ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.

High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in high-dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

Effects of Normobaric Hypoxia on Oxygen Saturation Variability.

Background: The study is the first to evaluate the effects of graded normobaric hypoxia on SpO2 variability in healthy individuals. Materials and Methods: Twelve healthy males (mean [standard deviation] age 22 [4] years) were exposed to four simulated environments (fraction of inspired oxygen [FIO2]: 0.12, 0.145, 0.17, and 0.21) for 45 minutes, in a balanced crossover design. Results: Sample entropy, a tool that quantifies the irregularity of pulse oximetry fluctuations, was used as a measure of SpO2 variability. SpO2 entropy increased as the FIO2 decreased, and there was a strong significant negative correlation between mean SpO2 and its entropy during hypoxic exposure (r = -0.841 to -0.896, p < 0.001). In addition, SpO2 sample entropy, but not mean SpO2, was correlated (r = 0.630-0.760, p < 0.05) with dyspnea in FIO2 0.17, 0.145, and 0.12 and importantly, SpO2 sample entropy at FIO2 0.17 was correlated with dyspnea at FIO2 0.145 (r = 0.811, p < 0.01). Conclusions: These findings suggest that SpO2 variability analysis may have the potential to be used in a clinical setting as a noninvasive measure to identify the negative sequelae of hypoxemia.