Open Collections

Flow cytometry data analysis pipeline : data quality control tool development and biomarker discovery

University of British Columbia

Technical complications occurring during the data acquisition process can impact the quality of the cytometry data and its analysis results. Clogs can cause spikes in the data sets in the time domain. Other issues, such as changing machine acquisition speed, can result in a shift in means of the populations analyzed. The outliers can potentially bias the downstream analysis if left unchecked and, as such, should be identified and removed. To address this need, I developed flowCut is an R package for automated detection of anomaly events and flagging of files for flow cytometry experiments. Results are on par with manual analysis, and it outperforms the existing approaches in data quality control. flowCut has the highest F1 scores in two types of evaluations used in this study and has zero crash rate on all files tested. I also studied the bone marrow regeneration pattern of acute myeloid leukemia patients after chemotherapy by applying state of the art automated methods. I identified cell populations and biomarkers that are uniquely present in relapsed patients when comparing to normal bone marrow data. I also identified cell populations that have different regeneration dynamics between relapsed and non-relapsed patients.

Text

2020-04-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/74076

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/