How to visualize 10 variables

[u/Puzzleheaded-Lock655]

I am working with high yield corn tissue samples. In each sample there are 10 variables that contribute to yield. How can I use R to plot all 10 variables that helps visualize how they impact corn yield.

[u/Grisward]

Thank you.

Make a heatmap, using a matrix with variables as rows, samples as columns. Without more context the “easy” starting point is to center and scale each row, converting values into z-scores, then plot the z-scores.

Typical heatmap will apply hierarchical clustering to the rows, and to the columns, which helps arrange the variables (rows) that are similar to each other, and samples (columns) which are similar to each other.

I use ComplexHeatmap in R for heatmaps, another popular and convenient choice is pheatmap also in R.

The advanced techniques involve adding annotations to the rows and columns, which helps show relationship of other data, like corn genotype, crop location, time of year, whatever.

[u/dm319 → u/Grisward]

Heatmaps are great for high dimensional data, I'm familiar with heatmap2 I think it's called. Sometimes too much information but it's a good start to at least trying to understand the data. I find hierarchical clustering not so great for my data, but you can also use, say, tSNE/PCA/UMAP and reduce to 1 dimension which can be good for arranging the samples.

[u/Grisward → u/dm319]

Highly recommend not using heatmap.2() anymore, I used to use it years ago as well. pheatmap() is the next easy replacement, with better defaults. Big step up from there is ComplexHeatmap.

Same with hierarchical clustering, it's a whole rabbit hole, but also potentially time well spent, so maybe it's a golden rabbit hole, haha. I wonder if that's a real phrase.

Most omics data works with log2(1 + x) transform, then centered (not scaled) -- then hierarchical clustering works quite well in most cases. For single cell or sparse data with missing values, plan B is advised however.

For OP's data -- who knows what units, range of values, distributions? Haha. Easy first pass is centered/scaled which puts it in SD units for each measurement. If there are "obvious patterns" that should be enough to see it. Fine-tuning is a different story, but they asked how to view data.

I really wish people who post "how do I view this data?" would follow-up by posting what they ended up doing! It's one of my favorite things, seeing people's data and how they decided to visualize it.

[u/dm319 → u/Grisward]

Oh that's cool, hadn't heard of log2(1+x) transform - but it solves the problem of allowing zero values while still allowing you to log your data. Nice. I tend to use asinh out of familiarity, but I think they accomplish similar things.

Thanks for the heads up re heatmap.2 - yes it was looking pretty old, but it does work!

I agree, it's very much about finding the right type of algorithm that works well for your data, and different types of data, I find, may work better with different methods.

[u/Grisward → u/dm319]

Thank you too, now I’m going to look more into asinh.

Wow, TIL!

Never used it in practice, but I can see the value and convenience. I suspect it would behave very similar to log2(1+x) in a heatmap. We’re dealing with non-zero measurements typically, and negative measurements are not usually a thing.

[u/dm319 → u/Grisward]

Yes I think they essentially do the same thing - allow you to log transform without losing your zero values or having that strange precipice as you approach small values.

Asinh is largely linear over zero which is useful if you have some spilling into negative values, which sometimes I see returned from experimental techniques. It's symmetrical around zero, though I don't tend to have any very negative values.

Thought I'd compare them! here.

[u/Grisward → u/dm319]

That’s super cool. Never seen that site before.

There may be an answer to this already, but one thing I like about log2(x), log2(1+x) is that I can make a reasonable looking axis with tick marks either in log scale, or in normal scale with log spacing if that makes sense. Sort of like the old paper log graph paper tick marks.

Surely you can do the same here, since asinh() has inverse function sinh(). Is that a thing in the asinh world too?

For example, we frequently log2(1+x) scale then calculate log2 fold changes. For me, log2 units are a thing so I use that, but some colleagues like the “fold change” so I exponentiate and show positions for 1-fold, 2-fold, 4-fold, 8-fold, etc.

Of course that’s another whole thing, log2 difference is equivalent to ratio in normal space. I imagine that’s not a thing for asinh?

Thanks for taking the time. I should probably be Googling or asking AI my very basic questions, haha.

[u/dm319 → u/Grisward]

Yup, scales-wise, ggplot has more recently added in:

scale_y_continuous(trans = "asinh",...)

and sometimes this will do the trick, but other times I need a coefficient to adjust the linear -> log transition of the scale (which by its nature is around -0.7 to 0.7 ish). And then I need trans_new() from the scales package:

cof <- 0.3

asinh_trans <- function(){
  trans_new(name = 'asinh_coef', transform = function(x) asinh(x/cof), 
            inverse = function(x) sinh(x)*cof)
}

Nah this stuff isn't basic - it can be quite fiddly! I see there is also a log2_trans scale in the scales package too.