Data Analysis

Although the mass cytometry platform enables us a more comprehensive understanding of immune responses, the visualization and interpretation of high-dimensional data produced by mass cytometry are extremely challenging. Therefore, it is necessary to adapt advanced data analysis algorithms for dimensionality reduction or automated clustering.

For instance, dimensionality reduction algorithms, such as t-distributed stochastic neighbor embedding (t-SNE) or uniform manifold approximation and projection (UMAP), were used in the representation of high-dimensional mass cytometry data in easy-to-understand two-dimensional (2D) plots. Additionally, automated clustering algorithms, such as PhenoGraph and FlowSOM, can be used to distinguish cellular clusters based on their attribute characteristics.

Thus, mass cytometry, combined with high-dimensional data analysis algorithms, provides insights into complex immune systems and enables a more comprehensive understanding of their heterogeneity.

As a part of the "immune@YIL" services, we provide the following Multi-parametric, High-Dimensional Data Analysis services for Mass Cytometry and scRNAseq data.

• Dimensionality Reduction & 2D visualization

• Phenotyping Immune Cells and their Subsets

• Quantifying Cellular Expressions of Various Proteins

• Customized Data Analysis Pipeline Development

Our service workflow is here !!