NEWS.md
try_query() now correctly handles errors that don’t return a response object. We also handle gzip decompression problems better since some databases compressed responses were not handled correctly.calculate_go_enrichment() got additional arguments.
replace_long_name: a logical argument that specifies if GO term names above 50 characters should be replaced by the GO ID instead for the plot. This ensures that the plotting area doesn’t become too small due to the long name. The default is TRUE.label_move_frac: a numeric argument between 0 and 1 that specifies which labels should be moved outside of the bar. The default is 0.2, which means that the labels of all bars that have a size of 20% or less of the largest bar are moved to the right of the bar. This prevents labels from overlapping with the bar boundaries.fetch_alphafold_aligned_error(), fetch_alphafold_prediction(), fetch_mobidb(), fetch_quickgo(), fetch_uniprot() and fetch_uniprot_proteome() got additional arguments:timeout: a numeric value specifying the time in seconds until the download times out.max_tries: a numeric value that specifies the number of times the function tries to download the data in case an error occurs.min_n_peptides parameter to the calculate_protein_abundance() function. This allows users to specify the minimum number of peptides per protein needed for analysis. Default is set at three peptides.fetch_uniprot() previously had an issue where it incorrectly identified certain IDs as UniProt IDs, such as ENSEMBL IDs. For example, it would incorrectly interpret "CON_ENSEMBL:ENSBTAP00000037665" as "P00000". To address this, the function now requires that UniProt IDs are not preceded or followed by letters or digits. This means that UniProt IDs should be recognized only if they stand alone or are separated by non-alphanumeric characters. For instance, in the string "P02545;P20700", both "P02545" and "P20700" are correctly identified as UniProt IDs because they are separated by a semicolon and not attached to any other letters or digits. Fixes issue #245.calculate_go_enrichment() now correctly uses the total number of provided proteins for the contingency table. Previously it falsely only considered proteins with a GO annotation for the enrichment analysis.fetch_uniprot() and fetch_uniprot_proteome() are more resistant to database connection issues. They also give more informative messages as to why the data could not be retrieved. Fixes issue #252.qc_csv() now properly works if the column supplied to the condition argument is a factor. Fixes issue #254.analyse_functional_network() function now includes enhanced error handling to ensure it fails gracefully in case of any issues. Fixes issue #259.version parameter for analyse_functional_network() has been updated to 12.0, aligning with the latest STRINGdb version. Fixes issue #244.calculate_treatment_enrichment() received additional arguments.
fill_colours: a character value that can be used to provide custom colours to the plot.fill_by_group: a logical value that specifies if the bars in the plot should be filled according to group.facet_n_col: specifies the number of columns in the facet plot if a group column was provided.calculate_go_enrichment() got additional arguments.
facet_n_col: determines the number of columns the faceted plot should have if a group column is provided.plot_title: specifies the title of the plot.min_n_detected_proteins_in_process: argument for plotting that specifies the minimum number of proteins a GO term needs to be detected for.enrichment_type: specifies what kind of enrichment should be calculated. It can be “all”, “enrichment” or “deenrichment”. This argument affects how the fisher.test() calculates the enrichment. A two-sided test will be used for “all”, while a one-sided test in the specific direction will be used for “enriched” or “deenriched”.barplot_fill_colour: specifies the colours used to fill the bars in the barplot. Needs always at least two values one for deenriched the other for enriched.plot_style: We added a new plot type to the function. The standard plot is still the default and is called “barplot”, while the new plot type is “heatmap”. The heatmap plot is especially useful for comparing GO enrichments of multiple groups.heatmap_fill_colour: specifies the colours used for the colour gradient of heatmap plots.heatmap_fill_colour_rev: a logical value that specifies if the colour gradient should be reversed.plot_cutoff: is now more flexible. You can provide any number with the “top” cutoff. E.g. “top10”, “top5”.barcode_plot() received additional arguments.
