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num_spline_params used in
compare_H1_and_H2() BIC calculation with the spline model
(5 parameters per curve).calculate_distance() now imputes query expression on
the registered time grid with impute_query_exp_value()
(linear interpolation via stats::approxfun()); spline-based
imputation remains available as
impute_query_exp_value_from_spline() for comparison
workflows.get_timepoint_comb_data() gains optional
cross_join_all to support four-quadrant time-point
combinations (reference-reference, query-query, etc.); ref_
/ query_ prefixes are only applied in that mode so the
default path keeps numeric time points.calculate_distance() and
plot.dist_greatR() now handle numeric and label-formatted
time-point tables more robustly (pre-aggregation relabelling removed
from distance calculation; plot-side label stripping is
conditional).dev/scripts/rebuild_brapa_registration_rds.R (plus notes
under dev/) to regenerate
inst/extdata/brapa_arabidopsis_registration.rds after
registration changes.dev/full_pipeline_example.Rmd: runnable full pipeline
(CSV → register() → summaries, curves, distances) for
smoke-testing from a source checkout.here under Suggests for the rebuild
script; updated Config/roxygen2/version for current
{roxygen2}.BIC_diff and
calculate_distance() to match current outputs.optimisation_method in
register() to be “lbfgsb” (LBFSG-B) instead of “nm”
(Nelder-Mead).arabidopsis_SOC1_data.csv and
brapa_SOC1_data.csv extdata.optimise_registration_parameters argument in
register() to use_optimisation.register() to return object of S3 class
res_greatR.calculate_distance() to return object of S3
class dist_greatR.summarise_registration() as
summary.res_greatR() S3 method.time_delta variable in registration
process.fun_args (a list of arguments used when calling
the function) in register() results.summary.res_greatR() to return NA
instead of [NA, NA] when all genes are non-registered.reg_params (table containing distribution of
registration parameters) to results list in
summary.res_greatR() method.calc_overlapping_percent() calculation.overlapping_percent when
applying manual registration.calc_variance() for data with no
replicates to consider expression_value.get_stretch_search_space_limits() and
get_shift_search_space_limits() to exclude unexplorable
regions in search space.calculate_distance() and aux
get_timepoint_comb_*_data() functions to eliminate column
selection and renaming inside lapply() calls, reducing
execution time by up to 25%.type (“registered” or “all”) and
genes_list arguments to calculate_distance()
to filter genes.plot() methods.get_shift_search_space_limits() to adjust shift
space limits accordingly to removal of time_delta variable
(see 48c943cd).overlapping_percent = 0.5 (instead of 50)
in register_manually().get_stretch_search_space_limits() to correctly
determine lower and upper limits when single stretch value is
provided.get_shift_search_space_limits() where
range variables were not available when
calc_mode == "bound".bind_results() auxiliary function to merge results from
register().theme_greatR() function and
greatR_palettes list.transform_input() S3 generic to accept different types
of input in register().plot.res_greatR() S3 method to replace
plot_registration_results().plot.dist_greatR() S3 method to replace
plot_heatmap().plot.summary.res_greatR() S3 method inspired by
WVPlots::ScatterHistC().num_cores parameter to register() to
allow users to run registration in parallel.exp_sd parameter to register() to
allow users to manually set up experimental gene expression
variance.scaling_method parameter in
register() and scale_data() to allow no
scaling (“none”, default), Z-score scaling (“z-score”), and min-max
scaling (“min-max”), and updated unit tests accordingly.register() to perform 3 sequential
registrations when using Nelder-Mead, this improves the results of
optimal stretch and shift parameters.calc_loglik() to use sigma_squared
in every time point in the sum.scaled_data() and
preprocess_data() to return all_data object
only, instead of a list() containing
all_data.compare_H1_and_H2() to return
BIC_diff column (BIC_combined - BIC_separate),
instead of BIC_combined and BIC_separate on
their own.explore_manual_search_space() to use
BIC_diff instead of BIC_combined to calculate
best_params from model_comparison table.register() to perform 3 sequential
registrations when using Nelder-Mead, this improves the results of
optimal stretch and shift parameters. This may be reverted by tweaking
neldermead() parameters to ensure correct convergence.stretch_init and shift_init
to get_search_space_limits(), and updated
optimise() to allow for different space_lims
calculation settings: automatic, given boundary box, and given initial
coords (new).mean_data calculation from
preprocess_data() and argument from
scale_data().register() to
preprocess_data() after running filter_*()
functions.results_list$data is arranged/ordered correctly
in register().get_H*_model_curves() functions to ensure model
curves are smooth.parse_gene_facets() to display
BIC_diff in facet strips.plot_mean_data parameter to
plot_registration_results().overlapping_percent parameter in
register() so it goes from 0 to 100 (it’s later normalised
in the function to avoid breakages down the line).scaling_method as an attribute in
data results from register(), this is used in
plot_registration_results() to build the y-axis label
according the the scaling method used.brapa_arabidopsis_registration.rds file with
new pipeline results.get_search_space_limits() into separate aux
functions for stretch and shift, which allows more stretch and shift
input combinations.validate_params(..., registration_type = "optimisation") to
allow more stretch and shift input combinations.get_timepoint_comb_original_data() and
get_timepoint_comb_registered_data() to perform
cross_join() on a single gene_id at a time
using lapply(), this fixes “Error: vector memory exhausted
(limit reached?)” error.match_names() to do double
setdiff() to ensure name matching is done two ways, and
updated corresponding unit test.filter_incomplete_accession_pairs() to filter out genes
that are missing one accession.calc_variance() to preprocess data variance inside
preprocess_data() instead of
calc_loglik().register_single_gene_*() functions inside
register() to simplify and generalise the pipeline for
parallel registration.calc_loglik() instead of stats::logLik().register()summarise_registration()get_approximate_stretch()plot_registration_results()plot_heatmap()calculate_distance()register() function, and
added scaling_method.register().summarise_registration(),
plot_registration_results(), plot_heatmap(),
calculate_distance() to simply require results
object from register(), vastly simplifying usage.calc_loglik_H1(),
calc_loglik_H2(), calc_overlapping_percent(),
calculate_distance(), cross_join(),
get_search_space_limits_from_params(),
get_search_space_limits(), objective_fun(),
optimise(), plot_heatmap(),
plot_registration_results(),
preprocess_data(), register_manually(),
register(), summary_registration(),
validate_params().match_names() call when validating accession
names in register()aes_string() by parsing
timepoint_var using !!ggplot2::sym()
call.preds left join in
plot_registration_results().plot_registration_results() not working
when all genes are unregistered with
type = "registered".time_delta in
preprocess_data() to ensure it’s grouped by
gene_id and accession (not just
accession).num_shifts and shift_extreme
parameters by simplified shifts parameter.calculate_between_sample_distance() to use
registration_results as primary parameter instead of
mean_df, mean_df_sc, and
imputed_mean_df.optimise_shift_extreme as
maintain_min_num_overlapping_points, properly defined and
corrected the boundary box if number overlapping points whether needed
to be maintained or not.get_approximate_stretch().x_sample
and y_sample columns according in
plot_heatmap().- character in accession names in
plot_heatmap() so that time points are parsed
correctly.optimise_registration_params().preprocess_data() to simplify
scale_and_register_data() code and reuse logic
elsewhere.get_best_stretch_and_shift_simplified().get_BIC_from_registering_data().get_boundary_box().optimise_registration_params_single_gene().optimise_registration_params() as wrapper of
optimise_registration_params_single_gene() for multiple
genes.get_best_stretch_and_shift_after_optimisation().NEWS.md file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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