Basic approach to data analysis of longitudinal samples
I am looking for some basic help with data analysis - as someone with limited experience of this and with little knowledge of R programming.
I am using the CyTOF to measure patient samples longitudinally at various time points post treatment. I am trying to identify an immune signature with 22 markers - mainly cell surface, chemokine and a few transcription factors. I am using PBMCs and mainly interested in T cell subsets.
I am aiming to run approx 200-250 patient samples and within each sample (2.5 million cells) I have spiked 0.5 million healthy PBMCs from one donor (frozen previously into aliquots), as a way of controlling for staining variability over time (healthy and patient samples each labelled with a different CD45 metal conjugate, then 3 million total cells stained with panel).
In terms of the data, firstly, I am looking at it manually in flowjo - separating the patient and healthy samples by CD45 gating (after cleaning the data). This is clearly not the best approach long-term but is hopefully enabling me to check that the experiment ‘worked’ and give me any broad clues as to changes in markers / subsets etc. One thing I’m surprised about - or maybe shouldn’t be - is the variation in the healthy sample between each batch (given these are the same cells - stored in batches of 10 million / vial and defrosted for each run). Not so much with CD4 / CD8 / CD27 but certainly with some of the other markers (e.g. CCR9, iNKT etc). I wondered if anyone had any comment on this? Presently, it makes it very hard to look at any changes in the patient samples with any meaning, given the variation in the healthy stained samples. Is anyone familiar with ways of correcting for the staining variation and how I might approach this?
For the longer-term I’m thinking of using CITRUS to look at differences between groups and possibly Phenograph as well.
I have limited biostatistician support where I am, so any recommendations on helpful biostats guys would also be much appreciated. In fact - any words of wisdom at all!
Best wishes,
James