Single-Cell RNA Trajectory Analysis using Monocle 3:
Study Design:
Conceptualized and designed a single-cell RNA sequencing experiment to investigate dynamic changes in gene expression across different cellular states. Experimental Execution:
Performed single-cell RNA sequencing on GSE132771 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132771)to generate high-dimensional gene expression profiles for individual cells. Quality Control and Preprocessing:
Conducted rigorous quality control measures to filter out low-quality cells and genes. Preprocessed raw sequencing data, including normalization, log transformation, and identification of highly variable genes. Dimensionality Reduction:
Applied dimensionality reduction techniques such as principal component analysis (PCA) to capture the major sources of variability in the dataset. Trajectory Inference with Monocle 3:
Utilized Monocle 3 for trajectory inference, reconstructing the developmental trajectory of cells and identifying branching points. Cell State Annotation:
Annotated cellular states along the trajectory based on differential gene expression, identifying key markers for each state. Visualization:
Generated visualizations, including trajectory plots, heatmaps, and t-SNE visualizations, to effectively communicate the temporal progression of cellular states. Functional Enrichment Analysis:
Conducted functional enrichment analysis on genes associated with different cell states to gain insights into biological processes driving cellular transitions. Validation and Interpretation:
Validated findings through cross-validation and comparison with existing literature. Provided biological interpretations of observed trajectories and identified potential key regulators. Communication of Results: This outline provides a structured and detailed overview of your single-cell RNA trajectory analysis experience using Monocle 3.