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Abstract In a COPSS-NISS webinar focused on leadership at the intersection of statistics and genomics, esteemed panelists Drs. Rafael Irizarry and Mingyao Li shared their leadership journeys and provided insights into this interdisciplinary field to inspire future leaders. They discussed the value of statistics in distinguishing signal from noise in the artificial intelligence (AI) era, the strengths of statisticians in ensuring rigor and robustness in genomics research, and the trade-offs between model expressiveness and interpretability. Additionally, they offered advice on how junior faculty can seek collaborations and increase their visibility, balance staying current with technological advancements, while developing methods carefully and thoroughly, and best practices for collaborating with domain experts. The recording of the webinar is available athttps://www.youtube.com/watch?v=t6SsAoh95ig. 

Abstract Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP’s 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell’s 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP. 

Abstract Two correspondences raised concerns or comments about our analyses regarding exaggerated false positives found by differential expression (DE) methods. Here, we discuss the points they raise and explain why we agree or disagree with these points. We add new analysis to confirm that the Wilcoxon rank-sum test remains the most robust method compared to the other five DE methods (DESeq2, edgeR, limma-voom, dearseq, and NOISeq) in two-condition DE analyses after considering normalization and winsorization, the data preprocessing steps discussed in the two correspondences.