4(… Disease   not provid…
## 3 PIK3R2 phosphoinositide-3-k… single nucle… NM_005027.4(… Disease   not provid…
## 4 UGT1A8 UDP glucuronosyltran… Microsatelli… UGT1A1*28     Disease   Gilbert's …

Merging MDBs with collections

Until now, we have seen how to use individual MDB by exploring general information about it, extracting tables, filtering and joining data. This part shows how to use collections to identify relationships between MDBs and to leverage these relationships to integrate them. Documenting collection members has been described above and further information about collections implementation is provided in the appendix.

file_chembl$CHEMBL_component_sequence

## # A tibble: 35 × 5
##    component_id accession organism                  db_source db_version
##           <int> <chr>     <chr>                     <chr>     <chr>     
##  1          259 P15260    Homo sapiens              Uniprot   2019_09   
##  2          327 Q99062    Homo sapiens              Uniprot   2019_09   
##  3          752 P35563    Rattus norvegicus         Uniprot   2019_09   
##  4          917 P07339    Homo sapiens              Uniprot   2019_09   
##  5         1807 Q54A96    Plasmodium falciparum     Uniprot   2019_09   
##  6         2180 P67774    Bos taurus                Uniprot   2019_09   
##  7         2398 P25098    Homo sapiens              Uniprot   2019_09   
##  8         2541 Q8II92    Plasmodium falciparum 3D7 Uniprot   2019_09   
##  9         3803 Q64346    Rattus norvegicus         Uniprot   2019_09   
## 10         4395 O60502    Homo sapiens              Uniprot   2019_09   
## # ℹ 25 more rows

file_clinvar$entrezNames

## # A tibble: 20 × 3
##       entrez name                                                         symbol
##        <int> <chr>                                                        <chr> 
##  1      1509 cathepsin D                                                  CTSD  
##  2      1903 sphingosine-1-phosphate receptor 3                           S1PR3 
##  3      3300 DnaJ heat shock protein family (Hsp40) member B2             DNAJB2
##  4      3423 iduronate 2-sulfatase                                        IDS   
##  5      3910 laminin subunit alpha 4                                      LAMA4 
##  6      5296 phosphoinositide-3-kinase regulatory subunit 2               PIK3R2
##  7      6748 signal sequence receptor subunit 4                           SSR4  
##  8      7633 zinc finger protein 79                                       ZNF79 
##  9     22906 trafficking kinesin protein 1                                TRAK1 
## 10     23155 chloride channel CLIC like 1                                 CLCC1 
## 11     26251 potassium voltage-gated channel modifier subfamily G member… KCNG2 
## 12     29851 inducible T cell costimulator                                ICOS  
## 13     54576 UDP glucuronosyltransferase family 1 member A8               UGT1A8
## 14     57684 zinc finger and BTB domain containing 26                     ZBTB26
## 15    115948 outer dynein arm docking complex subunit 3                   ODAD3 
## 16    139716 GRB2 associated binding protein 3                            GAB3  
## 17    169792 GLIS family zinc finger 3                                    GLIS3 
## 18    407054 microRNA 98                                                  MIR98 
## 19    441531 phosphoglycerate mutase family member 4                      PGAM4 
## 20 105373557 serous ovarian cancer associated RNA                         SOCAR

list_local_collections()

## # A tibble: 2 × 2
##   title     description                                  
##   <chr>     <chr>                                        
## 1 BE        Collection of biological entity (BE) concepts
## 2 Condition Collection of condition concepts

collection_members(file_chembl, "BE")

## # A tibble: 4 × 9
##   collection cid           resource   mid table         field static value type 
##   <chr>      <chr>         <chr>    <int> <chr>         <chr> <lgl>  <chr> <chr>
## 1 BE         CHEMBL_BE_1.0 CHEMBL       1 CHEMBL_compo… be    TRUE   Pept… <NA> 
## 2 BE         CHEMBL_BE_1.0 CHEMBL       1 CHEMBL_compo… iden… FALSE  acce… <NA> 
## 3 BE         CHEMBL_BE_1.0 CHEMBL       1 CHEMBL_compo… sour… FALSE  db_s… <NA> 
## 4 BE         CHEMBL_BE_1.0 CHEMBL       1 CHEMBL_compo… orga… FALSE  orga… Scie…

collection_members(file_clinvar, "BE")

## # A tibble: 4 × 9
##   collection cid            resource   mid table       field  static value type 
##   <chr>      <chr>          <chr>    <int> <chr>       <chr>  <lgl>  <chr> <chr>
## 1 BE         ClinVar_BE_1.0 ClinVar      1 entrezNames be     TRUE   Gene  <NA> 
## 2 BE         ClinVar_BE_1.0 ClinVar      1 entrezNames ident… FALSE  entr… <NA> 
## 3 BE         ClinVar_BE_1.0 ClinVar      1 entrezNames organ… TRUE   Homo… Scie…
## 4 BE         ClinVar_BE_1.0 ClinVar      1 entrezNames source TRUE   Entr… <NA>

get_shared_collections(filtered_clinvar, file_chembl)

## # A tibble: 3 × 5
##   collection table.x     mid.x table.y                   mid.y
##   <chr>      <chr>       <int> <chr>                     <int>
## 1 Condition  traits          2 CHEMBL_drug_indication        1
## 2 Condition  traitCref       1 CHEMBL_drug_indication        1
## 3 BE         entrezNames     1 CHEMBL_component_sequence     1

try(BED::connectToBed(a))

## Error in eval(expr, envir) : object 'a' not found

bedCheck <- try(BED::checkBedConn())
if(!inherits(bedCheck, "try-error") && bedCheck){
   sel_coll <- get_shared_collections(file_clinvar, file_chembl) %>% 
      filter(collection=="BE")
   filtered_cv_chembl <- merge(
      x=file_clinvar,
      y=file_chembl,
      by=sel_coll,
      dmAutoLayout=igraph_available
   )
}

get_shared_collections(file_hpo, file_clinvar)

## # A tibble: 4 × 5
##   collection table.x      mid.x table.y   mid.y
##   <chr>      <chr>        <int> <chr>     <int>
## 1 Condition  HPO_hp           1 traits        2
## 2 Condition  HPO_hp           1 traitCref     1
## 3 Condition  HPO_diseases     2 traits        2
## 4 Condition  HPO_diseases     2 traitCref     1

sel_coll <- get_shared_collections(file_hpo, file_clinvar) %>% 
   filter(table.x=="HPO_diseases", table.y=="traitCref") %>% 
   mutate(collection=NA)
sel_coll

## # A tibble: 1 × 5
##   collection table.x      mid.x table.y   mid.y
##   <lgl>      <chr>        <int> <chr>     <int>
## 1 NA         HPO_diseases     2 traitCref     1

hpo_clinvar <- merge(
   file_hpo, file_clinvar, by=sel_coll, dmAutoLayout=igraph_available
)
plot(data_model(hpo_clinvar))

hpo_clinvar$HPO_diseases_traitCref

## # A tibble: 1,950 × 2
##    db       id    
##    <chr>    <chr> 
##  1 DECIPHER 15    
##  2 DECIPHER 45    
##  3 DECIPHER 65    
##  4 OMIM     100050
##  5 OMIM     100650
##  6 OMIM     101800
##  7 OMIM     102500
##  8 OMIM     102510
##  9 OMIM     102700
## 10 OMIM     102800
## # ℹ 1,940 more rows

Local TKCat

MDB can be gathered in a TKCat (Tailored Knowledge Catalog) object.

k <- TKCat(file_hpo, file_clinvar)

Gathering MDBs in such a catalog facilitate their exploration and their preparation for potential integration. Several functions are available to achieve this goal.

list_MDBs(k)                     # list all the MDBs in a TKCat object

## # A tibble: 2 × 7
##   name    title         description url   version maintainer timestamp
##   <chr>   <chr>         <chr>       <chr> <chr>   <chr>      <dttm>   
## 1 HPO     Data extract… This is a … http… <NA>    <NA>       NA       
## 2 ClinVar Data extract… ClinVar is… http… 0.9     Patrice G… NA

get_MDB(k, "HPO")                # get a specific MDBs from the catalog

## fileMDB HPO: Data extracted from the HPO database
##    - 9 tables with 25 fields
## 
## Collection members: 
##    - 2 Condition members
## 
## This is a very small subset of the HPO! Visit the reference URL for more information.
## (http://human-phenotype-ontology.github.io/)
## 
## 

search_MDB_tables(k, "disease")  # Search table about "disease"

## # A tibble: 3 × 3
##   resource name                comment                 
##   <chr>    <chr>               <chr>                   
## 1 HPO      HPO_diseases        Diseases                
## 2 HPO      HPO_diseaseHP       HP presented by diseases
## 3 HPO      HPO_diseaseSynonyms Disease synonyms

search_MDB_fields(k, "disease")  # Search a field about "disease"

