sc-RNA-analysis

Functional Genomics final project which explores single cell RNA and trajectory analysis of differentiating pancreatic stem cells

Goals of this project:

1. Identifying cell type identity through gene expression
2. Observing gene expression changes through differentiation
3. Hypothesize developmental trajectory using gene expression
4. Model developmental trajectories and confirm hypothesis

Dataset of interest:

Dataset link - This dataset contains scRNA data of human pluripotent stem cells differentiating into pancreatic cells taken at different time points. I found this data to be interesting as it was suitable to perform trajectory analysis and understand how gene expression changes through cell differentiation.

Method of interest:

Standard single cell RNA analysis and trajectory analysis using PAGA and Slingshot. I chose these methods as they can perform trajectory analysis without the need of spliced/unspliced RNA data like sc-Velo or VeloCyto and also explore how I can incorporate gene expression inferences into lineage identification.

PAGA - PAGA trajectory analysis is a method for mapping cell development paths in single-cell RNA sequencing, highlighting how cells transition between different states or types during differentiation.

Slingshot - Slingshot is a computational tool used for inferring cellular lineages and trajectories in single-cell RNA sequencing data. It identifies differentiation paths in multi-dimensional data, allowing researchers to trace the progression of cell states and types over time.

Summary of files:

1. Final_Project_Report_Riddhi_Sera.pdf - The final report (duh.)
2. Project Code.ipynb - Code for performing sc-RNA analysis and PAGA
3. R code for slingshot.R - Code for implementing Slingshot using Seurat