ABSTRACT
Background Modern cytometry can simultaneously measure dozens of markers, empowering investigation of complex phenotypes. However, manual gating relies on previous biological knowledge, and clustering/dimension-reduction tools fail to capture discrete phenotypes. Consequently, complex phenotypes with potential biological importance are often overlooked. To address this, we developed PhenoComb, an R package that allows agnostic exploration of complex phenotypes by assessing the frequencies of all marker combinations in cytometry datasets. Methods PhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high). As Pheno- Comb works in a memory-safe manner, time and disk space are the only constraints to the number of markers and discrete states that can be evaluated. Next, the number of cells per sample from all possible marker combinations are counted and frequencies assessed. PhenoComb provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes, and assess the independence of identified phenotypes. PhenoComb also allows users to guide analysis by adjusting several function arguments such as identifying parent populations of interest, filtering low-frequency populations, and defining a maximum marker complexity. PhenoComb is compatible with local computer or server-based use. Results In testing of PhenoComb's performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Conclusions PhenoComb is a unique and powerful tool for agnostically assessing phenotypes. By more fully utilizing the high-dimension data in single cell datasets, PhenoComb empowering exploratory data analysis and discovery of phenotypes for further characterization.
ABSTRACT
The ongoing pandemic of SARS-CoV-2 calls for rapid and cost-effective methods to accurately identify infected individuals. The vast majority of patient samples is assessed for viral RNA presence by RT-qPCR. Our biomedical research institute, in collaboration between partner hospitals and an accredited clinical diagnostic laboratory, established a diagnostic testing pipeline that has reported on more than 252,000 RT-qPCR results since its commencement at the beginning of April 2020. However, due to ongoing demand and competition for critical resources, alternative testing strategies were sought. In this work, we present a clinically-validated procedure for high-throughput SARSCoV-2 detection by RT-LAMP in 25 minutes that is robust, reliable, repeatable, sensitive, specific, and inexpensive © 2021. Buck MD et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited