scRNA App

Built a cutting-edge single-cell RNA sequencing analysis platform that enables researchers to explore cellular heterogeneity with unprecedented detail and speed.

Single-cell, Machine Learning, Data Science, Vue.js

The Challenge

Single-cell RNA sequencing analysis requires sophisticated computational methods to handle the complexity and scale of the data. The Genomics Innovation Lab needed a platform that could perform clustering, trajectory analysis, and cell type identification while being accessible to researchers without extensive computational backgrounds.

Our Solution

We created an advanced scRNA analysis platform featuring automated cell clustering, trajectory inference, differential expression analysis, and cell type annotation. The platform incorporates machine learning algorithms for dimensionality reduction and clustering, with an intuitive drag-and-drop interface for creating analysis pipelines. Built with Vue.js frontend and Python backend utilizing Scanpy, Seurat, and custom ML models.

Technologies Used

Vue.js

Python

Scanpy

Seurat

scikit-learn

TensorFlow

MongoDB

Docker

Results & Impact

The lab achieved a 400% improvement in processing speed and identified new cell subpopulations that were previously overlooked. The standardized pipeline led to reproducible results across different research projects.

Client Testimonial

“The scRNA-seq suite has transformed our understanding of cellular heterogeneity in tumors. The automated machine learning pipelines have revealed insights that would have been impossible with manual methods.”

GIL

Genomics Innovation Lab

Research Team