A toolkit for ETL curation for the tranSMART data warehouse.
Project description
A toolkit for ETL curation for the tranSMART data warehouse. The TranSMART curation toolkit (tmtk) can be used to edit and validate studies prior to loading them with transmart-batch.
- Some features:
create a transmart-batch ready study from clinical data files.
load an existing study and validate its contents.
edit the transmart concept tree in The Arborist graphical editor.
create chromosomal region annotation files.
Disclaimer
tmtk is a python3 package meant to be run in Jupyter notebooks. Results for other setups may vary.
Usage
Step 1: Opening a notebook
First open a Jupyter Notebook, open a shell and change directory to some place where your data is. Then start the notebook server:
cd /path/to/studies/
jupyter notebook
This should open your browser to Jupyters file browser, create a new notebook for here.
Step 2: Using tmtk
# First import the toolkit into your environment
import tmtk
# Then create a <tmtk.Study> object by pointing to study.params of a transmart-batch study
study = tmtk.Study('~/studies/a_tm_batch_ready_study/study.params')
# Or, by using the study wizard on a directory with correctly structured, clinical data files.
# (Visit the transmart-batch documentation to find out what is expected.)
study = tmtk.wizard.create_study('~/studies/dir_with_some_clinical_data_files/')
Now we have loaded the study as a tmtk.Study object we have some interesting functions available:
# Check whether transmart-batch will find any issues with the way your study is setup
study.validate_all()
# Graphically manipulate the concept tree in this study by using The Arborist
study.call_boris()
For more, see the examples.
Installation
To install tmtk, and all dependencies, into your Python environment run:
$ python3 setup.py install
or if you want to run the tool from code:
$ python3 setup.py develop
Requirements
- These dependencies will have to be installed:
pandas
jupyter
flask
ipython
tqdm
requests
rpy2
Licence
GPLv3
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