Movatterモバイル変換

Title:	Bottom-Up Proteomics and LiP-MS Quality Control and DataAnalysis Tools
Version:	0.9.1
Description:	Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Imports:	rlang, dplyr, stringr, magrittr, data.table, janitor,progress, purrr, tidyr, ggplot2, forcats, tibble, plotly,ggrepel, utils, grDevices, curl, readr, lifecycle, httr,methods, R.utils, stats
RoxygenNote:	7.3.2
Suggests:	testthat, covr, knitr, rmarkdown, shiny, r3dmol, proDA,limma, dendextend, pheatmap, heatmaply, furrr, future,parallel, seriation, drc, igraph, stringi, STRINGdb, iq,scales, farver, ggforce, xml2, jsonlite
Depends:	R (≥ 4.0)
URL:	https://github.com/jpquast/protti,https://jpquast.github.io/protti/
BugReports:	https://github.com/jpquast/protti/issues
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-10-21 22:14:07 UTC; jan-philippquast
Author:	Jan-Philipp Quast [aut, cre], Dina Schuster [aut], ETH Zurich [cph, fnd]
Maintainer:	Jan-Philipp Quast <quast@imsb.biol.ethz.ch>
Repository:	CRAN
Date/Publication:	2024-10-21 22:40:02 UTC

Analyse protein interaction network for significant hits

Description

The STRING database provides a resource for known and predicted protein-protein interactions.The type of interactions include direct (physical) and indirect (functional) interactions.Through the R packageSTRINGdb this resource if provided to R users. This functionprovides a convenient wrapper forSTRINGdb functions that allow an easy use within theprotti pipeline.

Usage

analyse_functional_network(  data,  protein_id,  string_id,  organism_id,  version = "12.0",  score_threshold = 900,  binds_treatment = NULL,  halo_color = NULL,  plot = TRUE)

Arguments

Value

A network plot displaying interactions of the provided proteins. Ifbinds_treatment was provided halos around the proteins show which proteins interact withthe treatment. Ifplot = FALSE a data frame with interaction information is returned.

Examples

# Create example datadata <- data.frame(  uniprot_id = c(    "P0A7R1",    "P02359",    "P60624",    "P0A7M2",    "P0A7X3",    "P0AGD3"  ),  xref_string = c(    "511145.b4203;",    "511145.b3341;",    "511145.b3309;",    "511145.b3637;",    "511145.b3230;",    "511145.b1656;"  ),  is_known = c(    TRUE,    TRUE,    TRUE,    TRUE,    TRUE,    FALSE  ))# Perform network analysisnetwork <- analyse_functional_network(  data,  protein_id = uniprot_id,  string_id = xref_string,  organism_id = 511145,  binds_treatment = is_known,  plot = TRUE)network

Perform ANOVA

Description

Performs an ANOVA statistical test

Usage

anova_protti(data, grouping, condition, mean_ratio, sd, n)

Arguments

Value

a data frame that contains the within group error (ms_group) and the betweengroup error (ms_error), f statistic and p-values.

Examples

data <- data.frame(  precursor = c("A", "A", "A", "B", "B", "B"),  condition = c("C1", "C2", "C3", "C1", "C2", "C3"),  mean = c(10, 12, 20, 11, 12, 8),  sd = c(2, 1, 1.5, 1, 2, 4),  n = c(4, 4, 4, 4, 4, 4))anova_protti(  data,  grouping = precursor,  condition = condition,  mean = mean,  sd = sd,  n = n)

Assignment of missingness types

Description

The type of missingness (missing at random, missing not at random) is assigned based on thecomparison of a reference condition and every other condition.

Usage

assign_missingness(  data,  sample,  condition,  grouping,  intensity,  ref_condition = "all",  completeness_MAR = 0.7,  completeness_MNAR = 0.2,  retain_columns = NULL)

Arguments

Value

A data frame that contains the reference condition paired with each treatment condition.Thecomparison column contains the comparison name for the specific treatment/referencepair. Themissingness column reports the type of missingness.

• "complete": No missing values for every replicate of this reference/treatment pair forthe specific grouping variable.

• "MNAR": Missing not at random. All replicates of either the reference or treatmentcondition have missing values for the specific grouping variable.

• "MAR": Missing at random. At least n-1 replicates have missing values for thereference/treatment pair for the specific grouping varible.

• NA: The comparison is not complete enough to fall into any other category. It will notbe imputed if imputation is performed. For statistical significance testing these comparisonsare filtered out after the test and prior to p-value adjustment. This can be prevented by settingfilter_NA_missingness = FALSE in thecalculate_diff_abundance() function.

