This project investigates the functional heterogeneity of mesenchymal stromal cell (MSC) subtypes using proteomic analysis. MSCs are a subset of non-hematopoietic adult stem cells with the potential to differentiate into multiple cell types, making them crucial for regenerative medicine. However, MSC populations exhibit heterogeneity in their differentiation and immunomodulatory capacities. This study analyzes the soluble protein fraction of five immortalized MSC lines using mass spectrometry to understand differences in protein expression. The analysis includes data preprocessing, normalization, principal component analysis (PCA), hierarchical clustering, and visualization.
Contact Information: Emma Rand (emma.rand@york.ac.uk)
/BIO00058M-case-study
│── data-raw/ # Raw data files
│ ├── Y101_Y102_Y201_Y202_Y101-5.csv # Normalized protein abundances
│ ├── comparison_p_and_q.csv # Pairwise comparisons p/q values
│── data/ # Processed data
│── reports/ # Final report i(HTML) linked above
│── R/ # R scripts for analysis
│ ├── 00-pkg.R # Loads required packages
│ ├── data_summary.R # Functions for summary statistics
│ ├── 01-labels.R # Cell lineage and functionality labels
│ ├── 02-palette.R # Color scheme for visualization
│ ├── 03-banner.R # Generates a cell-type banner
│── sessioninfo.md # R session information
│── README.md # Project documentation
│── BIO00058M-case-study.Rproj # RStudio project file
- R (version 4.1.1, 2021-08-10)
- RStudio 2021.09 "Ghost Orchid"
- Platform: Windows 10 x64 (build 18363)
tidyverse(1.3.1)janitor(2.1.0)ggplot2(3.3.5)GGally(2.1.2)heatmaply(1.3.0)plotly(4.10.0)VennDiagram(1.7.0)patchwork(1.1.1)bookdown(0.24)sessioninfo
To install all required packages, use:
install.packages(c("tidyverse", "janitor", "ggplot2", "GGally",
"heatmaply", "plotly", "VennDiagram", "patchwork",
"bookdown", "sessioninfo"))The dataset consists of mass spectrometry data from five MSC subtypes, focusing on soluble protein fractions.
-
Y101_Y102_Y201_Y202_Y101-5.csv(Normalized protein abundances)- Rows: Proteins
- Columns:
Accession: Protein identifier (Uniprot ID)Peptide count: Number of peptides used to identify the proteinUnique peptides: Number of unique peptides used for identificationConfidence score: Score representing confidence in protein identificationAnova (p): P-value from one-way ANOVA for the effect of cell lineq Value: False discovery rate (FDR)-adjusted p-value (Benjamini-Hochberg correction)Max fold change: Maximum fold-change between the highest and lowest mean expression across cell linesPower: Statistical power of the ANOVA testHighest mean condition: Cell line with the highest mean expressionLowest mean condition: Cell line with the lowest mean expressionMass: Protein massDescription: Protein description (includes species origin)- Normalized Abundance Columns:
Y101_A, Y101_B, Y101_C: Replicates for cell line Y101Y102_A, Y102_B, Y102_C: Replicates for cell line Y102Y201_A, Y201_B, Y201_C: Replicates for cell line Y201Y202_A, Y202_B, Y202_C: Replicates for cell line Y202Y1015_A, Y1015_B, Y1015_C: Replicates for cell line Y101.5
>1pep: Binary indicator for whether at least two peptides were detected for a protein.
-
comparison_p_and_q.csv(Pairwise comparisons between cell lines)- Columns:
- Pairwise p-values and q-values for differential protein abundance tests
- Columns:
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Set up the environment
- Open
BIO00058M-case-study.Rprojin RStudio.
- Open
-
Generate the report
- Knit
main.Rmdin RStudio to generate reports in HTML, PDF, or Word. - Knit directory of report/main.Rmd is document directory.
- Knit