Customer Interview: Jamie D. McNicol, Analytical Project Manager
Q: Can you please tell us about your work on a high level?
Jamie: I am working as Data Scientist in the Human Immune Testing Suite (HITS) of the Immunology Department of McMaster University. We help our clients with immune monitoring for clinical trials. I am taking care of the overall analysis pipeline for our running studies
Q: What is a longitudinal study for you?
Jamie: To me, a longitudinal study involves multiple time points and several patients observed over time to see the impact of a treatment. It has variables (e.g., it involves different time points or hundreds of samples) that prevents you from acquiring all the samples in one day. Often longitudinal studies are quite long and may be conducted across different institutions. This means that you cannot do the entire study with one set of antibodies or the same cytometer calibration. Detailed planning of data acquisition, management and analyzing is required.
Jamie: We’re very blinded so we only store sample ID, treatment day and the collected FCS files. When we process blood for studies, there is a lot of paperwork involved to ensure the samples are processed similarly, and the resulting tubes are labelled accordingly prior to cryo-preservation. We have laboratory management software to hold the information. FCS files permit us to store a wealth of metadata, including antibody information, cytometer calibration settings, and other user-input data. I highly recommend taking advantage of these built-in ways of carrying data into downstream analyses.
Q: When planning a longitudinal study what are the most important points that you need to consider?
Q: What do you find to be the biggest challenges in analyzing data from longitudinal
Jamie: In the end of a study, you need to be able to compare the data reliably. That is the most important thing for me. FCS files from different patients being acquired on different instruments or even on different days can look very different and might not compare very well. An ideal raw data repository for a longitudinal study would be secure, continuously backed-up, searchable, and available to software analysis programs directly.
Q: What about file size?
Jamie: I think that the size of FCS files is not necessarily a limiting factor when it comes to analysis of longitudinal studies. Processing time is more of a concern, particularly when applying unbiased machine learning algorithms in your pipeline.
Learn more about how much data can one expect at the end of a study.Learn more
Cloud-based platform that offers machine-learning assisted analysis of high dimensional single cell data.Learn More
Enterprise licenses provide access to a private cloud and are designed to meet the needs of larger research teams in institutions or biopharma R&D or clinical research organizations.Get started
Machine Learning Quiz
Test how much you know about machine learning and learn how you can apply it to your research.Learn More