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* Vignette: [SummarizedExperiment for Coordinating Experimental Assays, Samples, and Regions of Interest](https://bioconductor.org/packages/release/bioc/vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html#anatomy-of-a-summarizedexperiment)
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@@ -331,8 +329,8 @@ For instance, the same initial state illustrated above can be achieved using the
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```{r}
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initial <- list(
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ReducedDimensionPlot(),
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FeatureAssayPlot()
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ReducedDimensionPlot(),
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FeatureAssayPlot()
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)
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```
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@@ -479,9 +477,9 @@ Due to the use of `uniquifyFeatureNames()` above, the function must first map to
@@ -541,7 +539,14 @@ In this demo, let us create a new class called `ReducedDimensionHexPlot`, that i
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The objective is to produce a panel that displays the same information as the original `ReducedDimensionPlot()` panel, but summarising data points into hexagonal bins.
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For this, the new class needs an additional slot to store the number of hexagonal bins to create (i.e., a resolution for the plot).
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One major motivation for this functionality is that arbitrarily large data sets comprising many data points may be summarised into a modest number of hexagonal bins, providing a boost in speed when rendering the plot.
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However, this is not without caveats:
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* Summarising can take many forms (e.g., counting, averaging).
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* The ideal number of bins may be different for different data sets.
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To proceed, the new class needs at least one additional slot to store the number of hexagonal bins to create (i.e., a resolution for the plot).
Time for the most exciting and challenging task: making the panel produce a different type of plot!
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To this end, the generic `.generateDotPlot()` is key.
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Given a panel state, axis labels, and an environment that contains the `SummarizedExperiment()` object and possibly other relevant values such as incoming selections,

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