Usage

Getting the data

First things first: you need to get the data! Once you have access to the PPMI database, log in to the database and follow these instructions:

  1. Select Download from the navigation bar at the top

  2. Select Study Data from the options that appear in the navigation bar

  3. Select ALL at the bottom of the left-hand navigation bar on the new page

  4. Click Select ALL tabular data (csv) format and then press Download>> in the top right hand corner of the page

  5. Unzip the downloaded directory and save it somewhere on your computer

Alternatively, you can use pypmi module to download the data programatically:

>>> import pypmi
>>> files = pypmi.fetch_studydata('all', user='username', password='password')  
Fetching authentication key for data download...
Requesting 113 datasets for download...
Downloading PPMI data: 17.3MB [00:33, 519kB/s]

By default, the data will be downloaded to your current directory making it easy to load them in the future, but you can optionally provide a path argument to pypmi.fetch_studydata() to specify where you would like the data to go. (Alternatively, you can set an environmental variable $PPMI_PATH to specify where they should be downloaded to; this takes precedence over the current directory.)

Loading and working with the data

Once you have the data downloaded you can use the functions to load various portions of it into tidy data frames.

For example, we can generate a number of clinical-behavioral measures:

>>> behavior = pypmi.load_behavior()
>>> behavior.columns
Index(['participant', 'visit', 'date', 'benton', 'epworth', 'gds',
       'hvlt_recall', 'hvlt_recognition', 'hvlt_retention', 'lns', 'moca',
       'pigd', 'quip', 'rbd', 'scopa_aut', 'se_adl', 'semantic_fluency',
       'stai_state', 'stai_trait', 'symbol_digit', 'systolic_bp_drop',
       'tremor', 'updrs_i', 'updrs_ii', 'updrs_iii', 'updrs_iii_a', 'updrs_iv',
       'upsit'],
      dtype='object')

The call to pypmi.load_behavior() may take a few seconds to run—there’s a lot of data to import and wrangle!

If we want to query the data with regards to, say, subject diagnosis it might be useful to load in some demographic information:

>>> demographics = pypmi.load_demographics()
>>> demographics.columns
Index(['participant', 'diagnosis', 'date_birth', 'date_diagnosis',
       'date_enroll', 'status', 'family_history', 'age', 'gender', 'race',
       'site', 'handedness', 'education'],
      dtype='object')

Now we can perform some interesting queries! As an example, let’s just ask how many individuals with Parkinson’s disease have a baseline UPDRS III score. We’ll have to use information from both data frames to answer the question:

>>> import pandas as pd
>>> updrs = (behavior.query('visit == "BL" & ~updrs_iii.isna()')
...                  .get(['participant', 'updrs_iii']))
>>> parkinsons = demographics.query('diagnosis == "pd"').get('participant')
>>> len(pd.merge(parkinsons, updrs, on='participant'))
423

And the same for healthy individuals:

>>> healthy = demographics.query('diagnosis == "hc"').get('participant')
>>> len(pd.merge(healthy, updrs))
195

There’s a lot of power gained in leveraging the pandas DataFrame objects, so take a look at the pandas documentation to see what more you can do!