facet_n_col: determines the number of columns the faceted plot.fill_colour_gradient: specifies the colours used for the colour gradient if the colouring column is continous.fill_colour_discrete: specifies the colours used for the fill colours if the colouring column is discrete.mako_colours to the package that contain 256 colours of the “mako” colour gradient.drc_4p_plot() received additional arguments.
facet_title_size: determines the size of the facet titles.export_height: determines the output height of an exported plot in inches.export_width: determines the output width of an exported plot in inches.x_axis_limits: user supplied x-axis limits for each plot.colours: determines colours used for the plot.fit_drc_4p() and parallel_fit_drc_4p() have been updated in the latest version of protti, leading to slight adjustments in their computational results compared to previous versions.
anova_cutoff lets you define the ANOVA adjusted p-value cutoff (default 0.05).n_replicate_completeness replaces replicate_completeness. Now we encourage you to provide a discrete number of minimal replicates instead of a fraction that is multiplied with the total number of replicates. This is particularly important to ensure that thresholds between different datasets and data completeness levels are reproducible.n_condition_completeness replaces condition_completeness. Same as above, we encourage you to provide the minimal number of conditions that need to meet the replicate completeness criteria as a number instead of a fraction.complete_doses is a new optional argument that should be provided if the dataset is small and potentially incomplete. This ensures that no matter if any doses are missing from the provided data or not, the MNAR of the curve is calculated correctly. We would recommend always providing it to ensure proper reproducibility.dose_MNAR column are now part of the hits. To get back to the old output you can just exclude them again from the ranked results.filter argument works. This argument controls if significance statistics should be annotated in the data.
"pre": This previously filtered curves by the completeness as well as the ANOVA adjusted p-value prior to fitting curves. Now it only filters by completeness. This also allows it to be an option for the parallel_fit_drc_4p() function."post": Is still the default value and still just annotates the data without any filtering."pre" to remove usually not trustworthy features with too few complete concentrations from the data before p-value adjustment and curve fittings. This will solidify your confidence that features without a dose-response behavior are true negative. The point is that it is better to not include any features with too few values because they are potentially false negative.normalise() now correctly works with grouped data. Previously it would only correctly work with ungrouped data frames. Now you can group the data to calculate group specific normalisations. If you want to compute a global normalisation for the dataset, you need to ungroup the data before using the function as usual. This fixes issue #209.qc_sequence_coverage() now correctly displays medians in faceted plot. This fixes issue #202 and #213.fit_drc_4p() and parallel_fit_drc_4p() now correctly calculates the ANOVA p-value. Previously the number of observations for each concentration was not provided correctly.fetch_uniprot() now correctly retrieves information if an input ID was also part of a non-conform input ID combination. When e.g. c("P02545", "P02545;P20700") was provided, previously the "P02545" accession was dropped from the input_id column even though it is also present on its own and not only in combination with "P20700". The new output now contains 3 rows, one for each ID, with "P02545" having one row with the input_id `"P02545" and one with the input_id "P02545;P20700". This also means that the input_id column now always contains the provided input IDs and not only if they were non-conform input ID combinations.fit_drc_4p() and parallel_fit_drc_4p() the arguments replicate_completeness and condition_completeness are now deprecated. Please use n_replicate_completeness and n_condition_completeness instead.qc_charge_states(), qc_peptide_type() and qc_missed_cleavages(). Also made appearance more uniform between methods "count" and "intensity".fetch_uniprot() now returns nothing instead of a partial output if some of the requested batches could not be retrieved due to database issues (e.g. timeout because of too many requests). This addresses issue #203, which requests this change, because the warning message regarding the partial output can be easily missed and users might wrongfully assume that all information was retrieved successfully from UniProt.find_peptide() now preserves the groups of the original data. This does not affect any of the calculations.calculate_sequence_coverage() now works on grouped data.correct_lip_for_abundance() was added. It corrects LiP-peptides for changes in protein abundance and calculates