Interface code to interact with data from the Ovara.net biobank.
Project description
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:
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 formatData 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, 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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