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Feature/add tyrosine support
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alexandra-valeanu authored Oct 22, 2024
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65 changes: 36 additions & 29 deletions README.md
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@@ -1,15 +1,8 @@
# Kinex
# Kinex - Kinome Exploration Tool

Alexandra Valeanu and Jitao David Zhang with the input and help of many colleagues
**Kinex** is a Python package for infering causal kinases from phosphoproteomics data.

**kinex** is a workflow implemented in a Python package with the same name. Kinex infers causal kinases from phosphoproteomics data.

## Table of Contents

- [Main Features](#main-features)
- [Requirements](#requirements)
- [Installation](#installation)
- [Documentation](#documentation)
Paper: Kinex infers causal kinases from phosphoproteomics data. https://doi.org/10.1101/2023.11.23.568445

## Main Features

Expand All @@ -24,47 +17,61 @@ Alexandra Valeanu and Jitao David Zhang with the input and help of many colleagu

## Installation

Here are few ways to install **kinex**

### From Conda

1. Create and activate your conda environment

```
# Create and activate your conda environment
conda create --name kinex
conda activate kinex
```

2. Install kinex package
```
# Install kinex package
conda install -c bioconda kinex
```


### From source

1. Create and activate a python 3.11 conda environment

```
# Create and activate a python 3.11 conda environment
conda create --name kinex
conda activate kinex
conda install python=3.11
```

2. Download the package:
```
# Download the package:
git clone git@github.com:bedapub/kinex.git
cd kinex
# Install the package
pip install .
```

3. Install the package
## Quick start

1. Import package and create Kinex object
```
pip install .
from kinex import Kinex
import pandas as pd
# Read scoring matrices from zenodo
scoring_matrix_ser_thr = pd.read_csv("https://zenodo.org/records/13964893/files/scoring_matrix_ser_thr_82k_sorted.csv.gz?download=1", compression="gzip")
scoring_matrix_tyr = pd.read_csv("https://zenodo.org/records/13964893/files/scoring_matrix_tyr_7k_sorted.csv.gz?download=1", compression="gzip")
# Create Kinex object
kinex = Kinex(scoring_matrix_ser_thr, scoring_matrix_tyr)
```
2. Score a sequence
```
sequence = "FVKQKAY*QSPQKQ"
res = kinex.get_score(sequence)
```

3. Enrichment analysis
```
enrich = kinex.get_enrichment(input_sites, fc_threshold=1.5, phospho_priming=False, favorability=True, method="max")
enrich.ser_thr.plot()
enrich.tyr.plot()
```

## [Documentation](https://kinex.readthedocs.io/en/latest/)
## Documentation

You can find detailed [documentation](https://kinex.readthedocs.io/en/latest/) describing every feature of the package with examples and tutorials [here](https://kinex.readthedocs.io/en/latest/).
You can find detailed documentation describing every feature of the package with examples and tutorials [here](https://kinex.readthedocs.io/en/latest/).
34 changes: 14 additions & 20 deletions docs/chapters/features/comparison.rst
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Expand Up @@ -5,13 +5,13 @@ Drug comparison

.. code:: python
>>> from kinex import Comparison
from kinex import Comparison
2. Initialize a Comparison object

.. code:: python
>>> comp = Comparison()
comp = Comparison()
Compare multiple experiments with each other
Expand All @@ -21,7 +21,7 @@ Compare multiple experiments with each other

.. code:: python
>>> data_path = "path/to/your/tables"
data_path = "path/to/your/tables"
.. note::
Expand All @@ -40,7 +40,7 @@ Compare multiple experiments with each other

.. code:: python
>>> fig = comp.get_comparison(data_path=data_path, method='mds')
fig = comp.get_comparison(data_path=data_path, method='mds')
.. note::

Expand All @@ -50,7 +50,7 @@ Compare multiple experiments with each other

.. code:: python
>>> fig.show()
fig.show()
.. raw:: html
:file: ../../figures/comparison_multiple_drugs.html
Expand All @@ -72,7 +72,7 @@ Compare an experiment to the existing collection of drug profiles

.. code:: python
>>> input_data = pd.read_csv('tables/table1.csv', index_col=0)
input_data = pd.read_csv('tables/table1.csv', index_col=0)
.. note::

Expand All @@ -83,13 +83,7 @@ Compare an experiment to the existing collection of drug profiles
dominant_enrichment_value_log2 dominant_p_value_log10_abs
0.868162 0.821932
-0.785398 0.707911
-0.934463 0.901927
-1.369094 0.000000
-1.474303 0.000000
... ...
-2.914661 2.022525
-2.490535 1.691968
-2.920072 0.000000
... ...
-1.551978 0.795959
-2.986266 1.521982
Expand All @@ -99,7 +93,7 @@ Compare an experiment to the existing collection of drug profiles

.. code:: python
>>> fig = comp.get_comparison(input_data=input_data, method='tsne')
fig = comp.get_comparison(input_data=input_data, method='tsne')
.. note::

Expand All @@ -118,7 +112,7 @@ Compare an experiment to the existing collection of drug profiles

.. code:: python
>>> fig.show()
fig.show()
.. raw:: html
:file: ../../figures/comparison_input.html
Expand All @@ -131,28 +125,28 @@ Save the plot in a desired format

.. code:: python
>>> fig.write_html("path/to/file.html")
fig.write_html("path/to/file.html")
- ``.svg``

.. code:: python
>>> fig.write_image("images/fig1.svg")
fig.write_image("images/fig1.svg")
- ``.pdf``

