> For the complete documentation index, see [llms.txt](https://edu.abi.am/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://edu.abi.am/ngs-data-analysis-and-functional-genomics/single-cell-sequencing-and-data-analysis/scrna-seq-data-analysis-workflow.md).

# 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](https://satijalab.org/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: 

{% embed url="<https://satijalab.org/seurat/articles/pbmc3k_tutorial.html>" %}

A **Python** alternative to Seurat is [scanpy](https://scanpy.readthedocs.io/en/stable/index.html), a scalable toolkit for analyzing single-cell gene expression data built jointly with [anndata](https://anndata.readthedocs.io/).  You can find a scanpy tutorial on the same PBMC dataset can be found here:

{% embed url="<https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html>" %}