Points of Significance: Principal Component Analysis for Biocentric Data Visualization

Arbatsky, M.; Tyurin-Kuzmin, P.; Kulebyakin, K.; Chechekhin, V.; Kalinina, N.; Sysoeva, V.; Semina, E.; Rubina, K.

Авторы: Arbatsky Mikhail, Tyurin-Kuzmin Pyotr, Kulebyakin Konstantin, Chechekhin Vadim, Kalinina Natalia, Sysoeva Veronika, Semina Ekaterina, Rubina Kseniya
Журнал: BioNanoScience
Том: 12
Год издания: 2022
Первая страница: 1366
Последняя страница: 1380
DOI: 10.1007/s12668-022-01021-w
Аннотация: Driven by resolving power of scRNA-seq analysis, multiple studies highlighted complexity and hierarchy of adipose-derived mesenchymal stem cells. Commonly used scRNA-seq data analysis utilizes a standard set of data processing tools, including preprocessing and downstream processing such as dimensionality reduction and clustering. Principal component analysis (PCA) is one of the standard algorithms used for dimensionality reduction. Among the generated PC, the first two are most commonly used to describe highly variable genes, while the rest PC are dismissed in most of the studies. However, biocentric clustering of downregulated and upregulated genes along these components allows for selecting specific components to describe similar biological processes. The differences between cell subtypes relevant for biologists may be yet mathematically insignificant. Here we aimed to unlock new data by constructing PCA plots using the coordinates by the components of the upregulated genes associated with specific biological processes. PCA was performed on the variable genes, and the first 20 components responsible for extracellular matrix, immune response, cell signaling, and cell differentiation were selected. This approach ensured not only cell clustering within the coordinates of a specific biological function, but also considering the clusters related to the biological process of interest in detail.
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