ChronoClust: Density-based clustering and cluster tracking in high-dimensional time-series data

This repository contains the WNV data files to reproduce the analyses in our paper on introducing a density-based clustering and cluster tracking algorithm for high dimensional time-series cytometry data. About the dataset: West Nile Virus (WNV) is disseminated by mosquitoes, causing infection of the central nervous system and severe neurological disease, which may culminate in death or permanent neurological damage in survivors. Our WNV dataset quantifies the immune response of WNV-infected mice over eight days: from day 0 (no infection) to day 7. For each day, immune cells were extracted from the bone marrow of four mice and analysed by flow cytometer. Of them, 190,000 cells from each day were obtained for ChronoClust. Expression levels of nine proteins were measured per cell: (1) B220, (2) CD3/NK11, (3) Ly6C, (4) CD115, (5) CD11b, (6) Ly6G, (7) SSC-A, (8) CD117, (9) SCA-1 Python scripts to reproduce all analyses and figures in the paper (including scripts to prepare the data files) are available from GitHub at: https://github.com/ghar1821/Chronoclust. The final paper is published in Knowledge Based Systems (KBS) journal available at: https://doi.org/10.1016/j.knosys.2019.02.018
Notes:
The dataset and gating files were converted from CSV to FCS format using the script available from Github: https://github.com/sydneycytometry/CSV-to-FCS