Comparison Between UMAP and t-SNE for Multiplex-Immunofluorescence Derived Single-Cell Data from Tissue Sections

Abstract

Using human hepatocellular carcinoma (HCC) tissue samples stained with seven immune markers including one nuclear counterstain, we compared and evaluated the use of a new dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP), as an alternative to t-Distributed Stochastic Neighbor Embedding (t-SNE) in analysing multiplex-immunofluorescence (mIF) derived single-cell data. We adopted an unsupervised clustering algorithm called FlowSOM to identify eight major cell types present in human HCC tissues. UMAP and t-SNE were ran independently on the dataset to qualitatively compare the distribution of clustered cell types in both reduced dimensions. Our comparison shows that UMAP is superior in runtime. Both techniques provide similar arrangements of cell clusters, with the key difference being UMAPs extensive characteristic branching. Most interestingly, UMAPs branching was able to highlight biological lineages, especially in identifying potential hybrid tumour cells (HTC). Survival analysis shows patients with higher proportion of HTC have a worse prognosis (p-value = 0.019). We conclude that both techniques are similar in their visualisation capabilities, but UMAP has a clear advantage over t-SNE in runtime, making it highly plausible to employ UMAP as an alternative to t-SNE in mIF data analysis.