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Expand Up @@ -155,274 +155,3 @@ backends by implementing new workflow engine plugins. This is relatively easy to
architecture of Omnipy allows the user to easily switch the workflow engine at runtime. Omnipy
supports both traditional DAG-based and the more _avant garde_ code-based definition of flows. Two
workflow engines are currently supported: _local_ and _prefect_.

# 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 the `frame=object` parameter, 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_origcolumn1` and `Experimentl.target_from_origcolumn2`
contain content from two different attributes from the original data that both corresponds
to `Experiment.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 organism`
will create mapping table with two columns: `Experiments.local_id`
and `Organism.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 as `Experiment.target_from_origcolumn1`
and `Experiment.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)
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