You should see the Unstim fill the x/y space fairly well and be black to purple. Once you have set the scales for all the channels, switch the plot type to ‘heat plot’. below the median, which is what will be plotted in the 3D plot).Ĥ) The events in your interesting stimulated sample should be above the middle of the plot (the median) or at least spread across it, so that there will be some heat signal when you switch to a heat plot.įor lineage and other markers that do not change across the samples that will be displayed:ġ) Things that stay the same across your samples should be used as x and y axesĢ) The measurement should span the scale range well across your samples. Repeat this process of settings in the min, max, and cofactor for any channel you will see in the heat plot.įor signaling channels and markers that change from sample to sample:ġ) Things that change a lot across your samples should be shown on the z-axis, not the x or yĢ) Phospho-protein signaling should span the scale range well across your samplesģ) The events in your Unstim sample should mostly be below the middle of the plot (i.e. For arcsinh scales, make sure the cofactor is appropriate for the zoomed in scales. You want to set them at ~the 2nd percentile and ~the 98th percentile, so a few outliers are on the axis, but not a lot of events (or a contour). Next, lower the value for the scale max for each channel until the events at the high end of the scale are just brushing up against the wall of the plot. in a signaling experiment, the Unstim and the positive control high level of stim).
You want to have a couple samples representing the full range of your dataset open (e.g. Next, completely fill the plot area (on all three axes). the y-axis (so that you can see whether each axis is filling up the plot scale). It might help to do this while still looking at a standard 2D contour plot of what you want to show on the z-axis first vs. Then you can edit the min, max, and cofactor. To edit the scales for a channel on Cytobank, open the wedge next to Plot Scales (above where the illustrations print out). To set up your scales just right so that the plot represents the data well and shows a nice contrast. For heat plots, you want to have a lot of cells in each bin or it will not look very nice, so smaller plots tend to work better.
This means that if you have only a few cells, you can make your plot smaller to smooth it out. On Cytobank, the more pixels in the plot, the more bins there are in the x and y dimension. The plot size should be no larger than 128px, and probably 64px.The plot background should be black if you are going to make mountain plots later.This will create a much smoother heat plot than the usual settings for contour plots of 5% or 10%. The percent of cells per contour (Plots: Style => Percent Per Contour) should be ‘2’ %.Some of the things that you will want to double check about your heat plots are: Then you pick what you want to show on each of the three channels, as usual. This will activate the z-axis control as well as the x and y. You can make heat plots using Cytobank if you pick "Contour" as the plot type in the illustration page and choose a Color By of "Channel". However, instead of displaying the number of cells present in each bin across two chosen x and y channels, a heat plot displays the median fluorescence intensity (MFI) of a z channel in each bin across two chosen x and y channels. A ‘heat plot’ on Cytobank is similar to a density plot or a contour plot and uses the same contouring algorithms to represent three dimensions of information known about each cell. This article overviews contour plots colored by channel (heat plots) and making 3D mountain plots out of heat plots. Note, a heat plot is different than a heatmap