scRNA-seq Data Analysis Workflow

Analyzing single-cell sequencing data involves several key steps:

1. Preprocessing: This includes quality control, normalization, and filtering to ensure high-quality data.

2. Dimensionality Reduction: Techniques like PCA, t-SNE, and UMAP are used to reduce the complexity of the data and visualize the relationships between cells.

3. Clustering: Cells are grouped based on similar gene expression patterns, allowing for the identification of distinct cell types or states.

4. Differential Expression Analysis: Identifying genes that are differentially expressed between cell clusters or conditions to understand the underlying biology.

One of the most used packages for single-cell data analysis is Seurat, which is a comprehensive R toolkit that facilitates the entire analysis workflow, from data preprocessing to advanced visualization and downstream analysis. A guided tutorial of a basic single-cell analysis worklow for a 10X Genomics dataset of Peripheral Blood Mononuclear Cells (PBMC) in Seurat can be found here:

Seurat - Guided Clustering Tutorial

A Python alternative to Seurat is scanpy, a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. You can find a scanpy tutorial on the same PBMC dataset can be found here:

Preprocessing and clustering 3k PBMCs (legacy workflow) — scanpy-tutorials 0.1.dev50+g203afb9 documentation