ProteinPCA (version 0.1.0)

pca_analysis: Principal Component Analysis (PCA) Tool on Protein Expression Data

Description

This function performs PCA on protein expression data and produces a biplot using ggplot2. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses.

Value

A list containing:

pca

The PCA object from prcomp.

explained_variance

The percentage of variance explained by each principal component.

plot

A ggplot2 PCA biplot (if plot = TRUE).

Arguments

data

A numeric matrix or data frame of protein expression data. Rows are samples, columns are features.

scale

Logical. Should the data be scaled? Default is TRUE.

center

Logical. Should the data be centered? Default is TRUE.

plot

Logical. Should a PCA biplot be generated? Default is TRUE.

groups

Optional. A factor or character vector specifying group memberships of the samples.

Author

Paul Angelo C. Manlapaz

References

Jolliffe, I. (2001). Principal Component Analysis (2nd ed.). Springer. https://doi.org/10.1007/b98835 Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467. https://doi.org/10.1093/biomet/58.3.453 Zhang, Z., Chen, L., Sun, B., Ruan, Z., Pan, P., Zhang, W., Jiang, X., Zheng, S., Cheng, S., Xian, L., Wang, B., Yang, J., Zhang, B., Xu, P., Zhong, Z., Cheng, L., Ni, H., & Hong, Y. (2024). Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-53239-9 Anandan, A., Nagireddy, R., Sabarinathan, S., Bhatta, B. B., Mahender, A., Vinothkumar, M., Parameswaran, C., Panneerselvam, P., Subudhi, H., Meher, J., Bose, L. K., & Ali, J. (2022). Multi-trait association study identifies loci associated with tolerance of low phosphorus in Oryza sativa and its wild relatives. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-07781-5

Examples

set.seed(123)
data_matrix <- matrix(rnorm(100 * 20), nrow = 100, ncol = 20)
rownames(data_matrix) <- paste0('Sample_', 1:100)
colnames(data_matrix) <- paste0('Protein_', 1:20)
groups <- sample(c("Group A", "Group B"), 100, replace = TRUE)
result <- pca_analysis(data_matrix, groups = groups)
print(result$explained_variance)
print(result$plot)