https://github.com/danymukesha/pca-pwa

simplified manner for insights and decision-making by visualizing complex relationships with PCA web application

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

○
CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
○
.zenodo.json file
○
DOI references
○
Academic publication links
○
Academic email domains
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (13.6%) to scientific vocabulary

Keywords

pca statistics unsupervised-learning

Last synced: 5 months ago · JSON representation

Repository

simplified manner for insights and decision-making by visualizing complex relationships with PCA web application

Basic Info

Host: GitHub
Owner: danymukesha
License: mit
Language: Python
Default Branch: main
Homepage: https://pypi.org/project/pca-pwa/
Size: 201 KB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 1

Topics

pca statistics unsupervised-learning

Created about 2 years ago · Last pushed about 2 years ago

Metadata Files

Readme License

README.md

pca-pwa

pca-pwa, a simplified manner for insights and decision-making by visualizing complex relationships with PCA web application.

The Purpose of the Package

The purpose of the package is to offer a simple way of visualizing relatationships between items of any given dataset. The user could easily obtain a pca plot without needing to configure or compile the application.

Installation

To install pca_pwa, you can use pip. Open your terminal and run:

sh pip install pca_pwa Open IPython or Jupyter Notebook ```python

from pcapwa import app app.app.run(debug=True, usereloader=True, host='0.0.0.0', port=8082)

* Serving Flask app 'app'

* Debug mode: on

* Running on http://127.0.0.1:8082

```

Open the url: http://127.0.0.1:8082

Upload xslx/slx file (Excel)

e.g.:
- Click here to download the excel file
- Items/Observations should be in rows
- Variables/Features should in columns
  - Standard Data (table) Format
  The example of standard data format to be used while uploading to pca-pwa web app is a dataframe from sample names in the first column, and the rest (e.g.: metabolites, genes, RNA, etc.) for each sample in the following columns (see Table 1).
  
  Table 1: Standard data table format.
  
  | Sample | Met 1 | Met 2 | Met 3 | ... | Met N | |--------|---------|---------|---------|---------|---------| | S1 | 99,380 | 10.177 | 51.484 | ... | 71.882 | | S2 | 101.195 | 10.786 | 50.446 | ... | 73.318 | | S3 | 102.165 | 9,375 | 49.668 | ... | 72,056 | | S4 | 99.481 | 8.291 | 48.111 | ... | 73.282 | | S5 | 101.282 | 10.867 | 50.209 | ... | 73,572 | | S6 | 99.43 | 9.95 | 47.602 | ... | 71,983 |

Choose a method of imputation for missing values.

Then run the pca by clicking Perform PCA button.

Otherwise you can use git clone:

Here is the Usage:

Clone the github repository

git git clone https://github.com/danymukesha/pca-pwa.git

Run the app

```sh cd pca-pwa python3.1 pca-pwa/app.y

* Serving Flask app 'app'

* Debug mode: on

* Running on http://127.0.0.1:8082

```

Open the url: http://127.0.0.1:8082

License

This project is licensed under the MIT License.

Credits

Author: MIT © Dany Mukesha

Email: danymukesha@gmail.com

Thank you for using pca_pwa!

Owner

Name: Dany Mukesha
Login: danymukesha
Kind: user
Location: Rome, Italy

Website: danymukesha.github.io
Repositories: 1
Profile: https://github.com/danymukesha

GitHub Events

Total

Last Year

Issues and Pull Requests

Last synced: 8 months ago

All Time

Total issues: 0
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 0
Total pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Dependencies

poetry.lock pypi

adjusttext 1.0
bioframe 0.6.0
blinker 1.7.0
certifi 2023.11.17
charset-normalizer 3.3.2
click 8.1.7
colorama 0.4.6
contourpy 1.2.0
cycler 0.12.1
et-xmlfile 1.1.0
flask 3.0.0
fonttools 4.47.0
idna 3.6
itsdangerous 2.1.2
jinja2 3.1.2
joblib 1.3.2
kiwisolver 1.4.5
markupsafe 2.1.3
matplotlib 3.8.2
numpy 1.26.3
openpyxl 3.1.2
packaging 23.2
pandas 2.1.4
pillow 10.2.0
pyparsing 3.1.1
python-dateutil 2.8.2
pytz 2023.3.post1
pyyaml 6.0.1
requests 2.31.0
scikit-learn 1.3.2
scipy 1.11.4
seaborn 0.13.1
six 1.16.0
threadpoolctl 3.2.0
tzdata 2023.4
urllib3 2.1.0
werkzeug 3.0.1

pyproject.toml pypi

adjusttext ^1.0
flask ^3.0.0
matplotlib ^3.8.2
openpyxl ^3.1.2
pandas ^2.1.4
python ^3.11
scikit-learn ^1.3.2
seaborn ^0.13.1

.github/workflows/build.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite
actions/upload-release-asset v1 composite

.github/workflows/main.yml actions

actions/setup-python v2 composite

.github/workflows/publish.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/python-publish.yml actions

code-specialist/pypi-poetry-publish v1 composite

.github/workflows/release-package.yml actions

actions/checkout v4 composite
actions/setup-node v3 composite

.github/workflows/release.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

package-lock.json npm

package.json npm

requirements.txt pypi

Flask ==2.0.1
adjustText ==0.7.3
matplotlib ==3.4.2
numpy ==1.20.3
pandas ==1.2.4
scikit-learn ==0.24.2
scipy ==1.6.3
seaborn ==0.11.1

setup.py pypi

Flask *
adjustText *
matplotlib *
numpy *
pandas *
scikit-learn *
scipy *
seaborn *