biolearns 0.0.62
BioLearns: Computational Biology and Bioinformatics Toolbox in Python
Requires Python: >=3.6

# biolearns
BioLearns: Computational Biology and Bioinformatics Toolbox in Python http://biolearns.medicine.iu.edu

<div style="text-align:center"><img src="figures/logo.png" width=300/></div>

[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/huangzhii/biolearns/blob/master/LICENSE)

## Installation

* From PyPI

```bash
pip install biolearns -U
```

## Documentation and Tutorials

* We select three examples listed below. For full list of tutorial, check our github wiki page:

    [Wiki](https://github.com/huangzhii/biolearns/wiki)

## Disclaimer
Please note that this is a pre-release version of the BioLearns which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an "as is" and "as available" basis. BioLearns does not give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. BioLearns will not be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user's own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or other problems on the website, please let us know immediately so we can rectify these accordingly. Your help in this regard is greatly appreciated.



### 1. Read TCGA Data

#### Example: Read TCGA Breast invasive carcinoma (BRCA) data

Data is downloaded directly from https://gdac.broadinstitute.org/.
The results here are in whole or part based upon data generated by 
the TCGA Research Network: https://www.cancer.gov/tcga.

```python
from biolearns.dataset import TCGA
```

```python
brca = TCGA('BRCA')
mRNAseq = brca.mRNAseq
clinical = brca.clinical
```

#### TCGA cancer table shortcut:

|              | Barcode            | Cancer full name         | Version            |
|---|---|---|---|
| 1      |  ACC          |  Adrenocortical carcinoma     | 2016_01_28 |
| 2      |  BLCA         |  Bladder urothelial carcinoma         | 2016_01_28 |
| 3      |  BRCA         |  Breast invasive carcinoma    | 2016_01_28 |
| 4      |  CESC         |  Cervical and endocervical cancers    | 2016_01_28 |
| 5      |  CHOL         |  Cholangiocarcinoma   | 2016_01_28 |
| 6      |  COAD         |  Colon adenocarcinoma         | 2016_01_28 |
| 7      |  COADREAD     |  Colorectal adenocarcinoma    | 2016_01_28 |
| 8      |  DLBC         |  Lymphoid Neoplasm Diffuse Large B-cell Lymphoma      | 2016_01_28 |
| 9      |  ESCA         |  Esophageal carcinoma         | 2016_01_28 |
| ...     |  ...         |  ...          | ... |


### 2. Gene Co-expression Analysis

We firstly download and access the mRNAseq data.
```python
from biolearns.dataset import TCGA

brca = TCGA('BRCA')
mRNAseq = brca.mRNAseq
```

mRNAseq data is noisy. We filter out 50% of genes with lowest mean values, and then filter out 50% remained genes with lowest variance values.

```python
from biolearns.preprocessing import expression_filter
mRNAseq = expression_filter(mRNAseq, meanq = 0.5, varq = 0.5)
```

We then use lmQCM class to create an lmQCM object ```lobj```.

The gene co-expression analysis is performed by simply call the ```fit()``` function.

```python
from biolearns.coexpression import lmQCM

lobj = lmQCM(mRNAseq)
clusters, genes, eigengene_mat = lobj.fit()
```

### 3. Univariate survival analysis

We firstly download and access the mRNAseq data. Use breast cancer as an example.
```python
from biolearns.dataset import TCGA

brca = TCGA('BRCA')
mRNAseq = brca.mRNAseq
```

We import logranktest from survival subpackage. Choose gene "ABLIM3" as the univariate input.
```python
from biolearns.survival import logranktest

r = mRNAseq.loc['ABLIM3',].values
```

We find the intersection of univariate, time, and event data
```python
bcd_m = [b[:12] for b in mRNAseq.columns]
bcd_p = [b[:12] for b in clinical.index]
bcd = np.intersect1d(bcd_m, bcd_p)

r = r[np.nonzero(np.in1d(bcd, bcd_m))[0]]
t = brca.overall_survival_time[np.nonzero(np.in1d(bcd, bcd_p))[0]]
e = brca.overall_survival_event[np.nonzero(np.in1d(bcd, bcd_p))[0]]
```

We perform log-rank test:

```python
logrank_results, fig = logranktest(r[~np.isnan(t)], t[~np.isnan(t)], e[~np.isnan(t)])
test_statistic, p_value = logrank_results.test_statistic, logrank_results.p_value
```

The output figure looks like:

<div style="text-align:center"><img src="https://github.com/huangzhii/biolearns/blob/master/figures/survival_plot_BRCA_ABLIM3.png" width=600/></div>