Burke, P. E. P. | bioRxiv Science

bioRxiv2022

PhenoComb: A discovery tool to assess complex phenotypes in high-dimension, single-cell datasets

MotivationHigh-dimension cytometry assays can simultaneously measure dozens of markers, enabling the investigation of complex phenotypes. However, as manual gating relies on previous biological knowledge, few marker combinations are often assessed. This results in complex phenotypes with potential for biological relevance being overlooked. Here we present PhenoComb, an R package that allows agnostic exploration of phenotypes by assessing all combinations of markers. DesignPhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high) and then counts the number of cells per sample from all possible marker combinations in a memory-safe manner. Time and disk space are the only constraints on the number of markers evaluated. PhenoComb also provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes, and assess the independence of identified phenotypes. PhenoComb allows users to guide analysis by adjusting several function arguments such as identifying parent populations of interest, filtering of low-frequency populations, and defining a maximum complexity of phenotypes to evaluate. We have designed PhenoComb to be compatible with local computer or server-based use. ResultsIn testing of PhenoCombs 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. Collectively, PhenoComb represents a powerful discovery tool for agnostically assessing high-dimension, single-cell data. AvailabilityThe PhenoComb R package can be downloaded from https://github.com/SciOmicsLab/PhenoComb

bioinformatics↗

bioRxiv2020

Accelerated Simulation of Large Reaction Systems Using a Constraint-Based Algorithm

Simulation of reaction systems has been employed along decades for a better understanding of such systems. However, the ever-growing gathering of biological data implied in larger and more complex models that are computationally challenging for current discrete-stochastic simulation methods. In this work, we propose a constraint-based algorithm to simulate such reaction systems, called the Constraint-Based Simulation Algorithm (CBSA). The main advantage of the proposed method is that it is intrinsically parallelizable, thus being able to be implemented in GPGPU architectures. We show through examples that our method can provide valid solutions when compared to the well-known Stochastic Simulation Algorithm (SSA). An analysis of computational efficiency showed that the CBSA tend to outperform other considered methods when dealing with a high number of molecules and reaction channels. Therefore, we believe that the proposed method constitutes an interesting alternative when simulating large chemical reaction systems.

systems biology↗