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Test case for within plate optimization
This example data did not do well using the standard options for the plate scoring function in the multi_plate_wrapper.
samples <- tibble::tribble(
~OriginalSampleID, ~StudyID, ~Diagnosis, ~SeverityGroup, ~Status, ~Gender, ~Age,
1L, "S4", "Disease", "Severe", "high", "Male", 91,
2L, "S4", "Disease", "MildModerate", "normal", "Female", 80,
3L, "S4", "Disease", "MildModerate", "normal", "Male", 82,
4L, "S4", "Disease", "MildModerate", "normal", "Female", 66,
5L, "S4", "Disease", "MildModerate", "normal", "Male", 89,
6L, "S4", "Disease", "Severe", "normal", "Male", 64,
7L, "S4", "Disease", "Severe", "normal", "Male", 61,
8L, "S4", "Disease", "MildModerate", "normal", "Female", 77,
9L, "S4", "Disease", "Severe", "high", "Male", 86,
10L, "S4", "Disease", "MildModerate", "normal", "Male", 65,
11L, "S4", "Disease", "MildModerate", "normal", "Male", 73,
12L, "S4", "Disease", "MildModerate", "normal", "Male", 72,
13L, "S4", "Disease", "MildModerate", "normal", "Male", 76,
14L, "S4", "Disease", "MildModerate", "normal", "Male", 62,
15L, "S4", "Disease", "MildModerate", "normal", "Female", 78,
16L, "S4", "Control", "Control", "normal", "Female", 70,
17L, "S4", "Control", "Control", "normal", "Male", 75,
18L, "S4", "Control", "Control", "normal", "Female", 75,
19L, "S4", "Control", "Control", "normal", "Female", 73,
20L, "S4", "Control", "Control", "normal", "Female", 75,
21L, "S4", "Control", "Control", "normal", "Male", 57,
22L, "S4", "Control", "Control", "normal", "Female", 69,
23L, "S4", "Control", "Control", "normal", "Female", 74,
24L, "S4", "Control", "Control", "normal", "Female", 72,
25L, "S4", "Control", "Control", "normal", "Male", 63,
26L, "S4", "Control", "Control", "normal", "Male", 55,
27L, "S4", "Control", "Control", "normal", "Female", 55,
28L, "S4", "Control", "Control", "normal", "Female", 63,
29L, "S4", "Control", "Control", "normal", "Female", 66,
30L, "S3", "Control", "Control", "high", "Female", 48,
31L, "S3", "Control", "Control", "high", "Male", 63,
32L, "S2", "Control", "Control", "high", "Female", 67,
33L, "S2", "Control", "Control", "high", "Female", 82,
34L, "S2", "Control", "Control", "high", "Female", 81,
35L, "S1", "Control", "Control", "high", "Male", 72,
36L, "S1", "Control", "Control", "high", "Female", 63,
37L, "S2", "Control", "Control", "normal", "Female", 79,
38L, "S2", "Control", "Control", "normal", "Male", 72,
39L, "S2", "Control", "Control", "normal", "Male", 66,
40L, "S2", "Control", "Control", "normal", "Female", 77,
41L, "S2", "Control", "Control", "normal", "Male", 62,
42L, "S2", "Control", "Control", "normal", "Male", 81,
43L, "S2", "Control", "Control", "normal", "Male", 75,
44L, "S3", "Disease", "MildModerate", "normal", "Female", 47,
45L, "S3", "Disease", "MildModerate", "normal", "Male", 55,
46L, "S2", "Disease", "MildModerate", "normal", "Male", 75,
47L, "S2", "Disease", "MildModerate", "normal", "Female", 87,
48L, "S2", "Disease", "MildModerate", "normal", "Female", 53,
49L, "S2", "Disease", "MildModerate", "normal", "Female", 71,
50L, "S2", "Disease", "MildModerate", "normal", "Female", 78,
51L, "S2", "Disease", "MildModerate", "normal", "Female", 76,
52L, "S2", "Disease", "MildModerate", "normal", "Male", 73,
53L, "S2", "Disease", "MildModerate", "normal", "Female", 65,
54L, "S3", "Disease", "MildModerate", "high", "Female", 52,
55L, "S3", "Disease", "MildModerate", "high", "Male", 45,
56L, "S3", "Disease", "MildModerate", "high", "Male", 48,
57L, "S3", "Disease", "MildModerate", "high", "Male", 47,
58L, "S2", "Disease", "MildModerate", "high", "Female", 75,
59L, "S2", "Disease", "MildModerate", "high", "Male", 63,
60L, "S2", "Disease", "MildModerate", "high", "Male", 62,
61L, "S2", "Disease", "MildModerate", "high", "Female", 77,
62L, "S1", "Disease", "MildModerate", "high", "Female", 69,
63L, "S1", "Disease", "MildModerate", "high", "Male", 83,
64L, "S1", "Disease", "MildModerate", "high", "Female", 67,
65L, "S3", "Disease", "Severe", "normal", "Female", 56,
66L, "S3", "Disease", "Severe", "normal", "Male", 54,
67L, "S3", "Disease", "Severe", "normal", "Male", 50,
68L, "S2", "Disease", "Severe", "high", "Female", 74,
69L, "S2", "Disease", "Severe", "normal", "Male", 70,
70L, "S2", "Disease", "Severe", "normal", "Female", 75,
