Interface code to interact with data from the Ovara.net biobank.
Project description
marburg\_biobank
================
Introduction
------------
The marburg\_biobank python module offers a high level interface to the
data sets stored in the [Ovarian Cancer Effusion Biobank and
Database])(https://www.ovara.net/biobank).
The basic usage is as follows:
.. code:: python
import marburg_biobank
db = marburg_biobank.OvcaBiobank("marburg_ovca_revision_5.zip") # you need to download that file from your biobank.
print(db.list_datasets())
df_wide = db.get_wide('transcriptomics/rnaseq') # to retrieve the data in a one sample per column / one row per measured variable format
df_tall = db.get_dataset('transcriptomics/rnaseq') # to retrieve the data in one row per data point format
Data formats available
----------------------
wide
~~~~
Using ``db.get_wide(dataset)``:
A pandas DataFrame that looks like this
+------------------------+------------------+-----------------+-------------------------+
| Index | Patient12, TAM | Patient12, TU | PatientX, Compartment |
+========================+==================+=================+=========================+
| **VariableA, unitA** | 23.23 | 112.2 | nan |
+------------------------+------------------+-----------------+-------------------------+
| **VariableB, unitB** | 3.23 | 12.2 | 12.7 |
+------------------------+------------------+-----------------+-------------------------+
Caveats: If a dataset has only one compartment, the compartment
information is ommited by get\_wide(), unless
.get\_wide(standardized=True) is used. The same applies for the unit in
the index. If there is a 'name' column in dataset, it get's added to the
index, regardless of the value of standardized.
tall
~~~~
Using: ``db.get_dataset(dataset)``):
A pandas DataFrame that looks like this
+-------------+---------+-------------+---------------+---------+-----------------------+
| variable | unit | patient | compartment | value | optional columns... |
+=============+=========+=============+===============+=========+=======================+
| variableA | unitA | Patient12 | TAM | 23.23 |
+-------------+---------+-------------+---------------+---------+-----------------------+
| variableA | unitA | Patient12 | TU | 112.2 |
+-------------+---------+-------------+---------------+---------+-----------------------+
| variableB | unitB | Patient13 | TAM | 3.23 |
+-------------+---------+-------------+---------------+---------+-----------------------+
| variableB | unitB | Patient13 | TU | 12.2 |
+-------------+---------+-------------+---------------+---------+-----------------------+
This is the internal storage format.
compartments
------------
Compartments are an abstraction on top of 'cells' and 'bio-liquid'.
Examples are Tumor associated macrophages (TAMs), Tumor cells (TU),
ascites, blood... ``db.get_compartments()`` provides a list
Datasets
--------
Datasets are organized two levels deep. The first one defines the
\*omics being measured (transcriptomics, proteomics, ... or 'clinical'),
while the second levels defines the actual method (RNaseq, FACS,...)
Survival data is in clinical/survival. Please remember: if using
`https://pypi.python.org/pypi/lifelines <lifelines>`__, censored and
event are negations of each other.
Excluded patients:
------------------
Patients are excluded from our studies on two levels.
- On global level (for example because their malignancy was not high
grade serous ovarian carcinoma)
- On a per dataset level.
To query what patients are excluded use
``db.get_excluded_patients(dataset)``. Dataset may be an empty string,
in which case you will receive only the globally excluded patients.
``db.get_exclusion_reasons()`` Lists for each patient (and datasets) why
they were excluded.
================
Introduction
------------
The marburg\_biobank python module offers a high level interface to the
data sets stored in the [Ovarian Cancer Effusion Biobank and
Database])(https://www.ovara.net/biobank).
The basic usage is as follows:
.. code:: python
import marburg_biobank
db = marburg_biobank.OvcaBiobank("marburg_ovca_revision_5.zip") # you need to download that file from your biobank.
print(db.list_datasets())
df_wide = db.get_wide('transcriptomics/rnaseq') # to retrieve the data in a one sample per column / one row per measured variable format
df_tall = db.get_dataset('transcriptomics/rnaseq') # to retrieve the data in one row per data point format
Data formats available
----------------------
wide
~~~~
Using ``db.get_wide(dataset)``:
A pandas DataFrame that looks like this
+------------------------+------------------+-----------------+-------------------------+
| Index | Patient12, TAM | Patient12, TU | PatientX, Compartment |
+========================+==================+=================+=========================+
| **VariableA, unitA** | 23.23 | 112.2 | nan |
+------------------------+------------------+-----------------+-------------------------+
| **VariableB, unitB** | 3.23 | 12.2 | 12.7 |
+------------------------+------------------+-----------------+-------------------------+
Caveats: If a dataset has only one compartment, the compartment
information is ommited by get\_wide(), unless
.get\_wide(standardized=True) is used. The same applies for the unit in
the index. If there is a 'name' column in dataset, it get's added to the
index, regardless of the value of standardized.
tall
~~~~
Using: ``db.get_dataset(dataset)``):
A pandas DataFrame that looks like this
+-------------+---------+-------------+---------------+---------+-----------------------+
| variable | unit | patient | compartment | value | optional columns... |
+=============+=========+=============+===============+=========+=======================+
| variableA | unitA | Patient12 | TAM | 23.23 |
+-------------+---------+-------------+---------------+---------+-----------------------+
| variableA | unitA | Patient12 | TU | 112.2 |
+-------------+---------+-------------+---------------+---------+-----------------------+
| variableB | unitB | Patient13 | TAM | 3.23 |
+-------------+---------+-------------+---------------+---------+-----------------------+
| variableB | unitB | Patient13 | TU | 12.2 |
+-------------+---------+-------------+---------------+---------+-----------------------+
This is the internal storage format.
compartments
------------
Compartments are an abstraction on top of 'cells' and 'bio-liquid'.
Examples are Tumor associated macrophages (TAMs), Tumor cells (TU),
ascites, blood... ``db.get_compartments()`` provides a list
Datasets
--------
Datasets are organized two levels deep. The first one defines the
\*omics being measured (transcriptomics, proteomics, ... or 'clinical'),
while the second levels defines the actual method (RNaseq, FACS,...)
Survival data is in clinical/survival. Please remember: if using
`https://pypi.python.org/pypi/lifelines <lifelines>`__, censored and
event are negations of each other.
Excluded patients:
------------------
Patients are excluded from our studies on two levels.
- On global level (for example because their malignancy was not high
grade serous ovarian carcinoma)
- On a per dataset level.
To query what patients are excluded use
``db.get_excluded_patients(dataset)``. Dataset may be an empty string,
in which case you will receive only the globally excluded patients.
``db.get_exclusion_reasons()`` Lists for each patient (and datasets) why
they were excluded.
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