Exploratory Data Analysis of Mouse Dataset GSE141259

Overview

This project involves the analysis of the GSE141259 mouse dataset, focusing on exploratory data analysis (EDA) techniques. The analysis includes data normalization, clustering, cell-cell communication studies using CellChat, and cell subtype characterization. The goal is to gain insights into the cellular composition and interactions in the dataset through advanced computational techniques.

Project Structure

Key Analyses Performed:

1. Data Normalization:

   • The raw gene expression data was normalized to ensure that cells and genes are on a comparable scale. This step is essential for reliable downstream analysis, such as clustering and differential expression analysis.

2. Clustering:

   • Unsupervised clustering was performed to identify distinct cell populations in the dataset. Clustering allows us to categorize cells based on their gene expression profiles, highlighting the heterogeneity present in the sample.

3. Cell-Cell Communication Analysis with CellChat:

   • CellChat, a computational tool, was used to infer cell-cell communication networks. This analysis identifies the signaling pathways that cells use to interact with each other, revealing potential cellular crosstalk and communication patterns within the tissue.

4. Cell Subtype Characterization:

   • Identified clusters were further analyzed to characterize cell subtypes based on marker genes and functional annotations. This step helps in understanding the specific roles of different cell populations within the tissue.

Data Source:

• Accession Number: GSE141259
• The dataset contains gene expression profiles for mouse samples, which have been used in this study to explore cellular heterogeneity and communication.

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Installation and Setup

Required Packages

This analysis was performed using R and the following R packages:

• Seurat: For data normalization, clustering, and visualization.
• CellChat: To infer cell-cell communication networks.
• dplyr: For data wrangling.
• ggplot2: For visualization.
• patchwork: For combining multiple plots.

You can install the required packages using the following code:

install.packages(c("Seurat", "dplyr", "ggplot2", "patchwork"))
BiocManager::install("CellChat")

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Key Steps and Results

1. Data Normalization

The raw gene expression data from the GSE141259 dataset was normalized using log normalization to scale the gene expression values across cells.

Result:

• The normalized data was used in subsequent clustering and communication analyses.

2. Clustering Analysis

Using Seurat, clustering was performed based on the principal components derived from the normalized data. This step categorized the cells into distinct populations based on gene expression similarity.

Result:

• The cells were grouped into clusters representing various cell types. These clusters were visualized using UMAP and t-SNE plots to capture the cellular diversity in the dataset.

3. Cell-Cell Communication with CellChat

CellChat was employed to analyze intercellular communication networks. It inferred the signaling pathways active between the identified clusters, providing insights into how different cell types communicate with one another.

Result:

• A detailed communication network was established, showing the signaling interactions and pathways that connect the different cell types. Pathways such as Wnt, Notch, and TNF signaling were investigated in detail.

4. Cell Subtype Characterization

Each cluster was characterized based on the expression of known marker genes. This step involved identifying specific cell subtypes such as immune cells, epithelial cells, and stromal cells based on the marker profiles.

Result:

• Subtypes of cells were assigned based on known markers, and their roles were inferred through functional enrichment analysis and literature review.

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Visualizations

• UMAP/t-SNE Clustering: Visualizes the clusters of cells in two dimensions, highlighting the separation between different cell types.
• Cell Communication Network: A graphical representation of the inferred cell-cell communication network, showing the active signaling pathways between clusters.
• Heatmaps and Violin Plots: Used to characterize and visualize the expression of marker genes across different cell types.

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Conclusion

This exploratory analysis of the GSE141259-mouse dataset reveals the complex cellular heterogeneity and cell-cell communication within the tissue. The use of CellChat provides insight into how cells interact through signaling pathways, while clustering and subtype characterization highlight the diversity of cell populations.

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Session Info

To view the R session information and the environment used for this analysis, run the following command:

sessionInfo()