their significance using a t-test. The function is based on the MSstatsLiP package developed by the Vitek Lab. Big thanks to @FehrAaron for implementing it!qc_cvs() received a new argument called max_cv that specifies the maximum CV that should be included in the plot.peptide_profile_plot() received a new argument called complete_sample. If set to TRUE, each protein gets assigned all sample names that are found in the input data. This ensures that the plot always contains all samples on the x-axis even if there are no measured intensities for a specific sample. The default is FALSE, which is the original behaviour of the function.volcano_plot() received the colour argument that allows the user to provide custom colours for points.find_peptide() and assign_peptide_type() by only computing on the smallest possible subset of data before joining back to the original data frame.calculate_treatment_enrichment() can now be applied on data frames with multiple different groups. The enrichment will be calculated for each group separately. If the data is plotted, each group is displayed in a separate facet. The group is provided to the new group argument.qc_pca(): If the condition argument is numeric a colour gradient is used instead.volcano_plot() now also works interactively if there are no significant hits.fetch_chebi(): fixed an issue caused by na_if() that changed its behaviour after the recent dplyr update.qc_proteome_coverage(): fixed the label order of fractions of proteins detected and not detected in the proteome. Fixes issue #194.calculate_protein_abundance() now correctly retains columns if for_plot = TRUE. Previously the columns to retain were not joined considering the precursor column, which lead to duplications of information where it did not belong. Fixes issue #197.fetch_kegg() now returns the pathway name correctly again.qc_intensity_distribution(), qc_median_intensities(), qc_charge_states(), qc_contaminants(), qc_missed_cleavages(), qc_peptide_type(), qc_ids(): If the provided sample column is of type factor, the level order won’t be overwritten anymore. *fit_drc_4p(): If there are no correlations an empty data frame is returned to prevent errors in parallel_fit_drc_4p().calculate_sequence_coverage() does not fail anymore if a protein only contains NA peptide sequences.qc_sequence_coverage() does not return a plot anymore if plot = FALSE. This fixes issue #207.qc_data_completeness() if sample was of type factor the function did not properly facet the data when the digestion argument was provided. Now we filter out all 0% completeness values that come from factor levels that are not present in subsetted data.calculate_go_enrichment() can now be applied on data frames with multiple different groups. The enrichment will be calculated for each group separately. If the data is plotted, each group is displayed in a separate facet. The group is provided to the new group argument. The y_axis_free argument determines if the y-axis of the faceted plot is “free” or “fixed”.version argument to fetch_alphafold_prediction() that specifies which verison of the database should be retrieved. The default is currently the newest version "v4".qc_ranked_intensities() was added. It ranks protein, peptide or precursor intensities from highest to lowest. Ranked intensities can also be plotted using the plot argument.fetch_chebi() recieved a timeout argument that specifies after how many seconds the connection to the database should timeout. The default is 60 seconds as previously used.pval_distribution_plot() facets now have the correct style.calculate_protein_abundance() requires at least three distinct peptides for quantification. The function now applies this rule for each sample independently except for checking the whole dataset to contain at least three distinct peptides.fetch_alphafold_aligned_error() was added. It fetches the aligned error matrix for structure predictions from the AlphaFold EBI database.predict_alphafold_domain() was added. It uses a graph-based community clustering algorithm of AlphaFold predicted aligned errors in order to infer protein domains in AlphaFold predictions. The code is based on python code by Tristan Croll.assign_missingness() now correctly deals with unequal replicate numbers of comparisons. In addition there is a message returned if an unequal number of replicates is detected for a comparison.fetch_chebi() fixed a bug that prevented the function from failing gracefully if there is a connection problem to the server.extract_metal_binders() now checks if the provided data frames are NULL. If yes, a message and NULL is returned.fetch_mobidb() was updated after the API changed.fetch_alphafold_aligned_error() and predict_alphafold_domain() functions.iq package to protti. calculate_protein_abundance() now has the method "iq" again as an option.fetch_pdb() now also retrieves information on engineered mutations, non-standard monomers, secondary structure and binding interfaces of ligands.extract_metal_binders() was completely redone. This