## # A tibble: 8 × 7
##   resource table               name    type      nullable unique comment        
##   <chr>    <chr>               <chr>   <chr>     <lgl>    <lgl>  <chr>          
## 1 HPO      HPO_diseases        db      character FALSE    FALSE  Disease databa…
## 2 HPO      HPO_diseases        id      character FALSE    FALSE  Disease ID     
## 3 HPO      HPO_diseases        label   character FALSE    FALSE  Disease lable …
## 4 HPO      HPO_diseaseHP       db      character FALSE    FALSE  Disease databa…
## 5 HPO      HPO_diseaseHP       id      character FALSE    FALSE  Disease ID     
## 6 HPO      HPO_diseaseSynonyms db      character FALSE    FALSE  Disease databa…
## 7 HPO      HPO_diseaseSynonyms id      character FALSE    FALSE  Disease ID     
## 8 HPO      HPO_diseaseSynonyms synonym character FALSE    FALSE  Disease synonym

collection_members(k)            # Get collection members of the different MDBs

## # A tibble: 5 × 3
##   resource collection table       
##   <chr>    <chr>      <chr>       
## 1 HPO      Condition  HPO_hp      
## 2 HPO      Condition  HPO_diseases
## 3 ClinVar  Condition  traits      
## 4 ClinVar  Condition  traitCref   
## 5 ClinVar  BE         entrezNames

c(k, TKCat(file_chembl))         # Merge 2 TKCat objects

## TKCat gathering 3 MDB objects

The function explore_MDBs() launches a shiny interface to explore MDBs in a TKCat object. This exploration interface can be easily deployed using an app.R file with content similar to the one below.

library(TKCat)
explore_MDBs(k, download=TRUE)

In this interface the users can explore the resources available in the catalog. They can browse the data model of each of them with some sample data. They can also search for information provided in resources, tables or fields. Finally, if the parameter download is set to TRUE, the users will also be able to download the data: either each table individually or an archive of the whole MDB.

chTKCat

A chTKCat object is a catalog of MDB as a TKCat object described above but relying on a ClickHouse database. This part focuses on using and querying a chTKCat object. The installation and the initialization of a ClickHouse database ready for TKCat are described below in the appendix.

The connection to the ClickHouse TKCat database is achieved using the chTKCat() function.

k <- chTKCat(
   host="localhost",                     # default parameter
   port=9111L,                           # default parameter
   drv=ClickHouseHTTP::ClickHouseHTTP(), # default parameter
   user="default",                       # default parameter
   password=""                           # if not provided the
                                         # password is requested interactively 
)

By default, this function connects anonymously (“default” user without password) to the database, using the HTTP interface of ClickHouse thanks to the ClickHouseHTTP driver. If the database is configured appropriately (see appendix), connection can be achieved through HTTPS with or without SSL peer verification (see the manual of ClickHouseHTTP::\ClickHouseHTTPDriver-class`for further information). Also, theRClickhouse::clickhouse()driver from the [RClickhouse][rclickhouse] package can be used (drvparameter of thechTKCat()` function) to leverage the native TCP interface of ClickHouse which has the strong advantage of having less overhead. But TLS wrapping is not supported yet by the RClickhouse package.

Once connected, this chTKCat object can be used as a TKCat object.

list_MDBs(k)             # get a specific MDBs from the catalog
search_MDB_tables(k, "disease")  # Search table about "disease"
search_MDB_fields(k, "disease")  # Search a field about "disease"
collection_members(k)  

explore_MDBs(k)

Pushing an MDB in a chTKCat instance

Any MDB object can be imported in a TKCat ClickHouse instance as following:

kw <- chTKCat(host="localhost", port=9111L, user="pgodard")
create_chMDB(kw, "HPO", public=TRUE)
ch_hpo <- as_chMDB(file_hpo, kw)

It is then accessible to anyone with relevant permissions on the Clickhouse database. Pushing data in a ClickHouse database works only if the user is allowed to write in the database.