.. code:: python
>>> fig.write_image("images/fig1.pdf")
fig.write_image("images/fig1.pdf")
- ``.png``

.. code:: python
>>> fig.write_image("images/fig1.png")
fig.write_image("images/fig1.png")
- ``.jpeg``

.. code:: python
>>> fig.write_image("images/fig1.jpeg")
fig.write_image("images/fig1.jpeg")
77 changes: 28 additions & 49 deletions docs/chapters/features/enrichment.rst
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Expand Up @@ -9,8 +9,11 @@ Kinases inference analysis

.. code:: python
>>> input_sites = pd.read_csv('path/to/your/input_sites.csv')
>>> input_sites
input_sites = pd.read_csv('path/to/your/input_sites.csv')
input_sites
.. code-block:: text
Sequence Fold Change: a/a' KO Clone A vs WT
0 KLEEKQKs*DAEEDGV -88.159789
1 EEDGVTGs*QDEEDSK -88.159789
Expand All @@ -34,55 +37,35 @@ Kinases inference analysis

.. code:: python
>>> enrich = kinex.get_enrichment(input_sites, fc_threshold=1.5, phospho_priming=False, favorability=True, method="max")
>>> enrich
Total number of upregulated phospho-sequences is: 63
Total number of downregulated phospho-sequences is: 86
Total number of unregulated phospho-sequences is: 309
enrichment.Enrichment
enrich = kinex.get_enrichment(input_sites, fc_threshold=1.5, phospho_priming=False, favorability=True, method="max")
3. Access the total number of up-regulated, down-regulated, and un-regulated phospho-sequences

.. code:: python
>>> enrich.total_upregulated
63
int
>>> enrich.total_downregulated
86
int
>>> enrich.total_unregulated
309
int
print("Total upregulated Ser/Thr kinases:", enrich.ser_thr.total_upregulated)
print("Total downregulated Ser/Thr kinases:", enrich.ser_thr.total_downregulated)
print("Total unregulated Ser/Thr kinases:", enrich.ser_thr.total_unregulated)
4. Check the sites that were marked as failed
.. code-block:: text
.. code:: python
Total upregulated Ser/Thr kinases: 63
Total downregulated Ser/Thr kinases: 86
Total unregulated Ser/Thr kinases: 309
>>> enrich.failed_sites
['EKIGEGTyGVVYKGR', 'KPSIVTKyVESDDEK', 'LGQRIYQyIQSRFYR', 'INPGYDDyADSDEDQ', 'ADNDITPyLVSRFYR', 'RGEPNVSyICSRYYR']
list
5. Check the regulation of each phospho-sequence, and get the top 15 kinases most likely to target each phospho-sequence
4. Check the sites that were marked as failed

.. code:: python
>>> enrich.input_sites
Sequence Fold Change: a/a' KO Clone A vs WT regulation top15_kinases
0 KLEEKQKs*DAEEDGV -88.159789 downregulated GRK7,IKKA,CAMK2B,CK2A1,CK2A2,GRK6,LATS2,GRK1,C...
1 EEDGVTGs*QDEEDSK -88.159789 downregulated DNAPK,CAMK2G,ATM,ATR,GRK5,GRK1,SMG1,CAMK2B,GRK...
.. ... ... ...
462 AKEESEEs*DEDMGFG 19.421218 upregulated BMPR1A,TGFBR1,BMPR1B,ALK2,CK1G2,CK2A2,ACVR2A,G...
463 RNGPRDAs*PPGSEPE 63.187703 upregulated SRPK2,SRPK1,SRPK3,HIPK4,CLK2,CLK3,HIPK2,KIS,GR...
enrich.failed_sites
[464 rows x 4 columns]
pandas.DataFrame
6. Show enrichment table
5. Show enrichment table

.. code:: python
>>> enrich.enrichment_table
enrich.ser_thr.enrichment_table
.. code-block:: text
upregulated downregulated ... dominant_enrichment_value_log2 dominant_p_value_log10_abs
kinase
Expand All @@ -95,16 +78,16 @@ Kinases inference analysis
[303 rows x 19 columns]
pandas.DataFrame
7. Vulcano plot of Enrichment Odds Ratio (EOR) and p-value
6. Vulcano plot of Enrichment Odds Ratio (EOR) and p-value

.. note::

Kinases are represented with colours corresponding to their class.

.. code:: python
>>> fig = enrich.plot(use_adjusted_pval=False)
>>> fig.show()
fig = enrich.ser_thr.plot(use_adjusted_pval=False)
fig.show()
.. raw:: html
Expand All @@ -117,38 +100,34 @@ Kinases inference analysis
`https://plotly.com/python/creating-and-updating-figures <https://plotly.com/python/creating-and-updating-figures>`_





8. Save the figure in a desired format

7. Save the figure in a desired format

- ``.html``

.. code:: python
>>> fig.write_html("path/to/file.html")
fig.write_html("path/to/file.html")
- ``.svg``

.. code:: python
>>> fig.write_image("images/fig1.svg")
fig.write_image("images/fig1.svg")
- ``.pdf``

.. code:: python
>>> fig.write_image("images/fig1.pdf")
fig.write_image("images/fig1.pdf")
- ``.png``

.. code:: python
>>> fig.write_image("images/fig1.png", scale=10)
fig.write_image("images/fig1.png", scale=10)
- ``.jpeg``

.. code:: python
>>> fig.write_image("images/fig1.jpeg", scale=10)
fig.write_image("images/fig1.jpeg", scale=10)
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