was in response to the UniProt update and rework of the binding column provided by UniProt. This function extracts and concatenates all metal binding information available for a protein based on the UniProt and QuickGO databases. Therefore, this function now also takes gene ontology (GO) information from QuickGO as input. Instead of being able to provide column names to specific argument of the function you now only provide the data frames. This makes the function less flexible but reduces the amount of arguments required to achieve the same result. You just need to make sure that the input data frames contain columns with the correct names as stated by the function documentation.fetch_quickgo() was added. It fetches gene ontology (GO) information from the QuickGO EBI database. The retrieved information can either be GO annotations for provided UniProt IDs or Taxon identifiers, a list of all GO terms or a “slims” subset of GO IDs that can be generated based on provided GO IDs.fetch_chebi() now has the stars argument with which one can select the evidence levels for which entries should be retrieved.auth_seq_id column that is part of the output of the fetch_pdb() function. Previously, the column could contain duplicated or missing positions. This was formerly identified by comparing the number of positions within the auth_seq_id column and the number of residues in the deposited pdb_sequence. Positions are now correct. The original output can be found in the auth_seq_id_original column.calculate_diff_abundance() function the intensity column can now be retained with the retain_columns argument. This was previously not possible until now since this column was used to reduce the annotation dataset. However, after reassessing the benefit of this filter step, it seemed not necessary.calculate_diff_abundance() that would not duplicate the data. However, this seems not to be the case, which can lead to wrong p-value adjustment. p-value adjustment was originally performed after the columns indicated in retain_columns are joined back to the data. Now p-value adjustment is performed prior to retaining columns as well as only on the subset of data that actually contains p-values. Previously we (by default filter_NA_missingness = TRUE) only filtered out NAs in the missingness column prior to p-value adjustment. However, it is possible that missingness is not NA but the p-value is NA. Now for all methods except for "proDA" we remove NA p-values before p-value adjustment. For "proDA" data is handled as previously since p-values are never NA.fetch_pdb() was changed to 100 (from 200). This was done since more information is retrieved now, which slows to function down and is slightly improved when batch sizes are smaller.try_query() now only retries to retrieve information once if the returned message was “Timeout was reached”. In addition, a timeout and accept argument have been added.fetch_uniprot() and fetch_uniprot_proteome() function accordingly. Please be aware that some columns names might have changed and your code might throw error messages if you did not adjust it accordingly.map_peptides_on_structure() was still using “residue” as its input. We now use find_peptide_in_structure() to generate the correct input column.fetch_uniprot() and fetch_uniprot_proteome(). As UniProt has updated their website and their programmatic access, we now download the information from the legacy version temporarily. A real fix will follow.fetch_kegg(). The function did not retrieve any data after the API URL had changed.fetch_eco() was added. It fetches evidence & conclusion ontology information from the EBI database.qc_proteome_coverage() now has the reviewed argument that specifies if only reviewed entries in UniProt should be considered as the proteome. The default is TRUE and stays the same as previously.volcano_plot() now has the facet_scales argument that specifies if the scales should be “free” or “fixed” when a faceted plot is created. The arguments that can be provided are the same that can be provided to the scales argument of ggplot2::facet_wrap(). The new default is now "fixed".pval_distribution_plot() now has the optional facet_by variable that allows faceting of the plot.map_peptides_on_structure() that caused an error if the column provided to the auth_seq_id argument was called “residue”.volcano_plot() that did not calculate the horizontal cutoff line correctly if there were multiple significance values that have the same adjusted significance value. Now it correctly uses the two p-values closest to the cutoff for the line position calculation. In addition, points were not correctly displayed if no horizontal cutoff line was created due to no significant values. Now all values are displayed correctly.try_query() function now returns errors as a character string if it encounters one. Functions using try_query() however, still expected NULL if there was an error. Also adjusted additional fetch functions that do not use try_query() to fail gracefully and to return