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Notes -
Interesting. I've spent a lot of time staring at t-SNE plots (or more recently UMAPs took over) and they map pretty well to our underlying understanding of the biology. It got a bit hairy when we asked it to split the data into too many clusters and it was difficult to know if we were looking at some novel, minor cell type or a hallucination.
I think I asked that question poorly and also lack the vocabulary to describe what I'm envisioning. Current software for analyzing this kind of data (flow) exists and the typical workflow is just making a series of scatterplots with 'gates,' or subsets of cells that express a given marker. Here's a basic example.
Verbally, it's all very simple - Gate on singlets, then lymphocytes via forward/side scatter, exclude dead cells, gate on CD3+ and then split into CD4 and CD8 T cells. It's the kind of instruction that should be very easy for chatGPT to parse even with a single sentence outlining the experiment. But how to feed the data? Is there a way for chatGPT to interact with an existing analysis software to draw gates/generate scatterplots...? I assume you wouldn't want to feed the raw array of cells into your prompt, although I don't know.
Maybe I'll back up and zoom out a bit. Most people use flowjo to analyze flow cytometry data. It's a multibillion dollar industry, they haven't updated the software in something like a decade (and that update made it worse than the version I was using before), and you routinely draw the same gates over and over again. Imagine you have 50 samples in your experiment, each sample has 10 gates so you're skimming over 500 scatter plots and then inputting however many readouts you have into other histogram plots to represent the data you got. It's repetitive and the software is clunky. LLMs definitely seem 'smart' enough to understand everything that's going on, but I don't have the first idea how you communicate that kind of data to them...
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