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delanalysis-0.2.3
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مشاهده بیشترتوضیحات
An analysis algoritm that is a companion to NGS-Barcode-Count
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | delanalysis-0.2.3 |
| نام | delanalysis |
| نسخه کتابخانه | 0.2.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Rory Coffey |
| ایمیل نویسنده | coffeyrt@gmail.com |
| آدرس صفحه اصلی | https://github.com/Roco-scientist/DEL-Analysis |
| آدرس اینترنتی | https://pypi.org/project/delanalysis/ |
| مجوز | - |
# DEL-Analysis
DNA encoded library analysis. This is companion software to <a href=https://github.com/Roco-scientist/NGS-Barcode-Count>NGS-Barcode-Count</a> 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=#run>Run</a></li>
<li><a href=#methods>Methods</a></li>
</ul>
## Installation
Anaconda python required for the instructions below
### Create a del environment and activate
```
conda create -n del python=3.9
conda activate del
```
### Install
From pypl:<br>
```
pip install delanalysis
```
From source:<br>
```
git clone https://github.com/Roco-scientist/DEL-Analysis.git
cd DEL-Analysis
pip install --use-feature=in-tree-build .
```
## Files Needed
Output files from NGS-Barcode-Count
## 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 NGS-Barcode-Count. 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.binomial_zscore().subtract_background(background_name="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
merged_data_transformed.comparison_graph(x_sample="test_2", y_sample="test_3", out_dir="./", min_score=4)
# Creates a DelDataSample object from a single sample from the merged object
test_2_data_transformed = merged_data_transformed.sample_data(sample_name="test_2")
# Create a 3d graph with each axis being a barcode within the current directory and a low end cutoff of 4
test_2_data_transformed.graph_3d(out_dir="./", min_score=4)
# Create a 2d graph within the current directory and a low end cutoff of 4
test_2_data_transformed.graph_2d(out_dir="./", min_score=4)
# Can all be done in one line
delanalysis.read_merged("test_counts.all.csv").binomial_zscore().subtract_background("test_1").sample_data("test_2").graph_3d("./", 4)
# Create a comparison graph for tri, di, and mono synthons
full = read_merged("../../test_del/test.all.csv")
double = read_merged("../../test_del/test.all.Double.csv")
single = read_merged("../../test_del/test.all.Single.csv")
full_double = full.concat(double)
full_double_single = full_double.concat(single)
full_double_single_zscore = full_double_single.binomial_zscore_sample_normalized()
full_double_single_zscore.subtract_background("test_1", inplace=True)
full_double_single_zscore.comparison_graph("test_2", "test_3", "../../test_del/", 0.002)
```
### Working with sample data output
All code below is within python<br><br>
```
import delanalysis
# Import sample data output from NGS-Barcode-Count. This creates a DelDataSample object
sample_data = delanalysis.read_sample("test_1.csv")
# zscore
sample_data_zscore = sample_data.binomial_zscore()
# Create a 3d graph with each axis being a barcode within the current directory and a low end cutoff of 4
sample_data_zscore.graph_3d(out_dir="./", min_score=4)
# Create a 2d graph within the current directory and a low end cutoff of 4
sample_data_zscore.graph_2d(out_dir="./", min_score=4)
```
### Resulting graphs
The actual graphs will be interactive HTML graphs with hover data etc. <br><br>
From comparison_graph()<br>
<br>
From graph_2d()<br>
<br>
From graph_3d()<br>
<br>
## Methods
### delanalysis methods to import data
<table>
<tr>
<th>Method</th> <th>Description</th>
</tr>
<tr>
<td>read_merged(file_path)</td> <td>Creates a DelDataMerged object which can use the methods below</td>
</tr>
<tr>
<td>read_sample(file_path)</td> <td>Creates a DelDataSample object which can use the methods below</td>
