---
title: "Introduction to ScRNA-Seq Data Analysis"
canonical: "https://help.biobam.com/space/BTP/3355672577/Introduction%20to%20ScRNA-Seq%20Data%20Analysis"
format: markdown
---
**Duration: **30 Minutes

**Lecture Material:**

- [PPT](https://drive.google.com/file/d/1Lm43wDNn8mK0K4zHX_NfacztYZuvdbFv/view?usp=drive_link)

---

## Outline

![image](media://e3322d11-b413-4f2a-af6f-93c1ef74e3f3)

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# Quick Summary

### Introduction

The fascinating domain of single-cell data analysis offers insights into the unique biological processes within individual cells, revealing patterns that can remain masked in bulk-cell studies. Analysing an individual cell's genomic or transcriptomic information presents opportunities and challenges. 

### The Importance of Pre-Processing

Every robust analytical workflow begins with data pre-processing. Here are the critical stages:

- **Pre-Alignment Quality Control**: This stage examines the quality of raw sequence reads using tools like FastQC.
- **Adapter Trimming & Filtering**: Tools such as Cutadapt come in handy, ensuring reads are free from technical artefacts.
- **Reference-Based Alignment**: Processed reads are then aligned to reference genomes using algorithms like Cell Ranger, Alevin, or STARsolo, producing a count matrix.
- **Count Matrix QC**: A crucial step where the count matrix undergoes rigorous quality checks, including doublet detection, imputation, and filtering techniques.

### **Unveiling Cell-Types through General Analysis**

Post-pre-processing, the data undergoes several transformative processes:

- **Normalization & Standardization**: This step removes biases, ensuring differences in gene expression are genuinely biological. Methods range from Counts Per Million to Quantile Normalization.
- **Feature Selection**: From the myriad of genes detected, a subset showing informative variation is chosen using techniques like Variance Thresholding or dispersion-based methods.
- **Dimension Reduction**: Transforming the high-dimensional gene expression data into a lower-dimensional space aids visualization and analysis. Tools such as PCA, t-SNE, and UMAP are commonly used.
- **Clustering**: Cells are grouped based on the similarity of their gene expression profiles using techniques like K-means clustering or density-based methods.
- **Cell-Type Inference**: Individual cells are categorized into specific cell types or subtypes based on their expression profiles, helping decode tissue heterogeneity.

### Exploratory Analysis

- **Differential Expression & Abundance**: Identifying genes that exhibit significant expression differences between groups of cells.
- **Trajectory Analysis**: This offers a temporal view, positioning cells based on their transition through various states.
- **Cell-Cell Communication**: Here, multi-cellular coordination and tissue functions are inferred, shedding light on how cells interact.

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### References & Useful links

1. [Jovic D, Liang X, Zeng H et al. Single‐cell RNA sequencing technologies and applications: A brief overview. Clinical & Translational Med 2022;12, DOI: 10.1002/ctm2.694.](https://onlinelibrary.wiley.com/doi/full/10.1002/ctm2.694)
2. [Orchestrating Single-Cell Analysis with Bioconductor](https://bioconductor.org/books/release/OSCA/)
3. [Single-cell best practices](https://www.sc-best-practices.org/preamble.html)
4. [Awesome Single Cell](https://github.com/seandavi/awesome-single-cell)
5. [Analysis of single-cell RNA-seq data](https://www.singlecellcourse.org/)