informative messages upon encountering errors.calculate_go_enrichment() now has the argument label. If TRUE labels are added to the plot that specify how many proteins from the specific GO term are among the significant hits. This new argument is by default TRUE.version argument in the analyse_functional_network() function.protti depends on and not also packages it suggests.iq package from protti for now since iq is currently not available on CRAN. Once it is available again we will add the functionalities back.fetch_metal_pdb() now gives more informative feedback regarding the reasons resources were not fetched correctly.qc_proteome_coverage that flipped the “Detected” and “Not detected” labels.highlight argument of the woods_plot() function was used huge plots were generated when more than 20 proteins were provided to the function. This is fixed and was due to a wrong variable used in a loop.create_structure_contact_map() now takes an optional data2 argument in which a second data frame of positions and structures can be provided. The positions in this data frame are used for distance calculations relative to the positions provided in the data argument. This helps to reduce the size of the map since only comparisons of interest are made. If a selection provided through the data argument should be compared to the whole structure the data2 argument should not be provided, which is the previous default setting.parallel_create_structure_contact_map() can create structure contact maps using parallel processing. It is also recommended for sequential processing if a large number of contact maps should be created. The non-parallel function should not be used if more than 50 maps should be created at the same time. In order to reduce contact maps to domains or even only peptides, a search pattern is created that selects only relevant regions. This pattern is shared over all maps and would become too large if too maps are created at once.qc_cvs() function now has a grey colour. This ensures that the other colours for each condition match the colours of other quality control plots.run_order argument of qc_sample_correlation() now has a gradient colour scheme that is easier to interpret than distinct colours. The colours are inspired by the “plasma” colour scheme of the viridis package.qc_intensity_distribution() is of type factor instead of character the provided factor levels are used for sample ordering in the plot. This allows for custom sample ordering. If you want this functionality in other functions, please let us know in an issue on GitHub.qc_ids(), which caused an error when the optional condition argument was not provided. Also fixed a bug that did not take the state of the remove_na_intensities argument into account.auth_seq_id variable in structure files was handled as a numeric variable even though it sometimes can be character. This was fixed in all functions using it. These include: fetch_pdb_structure(), fetch_alphafold_prediction() (output is now numeric), fetch_pdb() (provides the auth_seq_id column not as a list vector but as character vector with semicolons as separators), find_peptide_in_structure() (now uses the new auth_seq_id format and returns the character vector of all positions of each peptide as auth_seq_id in additon to auth_seq_id_start and auth_seq_id_end), create_structure_contact_map(), map_peptides_on_structure() (now take the new auth_seq_id column instead of start and end positions as input).map_peptides_on_structure(), which caused an error when file names larger than 256 characters were generated. This was the case if the number of proteins that are part of one structure is very large (e.g. ribosome). If the number of proteins is too large to fit into a normal length file name, they are abbreviated as for example “51_proteins”.fetch_alphafold_prediction() and fetch_pdb_structure() now return more informative messages if individual IDs have not been retrieved correctly (e.g. internet connection problem or outdated ID).calculate_diff_abundance(), comparison names starting with numbers were not supported. This has been fixed. There are no more restrictions for condition names.fetch_pdb() was added. It fetches PDB structure metadata from RCSB.fetch_pdb_structure() was added. It fetches atom level data for a PDB structure from RCSB.fetch_metal_pdb() was added. It fetches information about protein-metal binding sites from the MetalPDB database.find_peptide_in_structure() was added. It returns the positions of a protein region, peptide or amino acid within a protein structure using UniProt positions as input. This is necessary because often amino acid positions in a protein structure vary from their positions in the UniProt protein sequence.map_peptides_on_structure() was added. It can map peptides onto PDB structures based on their positions. This is accomplished by replacing the B-factor information in the structure file with values that allow highlighting peptides when the structure is coloured by B-factor.create_structure_contact_map() was