Gene Expression Analysis Toolkit
A robust software for performing K-Means clustering and data visualization on mouse brain grid annotations.
Background
I created this software during my research project as a Motorola Solutions Foundation Scholar, offering an easy and robust solution to performing machine-learning models on gene expression data. This software has made it incredibly easy to run K-Means clustering on grid expression values across data sourced from the Allen Mouse Brain Atlas.
Goals
1
Facilitate User-Friendly Data Analysis
Ensure that the toolkit is accessible to researchers with varying levels of computational expertise, allowing them to easily analyze and interpret gene expression data.
2
Enhance Data Visualization Capabilities
Provide intuitive and detailed visualizations to help researchers better understand gene expression patterns across the mouse brain.
3
Streamline Custom Dataset Clustering and Management
Simplify the process of clustering and managing user-specific datasets, enabling researchers to efficiently tailor analyses to their unique experimental needs.
Solutions
1
Customizable Dataset Templates
Develop customizable dataset templates that users can configure according to their specific research needs. These templates will allow users to define the structure, metadata, and preprocessing steps for different types of datasets, making it easier to apply consistent clustering workflows across multiple experiments.
2
Automated Data Preprocessing Pipeline
Implement an automated data preprocessing pipeline that handles common tasks like normalization, scaling, and dimensionality reduction. This pipeline will ensure that datasets are consistently prepared for clustering, reducing the likelihood of errors and saving users time in manual data preparation.
3
Dynamic Cluster Configuration
Create a dynamic cluster configuration tool that enables users to fine-tune clustering parameters on the fly. Users can adjust the number of clusters, distance metrics, and initialization methods, with real-time feedback on how changes impact clustering results. This tool will provide greater flexibility and precision in tailoring clustering to specific datasets.
Tools
Python
Programming Language
Conclusion
The Gene Expression Analysis Toolkit is a powerful platform that simplifies gene expression studies in mouse brain grid annotations. With customizable templates, automated preprocessing, dynamic clustering, and version control, it streamlines the research process, making complex analyses more accessible and reproducible, while enabling researchers to gain precise and meaningful insights in neurobiology.