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Single-cell RNA sequencing analysis

Updated: Sep 19, 2023

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technique that enables researchers to explore the intricacies of cell types, states, and interactions across various human tissues. By providing a glimpse into the gene expression profiles of individual cells, scRNA-seq offers a more detailed understanding of cellular diversity within tissues.


This cutting-edge method has a wide range of applications. Firstly, it allows researchers to identify and characterize the cell types in a given tissue, shedding light on the cellular makeup of complex systems. Additionally, scRNA-seq can uncover rare or previously unknown cell types or states, expanding our knowledge of cellular heterogeneity. It also enables the elucidation of gene expression changes during differentiation processes across time or conditions, providing valuable insights into cellular development.







Technical Variability Across Cells/Samples.

Technical variability in scRNA-Seq can make cells look more alike or different than they are because of technical reasons, not biology.


1. Cell-Specific Capture Efficiency: Some cells may capture fewer RNA molecules than others, leading to differences in data quality. This phenomenon can affect up to half of the RNA information we want to capture.


2. Library Quality: The quality of the data we get from scRNA-Seq depends on how well we handle the cells and RNA. If we mishandle cells, have low-quality RNA, or make mistakes, it can mess up the results.


3. Amplification Bias: When we make many copies of the RNA to study it, we sometimes make different numbers of copies for all genes. This can cause some genes to seem more or less active than they are.


4. Batch Effects: When we process cells in different groups (batches), it can introduce fake differences in the data just because of the batch, not because the cells are different.


Dealing with these technical challenges is essential to ensure reliable results from scRNA-Seq. By paying attention to data quality, being careful with amplification, and handling batch effects, we can better identify the characteristics of different cell types and states.


explore QC metrics and filter cells based on any user-defined criteria.

Explore QC metrics and filter cells based on any user-defined criteria. We use QC metrics to visualize the number of genes (nFeature_RNA), molecules in cells (nCount_RNA), and the percent mitochondrial genes. Each dot in the following plots represents a cell. The dot plots show the relationship between these features

Deciding which PCs will included in further downstream analyses.

Cells and features are arranged based on their PCA scores. Clustering cells is done using these PCA scores, where each Principal Component (PC) essentially represents a composite feature that combines information from a group of correlated features.


Non-linear dimensional reduction techniques to visualize and explore the dataset.

The algorithm uncovers the inherent structure of the data, grouping similar cells in a lower-dimensional space.

A typical single-cell data analysis pipeline includes the following steps:

  1. Data Pre-processing: This involves alignment and quantification using standard bioinformatics tools like STAR or CellRanger.

  2. Quality Control: Evaluating raw expression data, including detecting doublets.

  3. Batch Effect Correction: Aligning and harmonizing multiple datasets for combined analysis (e.g., Seurat CCA, Harmony, LIGER).

  4. Unsupervised Clustering: Using graph-based clustering algorithms to identify and visualize cell populations with UMAP.

  5. Cluster Quality Control: Evaluating identified clusters to eliminate technical or irrelevant biological biases.

  6. Cell Type Annotation: Assigning cell types to the identified clusters.

  7. Differential Expression Analysis: Identifying genes that are differentially expressed between cell populations.

  8. Functional Enrichment Analysis: To understand biological functions, utilize curated resources such as the Reactome Pathway Database and the Gene Ontology (GO) resource.


 

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