marburg-biobank 0.116

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:
```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:
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.