Consider a synthetically generated dataset. Assume we have two groups of people, one is given diet pills, the other a placebo. Their weights are measured at 4 different days, namely, d_1, d_2, d_3, and d_4. Below is the heatmap corresponding to the pearson correlations between the weights of these groups of people at different days.
This heatmap seems to have a weak resemblance of a checkerboard pattern. Does this suggest that this might be a result of a "batch effect" ? Or how else can this heatmap be interpreted ?
Best Answer
All the correlations are in the range from .89 to .94. Thus instead of trying to explain the minute differences among them you should be emphasizing their homogeneity.
It's not clear that this graphic is capturing any meaningful aspect of your data or findings. If you are particularly interested in describing the trajectory of changes over time, you might find it more revealing to graph the progressions of weights as a set of time series (the term "sparklines" might fit). Or, if you are looking to quantify, for example, mean (day 2 – day 1) differences as opposed to mean differences over other timeframes, you could use repeated measures ANOVA.
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