Metadata-Version: 2.4
Name: poly-cpg
Version: 1.0.5
Summary: A self-tuning sliding-window framework for phenotype-linked regional poly-methylation architecture in sparse wildlife methylomes.
Author-email: Thomas Stocker <Thomas.stocker@sydney.edu.au>
Project-URL: Homepage, https://github.com/tsto3616/poly-CpG
Project-URL: Repository, https://github.com/tsto3616/poly-CpG
Project-URL: Issues, https://github.com/tsto3616/poly-CpG/issues
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy>=1.26
Requires-Dist: pandas>=2.0
Requires-Dist: scipy>=1.10
Dynamic: license-file

# poly-CpG: Sliding window analysis of methylomes
This is a lightweight package to accompany the sliding window method for the analysis of methylomes. 

## Additional scripts and data can be found in the "Supplementary" branch. Herein the data and code used within the study are provided.

The package call be installed as:

```
pip install poly_cpg
```

The program can quickly be performed using the commands: 

```
import poly_cpg as pc

# Load synthetic example data
M, pos, gamma, beta, y, cpg_names = pc.load_example()

# Run sliding-window algorithm
df_windows = pc.self_tuning_windows_dual(M, gamma, beta, pos, y)

# Classify windows
df_windows = pc.classify_windows(df_windows)

print(df_windows.head())

```

This produces a table of windows with the genomic coordinates, number of CpGs, R^2 values, directional agreement and trust classificaiton 

The script folder contains the necessary script for the synthetic data generation. 
