Single-cell RNA sequencing (scRNA-seq) data analysis involves several fundamental steps to extract meaningful insights from the tr anscriptomes of individual cells. Here's a concise summary of the basic steps in scRNA-seq data analysis:

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  Data Preprocessing: Assess and filter out low-quality cells and reads and map sequencing reads to a reference genome or transcriptome.
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  Normalization: Adjust for differences in sequencing depth and other technical biases to ensure accurate comparisons between cells.
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  Feature Selection: Identify informative genes or features, often by considering expression levels and variability.
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  Dimensionality Reduction: Reduce the high-dimensional data to a lower-dimensional space using techniques like PCA or t-SNE for visualization and clustering.
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  Cell Clustering: Group cells into clusters based on their gene expression profiles, revealing distinct cell types or states.
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  Marker Gene Identification: Identify marker genes specific to each cluster to characterize cell types or subpopulations.
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  Trajectory Analysis (Optional): Reconstruct developmental or differentiation trajectories to understand cell state transitions and lineage relationships.
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  Differential Expression Analysis: Identify genes that are differentially expressed between cell clusters or conditions, highlighting potential drivers of biological processes.
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  Functional Enrichment Analysis: Determine the biological functions and pathways associated with differentially expressed genes.
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  Data Visualization: Generate informative visualizations like heatmaps, violin plots, and lineage trees to aid in data interpretation and communication.