71L, "S2", "Disease", "Severe", "normal", "Male", 78,
72L, "S2", "Disease", "Severe", "normal", "Female", 57,
73L, "S2", "Disease", "Severe", "normal", "Male", 70,
74L, "S2", "Disease", "Severe", "normal", "Male", 72,
75L, "S2", "Disease", "Severe", "normal", "Female", 76,
76L, "S2", "Disease", "Severe", "high", "Male", 76,
77L, "S2", "Disease", "Severe", "high", "Male", 86,
78L, "S2", "Disease", "Severe", "high", "Male", 74,
79L, "S2", "Disease", "Severe", "high", "Male", 59,
80L, "S2", "Disease", "Severe", "high", "Female", 73,
81L, "S3", "Disease", "Severe", "high", "Male", 65,
82L, "S3", "Disease", "Severe", "high", "Female", 66,
83L, "S2", "Disease", "Severe", "high", "Female", 53,
84L, "S2", "Disease", "Severe", "high", "Male", 73,
85L, "S1", "Disease", "Severe", "high", "Female", 57,
86L, "S1", "Disease", "Severe", "high", "Male", 77
)
bc <- BatchContainer$new(
dimensions = list(
"plate" = 1,
"row" = 8,
"column" = 11
)
)
assign_random(bc, samples)
# set scoring function for each balance variable
scoring_funcs <- purrr::map(
params$balance_variables,
~ mk_plate_scoring_functions(bc, row = "row", column = "column", group = .x,
p = 2, penalize_lines = "soft")
) %>% unlist()
names(scoring_funcs) <- params$balance_variables
bc$scoring_f <- scoring_funcs
traces <- optimize_design(
bc,
max_iter = 5000,
quiet = TRUE,
# not actually needed... since here is only one plate
shuffle_proposal_func = mk_subgroup_shuffling_function(
subgroup_vars = "plate",
restrain_on_subgroup_levels = 1
),
acceptance_func = accept_leftmost_improvement
)
Changing the mk_plate_scoring_functions options p = 1, penalize_lines = "none" works a lot better.
optimize_design(scoring_f=..., groupby = c(plate))
Please DO NOT use any confidential data when submitting an issue.
For Roche internal data use code.roche.com.
Please briefly describe your problem and what output you expect.
If you have a question, please don't use this form. Instead, ask in the Discussions.
When possible, please include a minimal reproducible example (AKA a reprex).
If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
Brief description of the problem
# insert reprex hereNamed optimization runs?
Would it be possible to give names to the optimization indices?
In case of the plate wrapper they could be the plate names.
Similarly, if we think about the "optimize_design_by " function, it could just be a column for the "by" variable levels.
It adds another column to the score tables but if they are made factors, maybe that's not too bad?
It could simplify trace plots if not all "scores" were used in all optimization runs:
update installation instructions in readme
replace the current caching approach with cached markdowns
Currently, we have some code to to cache slow parts of vignettes.
This code is ugly and relies on global variables, it also causes some warnings dring R check.
Instead we would like to put slow code into a child markdown document, which can be rendered manually when needed.
After discussing with @banfai we decided to put all the child documents (Rmds and mds) into vignettes/cached.
Replace mk_plate_scoring_functions with plate agnostic scoring functions will not work in current setup
in the current optimize_multi_plate_design function for each plate a plate specific scoring function is generated with mk_plate_scoring_function. For these scoring functions a plate specific distance matrix is pre-calculated that will be used every time the scoring is done. Such pre-calculation would not be possible if we want to use the scoring functions in a 'group_by' manner and apply them on the fly to a given subset (plate) of all samples.
Calculate degrees of freedom of error variance
TODO: expand this
Stan's book, p. 253
returning batch container from optimize_desing()
- make sure bc is readonly
- remove the scoring function from bc
- scoring function goes to optimize_design
Expand generative model vignette with simple optimize design call
After samples are assigned randomly to the BC and plotted,
- Add an optimize design call with Blocking for treatment and
- Check if for this simple case it has an impact
- Plot the result again.
optimize_design(scoring_f = ..., shuffle_by = c(plate))
Please DO NOT use any confidential data when submitting an issue.
For Roche internal data use code.roche.com.