</tr>
<tr>
<td></td> <td></td>
</tr>
</table>
### Common to merged data and sample data
Used with either delanalysis.read_merged() or delanalysis.read_sample() objects
<table>
<tr>
<th>Method</th> <th>Description</th>
</tr>
<tr>
<td>building_block_columns()</td> <td>returns all column names which contain building block info</td>
</tr>
<tr>
<td>data_columns()</td> <td>returns all column names which contain data</td>
</tr>
<tr>
<td>data_descriptor()</td> <td>Returns data_type with underscores for file output</td>
</tr>
<tr>
<td>data_type</td> <td>The data type of the DelData</td>
</tr>
<tr>
<td>to_csv(out_file)</td> <td>Writes the DelData object to the out_file in csv format</td>
</tr>
<tr>
<td>zscore(inplace=False)</td> <td>z-scores the data</td>
</tr>
<tr>
<td>binomial_zscore(del_library_size, inplace=False)</td> <td>z-scores the data using the binomial distribution standard deviation</td>
</tr>
<tr>
<td>binomial_zscore_sample_normalized(del_library_size, inplace=False)</td> <td>z-scores the data using the binomial distribution standard deviation and normalizes by sqrt(n). See: <a href=https://pubs.acs.org/doi/10.1021/acscombsci.8b00116>Quantitative Comparison of Enrichment...</a></td>
</tr>
<tr>
<td>enrichment(del_library_size, inplace=False)</td> <td>count * library_size/ total_counts</td>
</tr>
<tr>
<td>update_synthon_numbers(unique_synthons_per_barcode: List[int])</td> <td>The number of unique synthons is inferred by the total uniques found in the data. These numbers can be updated with this function</td>
</tr>
</table>
### Merged data
Used with delanalysis.read_merged() which creates a DelDataMerged object
<table>
<tr>
<th>Method</th> <th>Description</th>
</tr>
<tr>
<td>quantile_normalize(inplace=False)</td> <td>quantile normalizes the data</td>
</tr>
<tr>
<td>sample_enrichment(inplace=False)</td> <td>(sample_count/total_sample_count)/(non_sample_count/total_non_sample_count). Still experimental as if the count only happens in one sample, a div 0 error occurs</td>
</tr>
<tr>
<td>subtract_background(background_name, inplace=False)</td> <td>subtracts the background_name sample from all other samples</td>
</tr>
<tr>
<td>reduce(min_score, inplace=False)</td> <td>Removes all rows from the data where no samples have a score above the min_score</td>
</tr>
<tr>
<td>merge(deldata, inplace=False)</td> <td>Merges DelDataMerged data into the current DelDataMerged object</td>
</tr>
<tr>
<td>sample_data(sample_name)</td> <td>Returns a DelDataSample object from the DelDataMerged object. This is needed for the 2d and 3d graph</td>
</tr>
<tr>
<td>select_samples(sample_names: List, inplace=False)</td> <td>Reduces the data to the listed sample names</td>
</tr>
<tr>
<td>comparison_graph(x_sample, y_sample, out_dir, min_score=0)</td> <td>Outputs a comparison graph of x_sample vs y_sample names.</td>
</tr>
</table>
### Sample data
Used with delanalysis.read_sample() which creates a DelDataSample object
<table>
<tr>
<th>Method</th> <th>Description</th>
</tr>
<tr>
<td>reduce(min_score, inplace=False)</td> <td>reduces the data to only data greater than the min_score</td>
</tr>
<tr>
<td>max_score()</td> <td>Returns the maximum score within the data</td>
</tr>
<tr>
<td>data_column()</td> <td>Returns the data column name</td>
</tr>
<tr>
<td>graph_2d(out_dir, min_score=0)</td> <td>Produces two subplot 2d graphs for the different barcodes of a DelDataSample.</td>
</tr>
<tr>
<td>graph_3d(out_dir, min_score=0)</td> <td>Produces 3d graphs for the different barcodes of a DelDataSample.</td>
</tr>
</table>
نیازمندی
| مقدار | نام |
|---|---|
| ==1.2 | pandas |
| ==5.3 | plotly |
| ==1.6 | scipy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| ~=3.9 | Python |
نحوه نصب
نصب پکیج whl delanalysis-0.2.3:
pip install delanalysis-0.2.3.whl
نصب پکیج tar.gz delanalysis-0.2.3:
pip install delanalysis-0.2.3.tar.gz