The type of missingness has an influence on the way values are imputeted if imputation isperformed subsequently using theimpute() function. How each type of missingness isspecifically imputed can be found in the function description. The type of missingnessassigned to a comparison does not have any influence on the statistical test in thecalculate_diff_abundance() function.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 10,  frac_change = 0.5,  n_replicates = 4,  n_conditions = 2,  method = "effect_random",  additional_metadata = FALSE)head(data, n = 24)# Assign missingness informationdata_missing <- assign_missingness(  data,  sample = sample,  condition = condition,  grouping = peptide,  intensity = peptide_intensity_missing,  ref_condition = "all",  retain_columns = c(protein))head(data_missing, n = 24)

Assign peptide type

Description

Based on preceding and C-terminal amino acid, the peptide type of a given peptide is assigned.Peptides with preceeding and C-terminal lysine or arginine are considered fully-tryptic. If apeptide is located at the N- or C-terminus of a protein and fulfills the criterium to befully-tryptic otherwise, it is also considered as fully-tryptic. Peptides that only fulfill thecriterium on one terminus are semi-tryptic peptides. Lastly, peptides that are not fulfillingthe criteria for both termini are non-tryptic peptides.

Usage

assign_peptide_type(  data,  aa_before = aa_before,  last_aa = last_aa,  aa_after = aa_after)

Arguments

Value

A data frame that contains the input data and an additional column with the peptidetype information.

Examples

data <- data.frame(  aa_before = c("K", "S", "T"),  last_aa = c("R", "K", "Y"),  aa_after = c("T", "R", "T"))assign_peptide_type(data, aa_before, last_aa, aa_after)

Barcode plot

Description

Plots a "barcode plot" - a vertical line for each identified peptide. Peptides can be colored based on an additional variable. Also differentialabundance can be displayed.

Usage

barcode_plot(  data,  start_position,  end_position,  protein_length,  coverage = NULL,  colouring = NULL,  fill_colour_gradient = protti::mako_colours,  fill_colour_discrete = c("#999999", protti::protti_colours),  protein_id = NULL,  facet = NULL,  facet_n_col = 4,  cutoffs = NULL)

Arguments

Value

A barcode plot is returned.

Examples

data <- data.frame(  start = c(5, 40, 55, 130, 181, 195),  end = c(11, 51, 60, 145, 187, 200),  length = rep(200, 6),  pg_protein_accessions = rep("Protein 1", 6),  diff = c(1, 2, 5, 2, 1, 1),  pval = c(0.1, 0.01, 0.01, 0.2, 0.2, 0.01))barcode_plot(  data,  start_position = start,  end_position = end,  protein_length = length,  facet = pg_protein_accessions,  cutoffs = c(diff = 2, pval = 0.05))

Calculate scores for each amino acid position in a protein sequence

Description

Calculate a score for each amino acid position in a protein sequence based on the product of the-log10(adjusted p-value) and the absolute log2(fold change) per peptide covering this amino acid. In detail, all thepeptides are aligned along the sequence of the corresponding protein, and the average score peramino acid position is computed. In a limited proteolysis coupled to mass spectrometry (LiP-MS)experiment, the score allows to prioritize and narrow down structurally affected regions.

Usage

calculate_aa_scores(  data,  protein,  diff = diff,  adj_pval = adj_pval,  start_position,  end_position,  retain_columns = NULL)

Arguments

Value

A data frame that contains the aggregated scores per amino acid position, enabling todraw fingerprints for each individual protein.

Author(s)

Patrick Stalder

Examples

data <- data.frame(  pg_protein_accessions = c(rep("protein_1", 10)),  diff = c(2, -3, 1, 2, 3, -3, 5, 1, -0.5, 2),  adj_pval = c(0.001, 0.01, 0.2, 0.05, 0.002, 0.5, 0.4, 0.7, 0.001, 0.02),  start = c(1, 3, 5, 10, 15, 25, 28, 30, 41, 51),  end = c(6, 8, 10, 16, 23, 35, 35, 35, 48, 55))calculate_aa_scores(  data,  protein = pg_protein_accessions,  diff = diff,  adj_pval = adj_pval,  start_position = start,  end_position = end)

Calculate differential abundance between conditions

Description

Performs differential abundance calculations and statistical hypothesis tests on data frameswith protein, peptide or precursor data. Different methods for statistical testing are available.

Usage

calculate_diff_abundance(  data,  sample,  condition,  grouping,  intensity_log2,  missingness = missingness,  comparison = comparison,  mean = NULL,  sd = NULL,  n_samples = NULL,  ref_condition = "all",  filter_NA_missingness = TRUE,  method = c("moderated_t-test", "t-test", "t-test_mean_sd", "proDA"),  p_adj_method = "BH",  retain_columns = NULL)

Arguments

Value

A data frame that contains differential abundances (diff), p-values (pval)and adjusted p-values (adj_pval) for each protein, peptide or precursor (depending onthegrouping variable) and the associated treatment/reference pair. Depending on themethod the data frame contains additional columns:

• "t-test": Thestd_error column contains the standard error of the differentialabundances.n_obs contains the number of observations for the specific protein, peptideor precursor (depending on thegrouping variable) and the associated treatment/reference pair.

• "t-test_mean_sd": Columns labeled as control refer to the second condition of thecomparison pairs. Treated refers to the first condition.mean_control andmean_treatedcolumns contain the means for the reference and treatment condition, respectively.sd_controlandsd_treated columns contain the standard deviations for the reference and treatmentcondition, respectively.n_control andn_treated columns contain the numbers ofsamples for the reference and treatment condition, respectively. Thestd_error columncontains the standard error of the differential abundances.t_statistic contains thet_statistic for the t-test.

• "moderated_t-test":CI_2.5 andCI_97.5 contain the 2.5% and 97.5%confidence interval borders for differential abundances.avg_abundance contains averageabundances for treatment/reference pairs (mean of the two group means).t_statisticcontains the t_statistic for the t-test.B The B-statistic is the log-odds that theprotein, peptide or precursor (depending ongrouping) has a differential abundancebetween the two groups. Suppose B=1.5. The odds of differential abundance is exp(1.5)=4.48, i.e,about four and a half to one. The probability that there is a differential abundance is4.48/(1+4.48)=0.82, i.e., the probability is about 82% that this group is differentiallyabundant. A B-statistic of zero corresponds to a 50-50 chance that the group is differentiallyabundant.n_obs contains the number of observations for the specific protein, peptide orprecursor (depending on thegrouping variable) and the associated treatment/reference pair.

• "proDA": Thestd_error column contains the standard error of the differentialabundances.avg_abundance contains average abundances for treatment/reference pairs(mean of the two group means).t_statistic contains the t_statistic for the t-test.n_obs contains the number of observations for the specific protein, peptide or precursor(depending on thegrouping variable) and the associated treatment/reference pair.

For all methods execept"proDA", the p-value adjustment is performed only on theproportion of data that contains a p-value that is notNA. For"proDA" thep-value adjustment is either performed on the complete dataset (filter_NA_missingness = TRUE)or on the subset of the dataset with missingness that is notNA (filter_NA_missingness = FALSE).

Examples

set.seed(123) # Makes example reproducible# Create synthetic datadata <- create_synthetic_data(  n_proteins = 10,  frac_change = 0.5,  n_replicates = 4,  n_conditions = 2,  method = "effect_random",  additional_metadata = FALSE)# Assign missingness informationdata_missing <- assign_missingness(  data,  sample = sample,  condition = condition,  grouping = peptide,  intensity = peptide_intensity_missing,  ref_condition = "all",  retain_columns = c(protein, change_peptide))# Calculate differential abundances# Using "moderated_t-test" and "proDA" improves# true positive recovery progressivelydiff <- calculate_diff_abundance(  data = data_missing,  sample = sample,  condition = condition,  grouping = peptide,  intensity_log2 = peptide_intensity_missing,  missingness = missingness,  comparison = comparison,  method = "t-test",  retain_columns = c(protein, change_peptide))head(diff, n = 10)

Perform gene ontology enrichment analysis

Description

Analyses enrichment of gene ontology terms associated with proteins in the fraction ofsignificant proteins compared to all detected proteins. A two-sided Fisher's exact test isperformed to test significance of enrichment or depletion. GO annotations can be provided tothis function either through UniProtgo_annotations_uniprot, through a table obtainedwithfetch_go in thego_data argument or GO annotations are fetched automaticallyby the function by providingontology_type andorganism_id.

Usage

calculate_go_enrichment(  data,  protein_id,  is_significant,  group = NULL,  y_axis_free = TRUE,  facet_n_col = 2,  go_annotations_uniprot = NULL,  ontology_type,  organism_id = NULL,  go_data = NULL,  plot = TRUE,  plot_style = "barplot",  plot_title = "Gene ontology enrichment of significant proteins",  barplot_fill_colour = c("#56B4E9", "#E76145"),  heatmap_fill_colour = protti::mako_colours,  heatmap_fill_colour_rev = TRUE,  label = TRUE,  enrichment_type = "all",  replace_long_name = TRUE,  label_move_frac = 0.2,  min_n_detected_proteins_in_process = 1,  plot_cutoff = "adj_pval top10")

Arguments

Value

A bar plot or heatmap (depending onplot_style). By default the bar plot displays negative log10adjusted p-values for the top 10 enriched or deenriched gene ontology terms. Alternatively, plot cutoffscan be chosen individually with theplot_cutoff argument. Bars are colored according to the directionof the enrichment (enriched or deenriched). If a heatmap is returned, terms are organised on the y-axis, whilethe colour of each tile represents the negative log10 adjusted p-value (default). If agroup columnis provided the x-axis contains all groups. Ifplot = FALSE, a data frame is returned. P-values are adjusted withBenjamini-Hochberg.