Please briefly describe your problem and what output you expect.
If you have a question, please don't use this form. Instead, ask in the Discussions.
When possible, please include a minimal reproducible example (AKA a reprex).
If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
Brief description of the problem
# insert reprex hereexport accept_strict_improvement?
At the moment accept_strict_improvement() is marked as internal. This means that it's not exported and a user cannot read the documentation.
I propose to exporting it. @ingitwetrust what do you think?
example for manual two-step plate optimization
A colleague had a case which requires shuffling constraints (some plate locations are for controls) and multiple group variables for plate optimization.
We do not have, I believe, and example for this.
This would require manual two-step approach with:
- usage of
shuffle_with_constraints() - manual calling of
mk_plate_scoring_functions()
put a better example in the README file
create a single mock up vignette using the new optimize_design with groupby and shuffleby
Please DO NOT use any confidential data when submitting an issue.
For Roche internal data use code.roche.com.
Please briefly describe your problem and what output you expect.
If you have a question, please don't use this form. Instead, ask in the Discussions.
When possible, please include a minimal reproducible example (AKA a reprex).
If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
Brief description of the problem
# insert reprex here`optimize_multi_plate_design` does not optimize within plate distribution of covariates
optimize_multi_plate_design does not really optimize within plate distribution of covariates. Instead, clusters form.
Replacing the parameters p = 1 and penalize_lines = "none" fixes the issue but these cannot be set in optimize_multi_plate_design
Example
samples <- structure(list(Subject.Number = c(1, 2, 3, 3, 4, 4, 5, 5, 6,
7, 7, 8, 8, 9, 10, 10, 11, 12, 13, 13, 14, 14, 15, 15, 16, 17,
17, 18, 18, 19, 19, 20, 21, 21, 22, 22, 23, 24, 25, 26, 26, 27,
27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
42, 43, 43, 44, 44, 45, 46, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68), Gender = c("Male",
"Male", "Female", "Female", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Female", "Female", "Female", "Female", "Female", "Male", "Male",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Female", "Female", "Male", "Female", "Male", "Male",
"Male", "Female", "Male", "Male", "Male", "Female", "Female",
"Female", "Female", "Male", "Female", "Female", "Male", "Female",
"Male", "Male", "Male", "Female", "Female", "Male", "Male", "Male",
"Female", "Female", "Male", "Male", "Female", "Male", "Male",
"Male", "Male", "Female", "Female", "Female"), Timepoint = c("DAY 1",
"DAY 1", "DAY 1", "DAY 168", "DAY 1", "DAY 168", "DAY 1", "DAY 112",
"DAY 1", "DAY 1", "DAY 112", "DAY 1", "DAY 112", "DAY 1", "DAY 1",
"DAY 112", "DAY 1", "DAY 1", "DAY 1", "DAY 168", "DAY 1", "DAY 112",
"DAY 1", "DAY 112", "DAY 1", "DAY 1", "DAY 112", "DAY 1", "DAY 112",
"DAY 1", "DAY 112", "DAY 1", "DAY 1", "DAY 112", "DAY 1", "DAY 112",
"DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 112", "DAY 1", "DAY 112",
"DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1",
"DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1",
"DAY 1", "DAY 168", "DAY 1", "DAY 112", "DAY 1", "DAY 112", "DAY 1",
"DAY 1", "DAY 112", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1",
"DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1",
"DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1", "DAY 1",
"DAY 1", "DAY 1", "DAY 1"), Site = c("350545", "350545", "350545",
"350545", "350545", "350545", "350545", "350545", "350545", "350545",
"350545", "350545", "350545", "350545", "350545", "350545", "350545",
"350545", "350545", "350545", "350545", "350545", "350545", "350545",
"350545", "350545", "350545", "350545", "350545", "350545", "350545",
"350545", "350545", "350545", "350545", "350545", "350545", "350545",
"350545", "350545", "350545", "350545", "350545", "350545", "350545",
"350545", "350545", "350545", "350545", "350545", "350545", "350545",
"350545", "356312", "356312", "356312", "356312", "356312", "356312",
"356312", "356312", "356312", "356312", "356312", "356312", "356312",
"356312", "356312", "356312", "356312", "356312", "356312", "356312",
"356312", "356312", "356312", "356312", "356312", "356312", "356312",
"356312", "356312", "356312", "356312", "356312", "356312", "356312",
"356312"), Previous_treatment = c("Tx naive", "Pre-treated",
"Tx naive", "Tx naive", "Pre-treated", "Pre-treated", "Pre-treated",
"Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated",
"Pre-treated", "Tx naive", "Pre-treated", "Pre-treated", "Pre-treated",