added. It creates a contact map of a subset or of all atom or residue distances in a structure file.calculate_aa_scores() was added. It calculates a score for each amino acid of a protein based on the product of the -log10(adjusted p-value) and the absolute log2 fold change per peptide.woods_plot() recieved the highlight argument, which allows to highlight peptides in the plot with an asterisk based on a logical column. It also got export and export_name arguments which now makes it possible to directly export plots from the function. The new target argument allows the user to specify one or multiple proteins from their data frame for which a plot should be returned, however the default option is to return plots for all proteins in the provided data frame. Now it is also possible to provide more than 20 proteins at a time while the facet argument is TRUE. For every 20 proteins a new plot is created and all of them are returned together in a list.assign_missingness() and calculate_diff_abundance() now also take "all" as input to their ref_condition argument. This will create all pairwise condition pairs. Previously only one reference condition could be provided.volcano_plot() can now plot unadjusted p-values while the horizontal cutoff line is based on the adjusted p-value. the significance_cutoff argument was modified to also take a second value which specifies the adjusted p-value column name. In that case the y-axis of the plot could display p-values that are provided to the significance argument, while the horizontal cutoff line is on the scale of adjusted p-values transformed to the scale of p-values. The provided vector can be e.g. c(0.05, "adj_pval"). In that case the function looks for the closest adjusted p-value above and below 0.05 and takes the mean of the corresponding p-values as the cutoff line. If there is no adjusted p-value in the data that is below 0.05 no line is displayed. This allows the user to display volcano plots using p-values while using adjusted p-values for the cutoff criteria. This is often preferred because adjusted p-values are related to unadjusted p-values often in a complex way that makes them hard to be interpret when plotted.Multiple functions have been renamed. More function follow the convention that they should be verbs. Other functions are more similar to each other in their naming. The old functions still work but they are deprecated and will be removed in the future. Please use the new versions instead.
diff_abundance() has been renamed to calculate_diff_abundance().peptide_type() has been renamed to assign_peptide_type().median_normalisation() has been renamed to normalise(). The normalisation method is now defined through an argument called method.sequence_coverage() has been renamed to calculate_sequence_coverage().network_analysis() has been renamed to analyse_functional_network().treatment_enrichment() has been renamed to calculate_treatment_enrichment().go_enrichment() has been renamed to calculate_go_enrichment().kegg_enrichment() has been renamed to calculate_kegg_enrichment().volcano_protti() has been renamed to volcano_plot().plot_drc_4p() has been renamed to drc_4p_plot().plot_peptide_profiles() has been renamed to peptide_profile_plot().plot_pval_distribution() has been renamed to pval_distribution_plot().calculate_diff_abundance() had a bug that could not correctly deal with condition names containing spaces when a moderated t-test or the proDA algorithm was used. This has been fixed.scale_protti() can now deal with the case that a vector of only equal numbers (e.g. c(1, 1, 1)) is provided. In this case it always scales all values to 1 for method = "01" and to 0 for method = "center".pval_distribution_plot() had a bug centered each bin around values, which meant that the first and last bin were smaller. This has been fixed.header argument was removed from try_query(). It can now be directly supplied to the elipsis (…) of this function depending on which read method is used. For type = "text/tab-separated-values" the argument col_names of the corresponding readr function read_tsv() can be supplied.split_metal_name() removes more non-metal specific words that lead to false identifications. Also many metal names containing a “-” are now considered. A bug was resolved that did not deal with iron-sulfur cluster information correctly due to a wrong variable name.extract_metal_binders() now also correctly identifies monovalent inorganic cations as metals.passed_filter example column that is used in functions afterwards.diff_abundance() function. Previously, the p-values in the whole result table were adjusted together. Now adjustments are made by comparisons. This change only affects analyses that have more than one comparison (two conditions).fetch_uniprot(), fetch_uniprot_proteome() and fetch_kegg(). They now fail gracefully with informative messages if there is no internet connection, the database times out or the data resource has changed. Specifically the underlying try_query() function was changed.