Metadata-Version: 2.1
Name: delanalysis
Version: 0.0.1
Summary: An analysis algoritm that is a companion to DEL-Decode
Home-page: https://github.com/Roco-scientist/DEL-Analysis
Author: Rory Coffey
Author-email: coffeyrt@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# DEL-Analysis
DNA encoded library analysis.  This is companion software to DEL-Decode for outputing analysis and graphs.

## Table of Contents
<ul>
<li><a href=#installation>Installation</a></li>
<li><a href=#files-needed>Files Needed</a></li>
<li><a href=#methods>Methods</a></li>
<li><a href=#run>Run</a></li>
</ul>

## Installation

Anaconda python required for the instructions below

### Download and move into directory

```
git clone https://github.com/Roco-scientist/DEL-Analysis.git
cd DEL-Analysis
```

### Create a del environment

```
conda create -n del python=3
conda activate del
pip install -r requirements.txt
```

### Build DEL-Analysis

```
python3 -m pip install --upgrade build
python3 -m build
```

### Install DEL-Analysis

```
pip install ./dist/delanalysis-0.0.1-py3-none-any.whl
```

## Files Needed
Output files from DEL-Decode

## Methods
Work in progress

### Merged data

<table>
<tr>
<th>Method</th>
<th>Description</th>
</tr>
<tr>
<td></td>
<td></td>
</tr>
</table>

### Sample data

<table>
<tr>
<th>Method</th>
<th>Description</th>
</tr>
<tr>
<td></td>
<td></td>
</tr>
</table>

## Run

### Start

```
conda activate del
python
```

### Working with merged data output

All code below is within python<br><br>

```
import delanalysis

# Import merged data output from DEL-Decode.  This creates a DelDataMerged object
merged_data = delanalysis.read_merged("test_counts.all.csv")

# zscore, then quantile_normalize, then subtract background which is 'test_1'
merged_data_transformed = merged_data.zscore().quantile_normalize().subtract_background("test_1")

# Create a 2d comparison graph between 'test_2' and 'test_3' in the current directory and with a low end cutoff of 4
delanalysis.comparison_graph(merged_data_transformed, "test_2", "test_3", "./", 4)

# Creates a DelDataSample object from a single sample from the merged object
test_2_data_transformed = merged_data_transformed.sample_data("test_2")

# Create a 3d graph with each axis being a barcode within the current directory and a low end cutoff of 4
delanalysis.graph_3d(test_2_data_transformed, "./", 4)

# Create a 2d graph within the current directory and a low end cutoff of 4
delanalysis.graph_2d(test_2_data_transformed, "./", 4)
```

### Working with sample data output

All code below is within python<br><br>

```
import delanalysis

# Import sample data output from DEL-Decode.  This creates a DelDataSample object
sample_data = delanalysis.read_sample("test_1.csv")

# zscore
sample_data_zscore = sample_data.zscore()

# Create a 3d graph with each axis being a barcode within the current directory and a low end cutoff of 4
delanalysis.graph_3d(sample_data_zscore, "./", 4)

# Create a 2d graph within the current directory and a low end cutoff of 4
delanalysis.graph_2d(sample_data_zscore, "./", 4)
```



