RESUMEN
Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.
RESUMEN
As the coronavirus disease 2019 pandemic continues to impact hospital systems both in the United States and throughout the world, it is important to understand how the pandemic has impacted the volume of hospital admissions. Using the Vizient Inc (Chicago, IL) clinical databases, we analyzed inpatient hospital discharges from the general medicine service and its subspecialty services including cardiology, neonatology, pulmonary/critical care, oncology, psychiatry, and neurology between December 2019 and July 2020. We compared baseline discharge data to that of the first six months of the pandemic, from February to July 2020. We set the baseline as discharges by specialty from February 2019 through January 2020, averaged over the 12 months. Compared to baseline, by April 2020 the volume of general medicine hospital discharge was reduced by -20.2%, from 235,581 to 188,027 discharges. We found that while overall the number of discharges decreased from baseline, with a nadir in April 2020, pulmonary/critical care services had an increase in hospital discharge volume throughout the pandemic, from 7534 at baseline to 15,792 discharges in April. These findings are important for understanding health care use during the pandemic and ensuring proper allocation of resources and funding throughout the coronavirus disease 2019 pandemic.
RESUMEN
Phylogenetic tree reconciliation is widely used in the fields of molecular evolution, cophylogenetics, parasitology, and biogeography to study the evolutionary histories of pairs of entities. In these contexts, reconciliation is often performed using maximum parsimony under the Duplication-Transfer-Loss (DTL) event model. In general, the number of maximum parsimony reconciliations (MPRs) can grow exponentially with the size of the trees. While a number of previous efforts have been made to count the number of MPRs, find representative MPRs, and compute the frequencies of events across the space of MPRs, little is known about the structure of MPR space. In particular, how different are MPRs in terms of the events that they comprise? One way to address this question is to compute the diameter of MPR space, defined to be the maximum number of DTL events that distinguish any two MPRs in the solution space. We show how to compute the diameter of MPR space in polynomial time and then apply this algorithm to a large biological dataset to study the variability of events.
