marburg-biobank 0.124

Interface code to interact with data from the Ovara.net biobank.

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:

import marburg_biobank
db = marburg_biobank.OvcaBiobank("marburg_ovca_revision_15.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 three levels deep. The first one defines the whether you're looking t ex-vivo (=primary) data or in-vitro experiments (=secondary) or literature data (=tertiary). The second level defines *omics being measured (transcriptomics, proteomics, ... or 'clinical'), while the third levels defines the actual method (RNaseq, FACS,...)

Survival data is in primary/clinical/survival.

Please remember: if using https://pypi.python.org/pypi/lifelines, censored and event are negations of each other.

Excluded patients:

Exclusion can either be on a patient, or a patient+compartment level. In addition, there is per dataset exclusion and global exclusion.

Exclusion is by default applied to db.get_wide(), but not to db.get_dataset(), you can change the default by passing apply_exclusion=True|False.

Exclusion information can be retrieved by db.get_excluded_patients(dataset), which return a set of patients (or patient+compartment tuples), or db.get_exclusion_reasons(), which lists why the exclusion happend.