Project Title: Investigating gene expression patterns across disease conditions using single-cell RNA-seq data
Aim of the project: The main objective of this study is to address the following questions:
1. How are gene expression patterns altered in disease conditions (e.g., cancer, neurodegenerative diseases) compared to healthy states?
2. How does the interplay between gene expression contribute to disease progression?

This study aims to uncover unique gene expression signatures associated with specific cell types and conditions, aiding in the identification of potential diagnostic markers and therapeutic targets. By leveraging single-cell RNA-seq (scRNA-seq) data, we can explore these patterns at high resolution and gain insights into cellular heterogeneity in health and disease.
Stakeholders:
1. Researchers and clinicians: Understanding cellular-level changes in gene expression can aid in the diagnosis, prognosis, and development of targeted treatments.
2. Biotechnology and pharmaceutical companies: Identifying new drug targets based on disease-specific gene expression signatures.
3. Policy makers and health organizations: Utilizing research findings to inform public health strategies and personalized medicine approaches.

Key Performance Indicators (KPIs):
1. Identification of cell type-specific markers: Discover genes uniquely or differentially expressed in various cell types.
2. Differential gene expression analysis: Quantify changes in gene expression between healthy and diseased states.
3. Model validation: Cross-reference the findings with existing biomarkers or databases (e.g., TCGA (The Cancer Genome Atlas)).

Datasets:
Single-cell RNA-seq datasets from public repositories (NCBI GEO Database)