Bioinformatics - single-cell RNA-seq

Single-cell RNA sequencing (scRNA-seq) enables the exploration of gene expression at the resolution of individual cells. This method uncovers cellular heterogeneity, identifies cell types, and reveals gene expression dynamics in complex tissues.

Pipeline Overview

1. Demultiplexing: Assigning reads to individual cells based on barcode sequences.
2. Quality Control (QC): Analyzing data for low-quality cells, doublets, and high mitochondrial gene expression using tools like FastQC and Seurat.
3. Preprocessing: Filtering out low-quality or low-abundance cells and normalizing data.
4. Clustering: Grouping cells with similar expression profiles to identify distinct cell populations (e.g., using Seurat or Scanpy).
5. Differential Expression: Identifying genes that are differentially expressed between clusters or conditions (e.g., using Seurat, MAST).
6. Trajectory Analysis: Identifying cell differentiation or developmental trajectories.
7. Visualization: Visualizing the results through t-SNE, UMAP, and heatmaps to explore cellular relationships.

Expected Result Output

1. Main Results File:
   • An .html file summarizing the analysis results, including UMAP, clustering, marker expression dot plots, etc.
   • Seurat Object in RDS format, consisting of expression values for each gene across all cells.
   • Cell Ranger COUNT function outputs (web summary and filtered_feature_bc_matrix).
   • A .cloupe file for results viewing and further analysis using 10X Genomics Loupe Browser.

Additional analysis results and plots can be generated upon request.

[UMAP figure]

[Markers expression figure]