Use cases (old notes)

As initial use cases for the FAIRtracks project, we will consider the following two scenarios:

Transforming ENCODE metadata into FAIRtracks format
Transforming TCGA metadata into FAIRtracks format

Nomenclature:

Omnipy is designed to work with content which could be classified both as data and metadata in their original context. For simplicity, we will refer to all such content as "data".

Overview of the proposed FAIRification process:

Step 1: Import data from original source:
- 1A: From API endpoints
  - Input: API endpoint producing JSON data
  - Output: JSON files (possibly with embedded JSON objects/lists [as strings])
  - Description: General interface to support various API endpoints. Import all data by crawling API endpoints providing JSON content
  - Generalizable: Partly (at least reuse of utility functions)
  - Manual/automatic: Automatic
  - Details:
    - GDC/TCGA substeps (implemented as Step objects with file input/output)
      - 1A. Filtering step:
        
        Input: parameters defining how to filter, e.g.:
        
        For all endpoints (projects, cases, files, annotations), support:
        
        Filter on list of IDs
        
        Specific number of items
        
        All
        
        Example config:
        
        projects: 2 items
        
        cases: 2 items
        
        files: all
        
        annotations: all
        
        Define standard configurations, e.g.:
        
        Default: limited extraction (3 projects * 3 cases * 5 files? (+ annotations?))
        
        All TCGA
        
        List of projects
        
        Hierarchical for loop through endpoints to create filter definitions
        
        Output: Filter definitions as four files, e.g. as JSON, as they should be input to the filter parameter to the API: ``` projects_filter.json: { "op": "in", "content": { "field": "project_id", "value": ['TCGA_ABCD', 'TCGA_BCDE'] } }
        
        cases_filter.json: { "op": "in", "content": { "field": "case_id", "value": ['1234556', '234567', '3456789', '4567890'] } }
        
        files_filter.json: { "op": "in", "content": { "field": "file_id", "value": ['1234556', '234567', '3456789', '4567890'] } }
        
        annotations.json { "op": "in", "content": { "field": "annotation_id", "value": ['1234556', '234567', '3456789', '4567890'] } } ```
      - 1B. Fetch and divide all fields step:
        
        Input: None
        
        Output: JSON files specifying all the fields of an endpoint fetched from the mapping API. The fields should be divided into chunks of a size that is small enough for the endpoints to handle. The JSON output should also specify the primary_key field, that needs to be added to all the API calls in order for the results to be joinable.
        
        Example JSON files: ``` projects_fields.json: { "primary_key": "project_id", "fields_divided": [ ["field_a", "field_b"], ["field_c.subfield_a", "field_c.subfield_b", "field_d"]
        ] }
        
