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.Add the following code to your website.
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