"Pre-treated", "Tx naive", "Tx naive", "Tx naive", "Tx naive",
"Tx naive", "Tx naive", "Tx naive", "Tx naive", "Tx naive", "Tx naive",
"Tx naive", "Tx naive", "Tx naive", "Tx naive", "Tx naive", "Tx naive",
"Pre-treated", "Pre-treated", "Tx naive", "Tx naive", "Pre-treated",
"Tx naive", "Tx naive", "Tx naive", "Tx naive", "Tx naive", "Tx naive",
"Tx naive", "Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated",
"Pre-treated", "Pre-treated", "Tx naive", "Tx naive", "Pre-treated",
"Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated",
"Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated",
"Pre-treated", "Pre-treated", "Tx naive", "Pre-treated", "Pre-treated",
"Tx naive", "Tx naive", "Tx naive", "Tx naive", "Tx naive", NA,
"Pre-treated", "Pre-treated", "Pre-treated", "Tx naive", "Tx naive",
"Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated", "Pre-treated",
"Pre-treated", "Tx naive")), row.names = c(NA, -88L), class = "data.frame")
# make batch container
bc <- BatchContainer$new(
dimensions = list(
"plate" = 1,
"row" = list(values =c(1:8)),
"column" = list(values = c(1:11))
)
)
# initial assignment
bc <- assign_in_order(bc, samples)
# factors to balance
balance_variables <- c("Site", "Timepoint", "Previous_treatment", "Gender")
# running the wrapper
bc1 <- optimize_multi_plate_design(bc,
within_plate_variables = balance_variables,
plate = "plate",
row = "row",
column = "column",
n_shuffle = 2,
max_iter = 3000,
quiet = TRUE
)
# plot site
print(plot_plate(bc$get_samples(remove_empty_locations = FALSE),
column = column, row = row,
.color = Site)
# running it manually
# set scoring function for each balance variable
scoring_funcs <- purrr::map(
balance_variables,
~ mk_plate_scoring_functions(bc, row = "row", column = "column", group = .x, p = 1, penalize_lines = "none")
) %>% unlist()
names(scoring_funcs) <- balance_variables
bc2 <- optimize_design(
bc,
scoring = scoring_funcs,
max_iter = 3000,
quiet = TRUE,
acceptance_func = accept_leftmost_improvement
)
# plot site
print(plot_plate(bc$get_samples(remove_empty_locations = FALSE),
column = column, row = row,
.color = Site)how to track multiple traces within one batchcontainer
Please DO NOT use any confidential data when submitting an issue.
For Roche internal data use code.roche.com.
Please briefly describe your problem and what output you expect.
If you have a question, please don't use this form. Instead, ask in the Discussions.
When possible, please include a minimal reproducible example (AKA a reprex).
If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
Brief description of the problem
# insert reprex hereFooter looks weird with company included
Line 3 in 55bca85
add this:
authors:
footer:
roles: [cre, aut]
@idavydov @ingitwetrust @julianesiebourg
- do we want to add links to our names? (e.g. github or something else) (❤️)
- do we want to include the Roche logo (like RStudio) (🚀)
Extend constraints
#NCS2022
- constrain
on continuous variablethe order of one variable (e.g. sequential dilution on plate) - constrain on plates (e.g. pipetting sequence, alternating order or blocks, etc)
(these are almost the same problem)
Save design generating information
E.g. formula to generate design matrix for analysis
create issue templates
- public Github issue template, remark about not using any confidential data there, and point to
code.roche.com/PMDA/packages/designit/issuesfor confidential issues - internal code.roche.com, link to public, and a sentence to only put data-related stuff
Remove old randomization.R
Do a quick check if anything in randomization.R is still needed, otherwise remove file
REST API with plumber
Plumber API so the pkg can be used from other tools
TODO populate issue with details
OSAT vignette is not rendered
https://bedapub.github.io/designit/articles/osat.html
This vignette can only be rendered if `OSAT` package is installed
mk_plate_scoring_functions doesn't need batch container as a first argument
Rewrite mk_plate_scoring_functions in a such a way that can create all temporary objects during the first call.
E.g.,
scoring_f <- mk_plate_scoring_functions(
row = "row", column = "column", group = "Sex"
)
bc <- optimize_design(bc, scoring = scoring_f, max_iter = 100)simulation study from David
multiplate wrapper iterate only on current plate scores
line 49 and following
for (curr_plate in plate_levels) {
if (!quiet && length(plate_levels) > 1) cat(curr_plate, "... ")
bc <- optimize_design(bc,
scoring = scoring_funcs, # TODO: Here we should just use the scores of the current plate
max_iter = max_iter,
quiet = TRUE,
shuffle_proposal_func = mk_subgroup_shuffling_function(
subgroup_vars = plate,
restrain_on_subgroup_levels = curr_plate
),
acceptance_func = accept_leftmost_improvement
)
}
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