# 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>" %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://edu.abi.am/ngs-data-analysis-and-functional-genomics/single-cell-sequencing-and-data-analysis/scrna-seq-data-analysis-workflow.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.