The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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.