Hi Lluís,
> So your setter method `[<-` could have this call inside it, and this
> would prevent the creation of an invalid object.
Thank you for solving my issue! I wasn't successful trying something
similar earlier because I was passing no arguments to callNextMethod(),
but retrying your suggestion helped me figure out I need to explicitly
set my arguments to call the setter. My wrapper below first makes a
copy to use for the call to the next appropriate class and only assigns
the result if validObject() does not throw an error:
setMethod(
"[<-",
"CategoriesDataFrame",
function(x, i, j, ..., value) {
y <- x
y <- callNextMethod(y, i, j, ..., value = value)
validObject(y)
x <- y
})
## NB: Above signature per output of:
## selectMethod("[<-", "DFrame")
> Why do you use a new S4 class for parameter input? Your comments
> on what made you try this route would be helpful, thanks!
The whole point of using S4 classes is to have checks to ensure object
validity and if not to fail fast / fail early.
The reason for choosing a DataFrame representation in particular is
there are 19 sets of parameters (Coexpression, ...,ToppCell) each with 6
values (PValue, MinGenes, ..., Enabled) to potentially edit which can
get tedious. Each parameter set shares the same vector (PValue,
MinGenes, ..., Enabled) so using DataFrame semantics is intuitive to a
user to set multiple parameters at once. Especially now with the DFplyr
package, users can use dplyr verbs to fine tune these parameters.
Pariksheet
On 1/4/25 5:51 AM, Lluís Revilla wrote:
[You don't often get email from lluis.revi...@gmail.com. Learn why this is
important at https://aka.ms/LearnAboutSenderIdentification ]
Hi Pariksheet,
You can check if the object is valid inside your methods.
I use something like:
method <- function(...) {
# Input processing
validObject(object)
object
}
So your setter method `[<-` could have this call inside it, and this
would prevent the creation of an invalid object.
In this case, even keeping the class and without adding the validity
check on the [<- method, the validation of the input data could be
done inside the function making the requests to the website.
I am part of a working group trying to make it easier for people to
use classes on Bioconductor.
I would like to write a guide about when to use classes and how to
choose between the different object oriented paradigms existing on R
(including the new S7) and when to use it. Why do you use a new S4
class for parameter input? Your comments on what made you try this
route would be helpful, thanks!
Best,
Lluís
On Fri, 3 Jan 2025 at 01:17, Nanda, Pariksheet via Bioc-devel
<bioc-devel@r-project.org> wrote:
Hello S4-class boffins,
How bad of an idea it is to inherit from a S4Vectors::DataFrame / DFrame S4 class to
impose additional constraints on it? I'm writing a light-weight wrapper around the
ToppGene web API (github com/ImmuSystems-Lab/toppgene/blob/main/R/categories.R) [1] and
while it's functional, it currently only runs JSON web queries using default values. To
pass non-default values, each category queried needs to have associated parameters within
some boundaries. While it's intuitive for a Biconductor user to see and manipulate
DataFrames containing the parameters, the trouble I'm seeing is that validObject() is of
course not automagically run on the dispatched S4Vectors setters and I don't know how to
inject validObject() into the process without rewriting / repeating a lot of the
S4Vectors method implementation internals; callNextMethod() does not seem like it would
work? Currently, the only time validObject() is called is when invoking the constructor,
CategoriesDataFrame() and because code is worth a thousand words, see below the
"---" line for what I mean.
Pariksheet
[1] Yes, I'm trying to avoid the GitHub URL from being mangled into illegible
horrors by removing the protocol prefix and the dot before the domain, so
you'll have to add at least the latter back in to visit the GitHub page.
---
devtools::load_all()
[...]
cats <- CategoriesDataFrame()
cats
ToppGene CategoriesDataFrame with 19 enabled categories
PValue MinGenes MaxGenes MaxResults Correction
Enabled
Coexpression 0.05 2 1500 50 FDR
TRUE
CoexpressionAtlas 0.05 2 1500 50 FDR
TRUE
Computational 0.05 2 1500 50 FDR
TRUE
Cytoband 0.05 2 1500 50 FDR
TRUE
Disease 0.05 2 1500 50 FDR
TRUE
Domain 0.05 2 1500 50 FDR
TRUE
Drug 0.05 2 1500 50 FDR
TRUE
GeneFamily 0.05 2 1500 50 FDR
TRUE
GeneOntologyBiologicalProcess 0.05 2 1500 50 FDR
TRUE
GeneOntologyCellularComponent 0.05 2 1500 50 FDR
TRUE
GeneOntologyMolecularFunction 0.05 2 1500 50 FDR
TRUE
HumanPheno 0.05 2 1500 50 FDR
TRUE
Interaction 0.05 2 1500 50 FDR
TRUE
MicroRNA 0.05 2 1500 50 FDR
TRUE
MousePheno 0.05 2 1500 50 FDR
TRUE
Pathway 0.05 2 1500 50 FDR
TRUE
Pubmed 0.05 2 1500 50 FDR
TRUE
TFBS 0.05 2 1500 50 FDR
TRUE
ToppCell 0.05 2 1500 50 FDR
TRUE
------------------------------
Values allowed by ToppGene are:
PValue: [0, 1] <numeric>
MinGenes: [1, 5000] <integer>
MaxGenes: [2, 5000] <integer>
MaxResults: [1, 5000] <integer>
Correction: {None, FDR, Bonferroni} <character>
## This next line should not complete without an error! But it does.
cats[, "PValue"] <- 2
## Explicitly calling validObject() will point out the problem post-hoc,
## but not prevent the above assignment.
validObject(cats)
Error in validObject(cats) :
invalid class “CategoriesDataFrame” object: column PValue must contain values
<= 1
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