A0813
Title: Supervised capacity preserving mapping: A clustering guided visualization method for scRNAseq data
Authors: Yuying Xie - Michigan State University (United States) [presenting]
Abstract: The rapid development of scRNA-seq technologies enables the exploration of the transcriptome at the cell level on a large scale. However, current visualization methods, including t-SNE and UMAP, are challenged by the limited accuracy of rendering the geometric relationship of populations with distinct functional states. Most visualization methods are unsupervised, leaving out information from the clustering results or given labels. This leads to the inaccurate depiction of the distances between the bona fide functional states. In particular, UMAP and t-SNE are not optimal for preserving the global geometric structure. They may result in a contradiction that clusters with near distance in the embedded dimensions are, in fact, further away in the original dimensions. Besides, UMAP and t-SNE cannot track the variance of clusters. Through the embedding of t-SNE and UMAP, the variance of a cluster is not only associated with the true variance but also is proportional to the sample size. SupCPM, a robust supervised visualization method, is presented, which separates different clusters, preserves the global structure and tracks the cluster variance. Compared to six visualization methods using various datasets, supCPM shows improved performance than other methods in preserving the global geometric structure and data variance. Overall, supCPM provides an enhanced visualization pipeline to assist the interpretation of functional transition and accurately depict population segregation.