Specific operations on chMDB objects

The function get_MDB() returns a chMDB object that can be used as any MDB object. The data are located in the ClickHouse database and pulled on request.

ch_hpo <- get_MDB(k, "HPO")

To avoid pulling a whole table from ClickHouse (which can take time if the table is big), SQL queries can be made on the chMDB object as shown below.

get_query(
   ch_hpo,
   query="SELECT * from HPO_diseases WHERE lower(label) LIKE '%epilep%'"
)

chTKCat operations

# In shell
docker stop tkcat_test
docker rm tkcat_test
docker volume prune -f
# Remove the folder with all the data: `$TKCAT_HOME`.`
sudo rm -rf ~/Documents/Projects/TKCat_Test

k <- chTKCat(user="pgodard")
create_chTKCat_user(
   k, login="lfrancois", contact=NA, admin=FALSE, provider=TRUE
)

k <- chTKCat(user="pgodard")
change_chTKCat_password(k, "lfrancois")
update_chTKCat_user(k, contact="email", admin=FALSE)

manage_chTKCat_users(k)

drop_chTKCat_user(k, login="lfrancois")

create_chMDB(k, "CHEMBL", public=FALSE)

set_chMDB_access(k, "CHEMBL", public=TRUE)

add_chMDB_user(k, "CHEMBL", "lfrancois", admin=TRUE)
# remove_chMDB_user(k, "CHEMBL", "lfrancois")
list_chMDB_users(k, "CHEMBL")

lc <- scan_fileMDBs("fileMDB_directory")
## The commented line below allows the exploration of the data models in lc.
# explore_MDBs(lc)
for(r in toFeed){
   message(r)
   lr <- as_memoMDB(lc[[r]])
   cr <- as_chMDB(lr, k, overwrite=FALSE)
}

empty_chMDB(k, "CHEMBL")

drop_chMDB(k, "CHEMBL")

add_chTKCat_collection(k, "BE")
list_chTKCat_collections(k)
remove_chTKCat_collection(k, "BE")

TKCat collections

Some MDBs refer to the same concepts and can be integrated accordingly. However they often use different vocabularies or scopes. Collections are used to identify such concepts and to define a way to document formally the scope used by the different members of these collections. Thanks to this formal description, tools can be used to automatically combine MDBs referring to the same collection but using different scopes, as shown above.

This appendix describes how to create TKCat Collections, document collection members and create functions to support the merging of MDBs.

list_local_collections()

get_local_collection("BE")

get_local_collection("BE") %>%
   show_collection_def()

system.file(
   "examples/CHEMBL/model/Collections/BE-CHEMBL_BE_1.0.json",
   package="TKCat"
) %>% 
   readLines() %>% paste(collapse="\n")

jsonvalidate::json_validate(
   json=system.file(
      "examples/CHEMBL/model/Collections/BE-CHEMBL_BE_1.0.json",
      package="TKCat"
   ),
   schema=get_local_collection("BE"),
   engine="ajv"
)

get_collection_mapper("BE")

Remarks about supported data format and data types

Most of the data format and data types supported by the ReDaMoR and the TKCat packages are taken into account in the examples described in the main sections of this vignette. Nevertheless, one specific data format (matrix) and one specific data type (base64) are not exemplified. This appendix provides a short description of these format and type.

d <- matrix(
   rnorm(40), nrow=10,
   dimnames=list(
      paste0("g", 1:10),
      paste0("s", 1:4)
   )
)
m <- ReDaMoR::df_to_model(d) %>% 
   ReDaMoR::rename_field("d", "row", "gene") %>%
   update_field("d", "gene", comment="Gene identifier") %>% 
   ReDaMoR::rename_field("d", "column", "sample") %>% 
   update_field("d", "sample", comment="Sample identifier") %>% 
   ReDaMoR::rename_field("d", "value", "expression") %>% 
   update_field(
      "d", "expression", nullable=FALSE, comment="Gene expression value"
   )
md <- memoMDB(list(d=d), m, list(name="Matrix example"))
plot(data_model(md))

ch_config_files <- tibble(
   name=c("config.xml", "users.xml"),
   file=c(
      base64enc::base64encode(
         system.file("ClickHouse/config.xml", package="TKCat")
      ),
      base64enc::base64encode(
         system.file("ClickHouse/users.xml", package="TKCat")
      )
   )
)
m <- df_to_model(ch_config_files) %>% 
   update_field(
      "ch_config_files", "name",
      type="base64", comment="Name of the config file",
      nullable=FALSE, unique=TRUE
   ) %>% 
   update_field(
      "ch_config_files", "file",
      type="base64", comment="Config file in base64 format",
      nullable=FALSE
   )
md <- memoMDB(
   list(ch_config_files=ch_config_files), m, list(name="base64 example")
)
plot(data_model(md))