Introduction to ScRNA-Seq Data Analysis
Duration: 30 Minutes
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.
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.