Recent technological advancements have enabled the profiling of a large number of
genome-wide features in individual cells. However, single-cell data present unique …

With the rapid development of biotechnology, the number of biological sequences has
grown exponentially. The continuous expansion of biological sequence data promotes the …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

Limited access to embryos has hampered the study of human embryogenesis and disorders
that occur during early pregnancy. Human pluripotent stem cells provide an alternative …

A key challenge in analyzing single cell RNA-sequencing data is the large number of false
zeros, where genes actually expressed in a given cell are incorrectly measured as …

Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMap have
demonstrated impressive visualization performance on many real-world datasets. One …

The high-dimensional data created by high-throughput technologies require visualization
tools that reveal data structure and patterns in an intuitive form. We present PHATE, a …

INTRODUCTION The ability to regenerate retinal neurons after injury varies drastically
among vertebrate species. Teleost fish such as zebrafish can regenerate all major retinal …

Our aim here was not to argue which algorithm, t-SNE or UMAP, is more suitable for single-
cell studies. Once informative initialization is used, the two algorithms appear to preserve the …

Current immunotherapy paradigms aim to reinvigorate CD8+ T cells, but the contribution of
humoral immunity to antitumor immunity remains understudied. Here, we demonstrate that in …