Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/thecoderpinar/gen-expression
Gene expression analysis is a fundamental component of genomics research, providing valuable insights into how genes are regulated and their impact on various biological processes. This project delves into the realm of gene expression data, aiming to uncover hidden patterns and relationships within complex datasets. 🚀
https://github.com/thecoderpinar/gen-expression
bioinformatics biotechnology data-analysis data-science data-visualization genomics kaggle machine-learning pca python
Last synced: about 1 month ago
JSON representation
Gene expression analysis is a fundamental component of genomics research, providing valuable insights into how genes are regulated and their impact on various biological processes. This project delves into the realm of gene expression data, aiming to uncover hidden patterns and relationships within complex datasets. 🚀
- Host: GitHub
- URL: https://github.com/thecoderpinar/gen-expression
- Owner: ThecoderPinar
- License: mit
- Created: 2023-09-15T15:16:20.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-15T15:38:16.000Z (over 1 year ago)
- Last Synced: 2024-01-28T20:34:44.985Z (12 months ago)
- Topics: bioinformatics, biotechnology, data-analysis, data-science, data-visualization, genomics, kaggle, machine-learning, pca, python
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/datasets/crawford/gene-expression
- Size: 2.15 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Gene Expression Analysis Project
https://github.com/ThecoderPinar/gen-expression/assets/107423523/55923acc-d613-457a-83c3-21cf8c31c40d
Gene expression analysis is a crucial part of genomics research, offering valuable insights into the regulation of genes and their influence on various biological processes. This project focuses on exploring gene expression data to discover hidden patterns and relationships within complex datasets.
## Table of Contents
- [Project Description](#project-description)
- [Objectives](#objectives)
- [Dataset](#dataset)
- [Methodology](#methodology)
- [Results](#results)
- [Usage](#usage)
- [Contribution](#contribution)
- [License](#license)
- [Tags](#tags)
## Project Description
Gene expression analysis plays a pivotal role in understanding the molecular mechanisms behind various biological processes and diseases. This project dives into gene expression data analysis, aiming to extract meaningful insights from large and complex datasets.
## Objectives
- **Dimensionality Reduction**: We employ Principal Component Analysis (PCA) to reduce the high-dimensional gene expression data, making it more manageable and interpretable.
- **Biological Insights**: By visualizing the PCA results and conducting statistical tests, we aim to identify gene clusters and associations indicative of specific biological pathways or disease mechanisms.
- **Data Visualization**: Utilizing Python libraries such as Matplotlib and Seaborn, we create informative visualizations to present our findings effectively.
## Dataset
The dataset used in this project comprises gene expression profiles across multiple samples and genes. Each data point includes a gene's description, accession number, and corresponding expression values. You can access the dataset [here](https://www.kaggle.com/datasets/crawford/gene-expression).
## Methodology
Our analysis pipeline involves the following steps:
1. **Data Preprocessing**: We clean, normalize, and prepare the gene expression data for PCA.
2. **Principal Component Analysis (PCA)**: We apply PCA to reduce dimensionality and extract key components.
3. **Data Visualization**: We visualize the PCA results, including scatter plots, heatmaps, and variance explained plots.
4. **Statistical Analysis**: We perform statistical tests to identify significant gene clusters and associations.
5. **Biological Interpretation**: We interpret the biological significance of the identified gene clusters and correlations.
## Results
Our analysis provides valuable insights into the intricate relationships within the gene expression data:
- Identification of gene sets associated with specific biological pathways.
- Insights into potential biomarkers for disease diagnosis.
- Visualizations that simplify complex data for easy comprehension.
## Usage
This repository contains a Jupyter Notebook (`gen-expression.ipynb`) that provides a step-by-step guide to replicate our analysis. Users can adapt the code for their specific gene expression datasets or research questions.
## Contribution
We welcome contributions and feedback from the community. If you have suggestions, find issues, or want to collaborate, please feel free to create issues or submit pull requests.
## License
This project is open-source and is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Tags
#DataScience #Genomics #PCA #DataAnalysis #Bioinformatics #MachineLearning #Python #DataVisualization #Kaggle #Biotechnology
