Single-cell RNA sequencing (scRNA-seq) is a powerful technique that allows the measurement of gene expression at the resolution of individual cells. This enables a detailed exploration of cellular diversity, rare cell types, and gene regulatory networks in complex tissues. The analysis of scRNA-seq data involves several steps, each crucial for deriving meaningful biological insights.

Key Steps in Single-Cell RNA-seq Data Analysis:

1. Preprocessing and Quality Control (QC):

   • Raw Data Input: scRNA-seq produces raw read data that needs to be aligned to a reference genome to generate a matrix of gene expression counts.
   • Filtering: Cells with low-quality metrics (e.g., low gene counts, high mitochondrial gene content) are filtered out. This step ensures that only viable cells are analyzed.
   • Normalization: scRNA-seq data is often affected by varying sequencing depth. Normalization methods adjust for differences in sequencing depth and allow for meaningful comparisons between cells.