        (...) # For all endpoints `` * 1C. Download from all endpoints according to the filters and the field divisions. If there is a limitation on the number of hits that the endpoint is able to return, divide into smaller API calls for a certain number of hits and concatenate the results. Make sure that proper waiting time (1 second?) is added between the calls (to not overload the endpoint). * 1D. Extract identifiers from nested objects (when present) and insert into parents objects * ENCODE: * Identify where to start (Cart? Experiment?) * To get all data for a table (double-check this):https://www.encodeproject.org/experiments/@@listing?format=json&frame=object* Download all tables directly. * #### 1b: From JSON files * _Input:_ JSON content as files * _Output:_ Pandas DataFrames (possibly with embedded JSON objects/lists) * _Description:_ Import data from files. Requires specific parsers to be implemented. * _Generalizable:_ Fully * _Manual/automatic:_ Automatic * #### 1c: From non-JSON files * _Input:_ File content in some supported format (e.g. GSuite) * _Output:_ Pandas DataFrames (possibly containing lists of identifiers as Pandas Series) + reference metadata * _Description:_ Import data from files. Requires specific parsers to be implemented. * _Generalizable:_ Partly (generating reference metadata might be tricky) * _Manual/automatic:_ Automatic * #### 1d: From database * _Input:_ Direct access to relational database * _Output:_ Pandas DataFrames (possibly containing lists of identifiers as Pandas Series) + reference metadata * _Description:_ Import data from database * _Generalizable:_ Fully * _Manual/automatic:_ Automatic * ### Step 2: JSON cleanup * _Input:_ Pandas DataFrames (possibly with embedded JSON objects/lists) * _Output:_ Pandas DataFrames (possibly containing lists of identifiers as Pandas Series) + reference metadata * _Description:_ Replace embedded objects with identifiers (possibly as lists) * _Generalizable:_ Partly (generating reference metadata might be tricky) * _Manual/automatic:_ Depending on original input * _Details:_ * If there are embedded objects from other tables: * ENCODE update: * By using theframe=objectparameter, we will not get any embedded objects from the APIs for the main tables. There is, however, some "auditing" fields that contain JSON objects. We can ignore these in the first iteration. * If the original table of the embedded objects can be retrieved directly from an API, replace such embedded objects with unique identifiers to the object in another table ( maintaining a reference to the name of the table, if needed) * Record the reference metadata(table_from, attr_from) -> (table_to, attr_to)for joins: * Example:(table: "experiment", column: "replicates") -> (table: "replicate", column: "@id")* If the original table of the embedded objects are not directly available from an API, one needs to fill out the other table with the content that is embedded in the current object, creating the table if needed. * For all fields with identifiers that reference other tables: * Record the reference metadata(table_from, attr_from) -> (table_to, attr_to)for joins. * If the field contains a list of identifiers * Convert into Pandas Series * ### Step 3: Create reference tables to satisfy 1NF * _Input:_ Pandas DataFrames (possibly containing lists of identifiers as Pandas Series) + reference metadata * _Output:_ Pandas DataFrames (original tables without reference column) [1NF] + reference tables + reference metadata * _Description:_ Move references into separate tables, transforming the tables in first normal form ([1NF](https://en.wikipedia.org/wiki/Database_normalization#Satisfying_1NF)) * _Generalizable:_ Fully * _Manual/automatic:_ Automatic * _Details:_ * For each reference pair: * Create a reference table * For each item in the "from"-reference column: * Add new rows in the reference table for each "to"-identifier, using the same " from"-identifier * Example: Table "experiment-replicate" with columns "experiment.@id", " replicate.@id" * Delete the complete column from the original table * ### Step 4: Satisfy 2NF * _Input:_ Pandas DataFrames (original tables without reference column) [1NF] + reference tables * _Output:_ Pandas DataFrames (original tables without reference column) [2NF] + reference tables * _Description:_ Automatic transformation of original tables into second normal form ([2NF](https://en.wikipedia.org/wiki/Database_normalization#Satisfying_2NF)): * _Generalizable:_ Fully (if not, we skip it) * _Manual/automatic:_ Automatic * _Details:_ * Use existing library. * ### Step 5: Satisfy 3NF * _Input:_ Pandas DataFrames (original tables without reference column) [2NF] + reference tables * _Output:_ Pandas DataFrames (original tables without reference column) [3NF] + reference tables * _Description:_ Automatic transformation of original tables into third normal form ([3NF](https://en.wikipedia.org/wiki/Database_normalization#Satisfying_3NF)): * _Generalizable:_ Fully (if not, we skip it) * _Manual/automatic:_ Automatic * _Details:_ * Use existing library. * ### Step 6: Create model map * _Input:_ Pandas DataFrames (original tables without reference column) [Any NF] + reference tables + FAIRtracks JSON schemas * _Output:_ Model map [some data structure (to be defined) mapping FAIRtracks objects and attributes to tables/columns in the original data] * _Description:_ Manual mapping of FAIRtracks objects and attributes to corresponding tables and columns in the original data. * _Generalizable:_ Fully * _Manual/automatic:_ Manual * Details: * For each FAIRtracks object: * Define a start table in the original data * For each FAIRtracks attribute: * Manually find the path (or paths) to the original table/column that this maps to * _Example:_Experiments:organism (FAIRtracks) -> Experiments.Biosamples.Organism.scientific_name* ### Step 7: Apply model map to generate initial FAIRtracks tables * _Input:_ Pandas DataFrames (original tables without reference column) [Any NF] + reference tables + Model map * _Output:_ Pandas DataFrames (initial FAIRtracks tables, possibly with multimapped attributes) * Example:Experiment.target_from_origcolumn1andExperimentl.target_from_origcolumn2contain content from two different attributes from the original data that both corresponds toExperiment.target* _Description:_ Generate initial FAIRtracks tables by applying the model map, mapping FAIRtracks attributes with one or more attributes (columns) in the original table. * _Generalizable:_ Fully * _Manual/automatic:_ Automatic * _Details:_ * For every FAIRtracks object: * Create a new pandas DataFrame * For every FAIRtracks attribute: * From the model map, get the path to the corresponding original table/column, or a list of such paths in case of multimapping * For each path: * Automatically join tables to get primary keys and attribute value in the same table: * _Example:_experiment-biosample JOIN biosample-organism JOIN organismwill create mapping table with two columns:Experiments.local_idandOrganism.scientific_name* Add column to FAIRtracks DataFrame * In case of multimodeling, record the relation between FAIRtracks attribute and corresponding multimapped attributes, e.g. by generating unique attribute names for each path, such asExperiment.target_from_origcolumn1andExperiment.target_from_origcolumn2`, which one can derive directly from the model map. * ### Step 8: Harmonize multimapped attributes * Input: Pandas DataFrames (initial FAIRtracks tables, possibly with multimapped attributes) + model map * Output: Pandas DataFrames (initial FAIRtracks tables) * Description: Harmonize multimapped attributes manually, or possibly by applying scripts * Generalizable: Limited (mostly by reusing util functions) * Manual/automatic: Mixed (possibly scriptable) * Details: * For all multimapped attributes: * Manually review values (in batch mode) and generate a single output value for each combination: * Hopefully Open Refine can be used for this. If so, one needs to implement data input/output mechanisms.
Further steps to be detailed:
- For all FAIRtracks attributes with ontology terms: Convert terms using required ontologies
- Other FAIRtracks specific value conversion
- Manual batch correction of values (possibly with errors), probably using Open Refine
- Validation of FAIRtracks document

Suggestion: we will use Pandas DataFrames as the core data structure for tables, given that the library provides the required features (specifically Foreign key and Join capabilities)

Use cases (old notes)

Nomenclature:

Overview of the proposed FAIRification process:

Step 1: Import data from original source:

1A: From API endpoints

Further steps to be detailed: