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Programming practices chapter (now on a branch, rather than a fork) #14
Programming practices chapter (now on a branch, rather than a fork) #14
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I'm a little confused by this sentence. Can you explain what you mean here?
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I think what I'm trying to say is that a t-test (or the function to perform one) has a specific input and output, and is very commonly used in a wide variety of applications. So it's probably okay to assume that it's doing what its documentation says it will do, without verifying it yourself or writing your own tests for it, unless you're doing something really non-standard with it, or making a mistake in your own code (like the empty list example).
This would be in contrast to the sequencing pipeline example, where it's (as far as I know) not practical to have a single well-tested and widely used function that goes from raw reads to a volcano plot - there are a lot of very subjective decisions and data transformations that have to happen to get from point A to point B, and they can't be completely encapsulated or abstracted away, which makes it more important to make sure that each step is doing what you expect it to do.
I guess another way to think about this would be the difference between using a programming language as a calculator or a set of steps (which is more or less where I started, and where I think many people probably start), and using it to build more complex software with more extensive computing/testing requirements. I think as someone gravitates toward the latter, the argument for automation starts to make a bit more sense.
Does that make any sense to you? I guess I'll have to think about how to get all that across more concisely - let me know if you have ideas.
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Yes. I think this is a good point. Let's just figure out how we can get this point across more concisely.
It sounds like to me you are saying that as the complexity of an analysis increases, so does the decisions and parameters surrounding it. This means its even more critical to carefully read and consider the documentation of the software you are using to figure out what best fits the goals for your data.
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Could probably make a toy graph illustrating this idea. x axis is complexity of the analysis y is the number of decisions and parameters associated with the analysis.
(This idea would need some polishing).
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Here's a graph I added to the slides (I'll link to it in the part of the text you referenced):
I also toyed with labeling the parts of the graph off the "y = x" line, but I'm not sure if this is helpful or counter-productive: maybe we shouldn't even mention overengineering since it's such an uncommon case in academic software (in my experience at least!)
Let me know what you think.
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Made some minor edits to these slides but overall I love the concept!!! Great visual.
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Can we dive into this point a bit more?
For example, instead of saying it is more beneficial explain why it is. Perhaps a list of reasons (feel free to edit and add to what I've written here -- just giving example thoughts):
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Ok, I tried adding a bit more detail in 9f45371 - let me know if that's closer to what you had in mind.