In our early mass cytometry work, the analysis of high-dimensional datasets using manual methods became prohibitive, requiring the use of computational analysis approaches. However, the analysis tools available at the time were not well suited to the analysis of large cytometry datasets, consistings of millions of single cells. To address these limitations, I developed the ‘Cytometry Analysis Pipeline for large and compleX datasets’ (CAPX), an analysis workflow using the R programming language (Ashhurst et al. 2019).
Following a seed funding grant from the Marie Bashir Institute for Infectious Diseases and Biosecurity (“Mapping dynamic immunity: next-generation computational approaches to track the evolution of immune responses in West Nile virus and Zika virus encephalitis”), we established the ‘Immune Dynamics’ team, a collaborative group with a focus on the development of novel computational analysis tools to address challenges in high-dimensional analysis. My team and I have developed a number of computational analysis approaches, including SPECTRE, an R package that enables comprehensive end-to-end integration and analysis of high-dimensional cytometry data from different batches or experiments (Ashhurst*, Marsh-Wakefield*, Putri* et al. bioRxiv. 2020). Spectre allows for… … … This work lead to the participation of members of the team (Dr. Thomas Ashhurst, Dr. Felix Marsh-Wakefield, and Givanna Putri) as speakers in the ACS High-Dimensional Cytometry Analysis in 2020.
To facilitate the analysis of time-series data, we have also developed a number of time-series clustering algorithms, including ChronoClust (Putri et al. 2019) and TrackSOM, which we recently applied to the study of COVID-19 (Koutsakos et al. 2021). We have also developed novel multi-perspective methods for the evaluation of clustering algorithms (Putri et al. 2021).
Most recently, we have adapted the Spectre package to facilitate spatial analysis of IMC data (SpectreMAP Github).
Our currenty efforts… our ongoing work is focused on the development of better data integration approaches and spatial analysis tools.