Examples

# Load librarieslibrary(dplyr)library(stringr)# Create example data# Contains artificial de-enrichment for ribosomes.uniprot_go_data <- fetch_uniprot_proteome(  organism_id = 83333,  columns = c(    "accession",    "go_f"  ))if (!is(uniprot_go_data, "character")) {  data <- uniprot_go_data %>%    mutate(significant = c(      rep(TRUE, 1000),      rep(FALSE, n() - 1000)    )) %>%    mutate(significant = ifelse(      str_detect(        go_f,        pattern = "ribosome"      ),      FALSE,      significant    )) %>%    mutate(group = c(      rep("A", 500),      rep("B", 500),      rep("A", (n() - 1000) / 2),      rep("B", round((n() - 1000) / 2))    ))  # Plot gene ontology enrichment  calculate_go_enrichment(    data,    protein_id = accession,    go_annotations_uniprot = go_f,    is_significant = significant,    plot = TRUE,    plot_cutoff = "pval 0.01"  )  # Plot gene ontology enrichment with group  calculate_go_enrichment(    data,    protein_id = accession,    go_annotations_uniprot = go_f,    is_significant = significant,    group = group,    facet_n_col = 1,    plot = TRUE,    plot_cutoff = "pval 0.01"  )  # Plot gene ontology enrichment with group in a heatmap plot  calculate_go_enrichment(    data,    protein_id = accession,    group = group,    go_annotations_uniprot = go_f,    is_significant = significant,    min_n_detected_proteins_in_process = 15,    plot = TRUE,    label = TRUE,    plot_style = "heatmap",    enrichment_type = "enriched",    plot_cutoff = "pval 0.01"  )  # Calculate gene ontology enrichment  go_enrichment <- calculate_go_enrichment(    data,    protein_id = accession,    go_annotations_uniprot = go_f,    is_significant = significant,    plot = FALSE,  )  head(go_enrichment, n = 10)}

Sampling of values for imputation

Description

calculate_imputation is a helper function that is used in theimpute function.Depending on the type of missingness and method, it samples values from a normal distributionthat can be used for the imputation. Note: The input intensities should be log2 transformed.

Usage

calculate_imputation(  min = NULL,  noise = NULL,  mean = NULL,  sd,  missingness = c("MNAR", "MAR"),  method = c("ludovic", "noise"),  skip_log2_transform_error = FALSE)

Arguments

Value

A value sampled from a normal distribution with the input parameters. Method specificsare applied to input parameters prior to sampling.

Perform KEGG pathway enrichment analysis

Description

Analyses enrichment of KEGG pathways associated with proteins in the fraction of significantproteins compared to all detected proteins. A Fisher's exact test is performed to testsignificance of enrichment.

Usage

calculate_kegg_enrichment(  data,  protein_id,  is_significant,  pathway_id = pathway_id,  pathway_name = pathway_name,  plot = TRUE,  plot_cutoff = "adj_pval top10")

Arguments

Value

A bar plot displaying negative log10 adjusted p-values for the top 10 enriched pathways.Bars are coloured according to the direction of the enrichment. Ifplot = FALSE, a dataframe is returned.

Examples

# Load librarieslibrary(dplyr)set.seed(123) # Makes example reproducible# Create example datakegg_data <- fetch_kegg(species = "eco")if (!is.null(kegg_data)) { # only proceed if information was retrieved  data <- kegg_data %>%    group_by(uniprot_id) %>%    mutate(significant = rep(      sample(        x = c(TRUE, FALSE),        size = 1,        replace = TRUE,        prob = c(0.2, 0.8)      ),      n = n()    ))  # Plot KEGG enrichment  calculate_kegg_enrichment(    data,    protein_id = uniprot_id,    is_significant = significant,    pathway_id = pathway_id,    pathway_name = pathway_name,    plot = TRUE,    plot_cutoff = "pval 0.05"  )  # Calculate KEGG enrichment  kegg <- calculate_kegg_enrichment(    data,    protein_id = uniprot_id,    is_significant = significant,    pathway_id = pathway_id,    pathway_name = pathway_name,    plot = FALSE  )  head(kegg, n = 10)}

Label-free protein quantification

Description

Determines relative protein abundances from ion quantification. Only proteins with at leastthree peptides are considered for quantification. The three peptide rule applies for eachsample independently.

Usage

calculate_protein_abundance(  data,  sample,  protein_id,  precursor,  peptide,  intensity_log2,  min_n_peptides = 3,  method = "sum",  for_plot = FALSE,  retain_columns = NULL)

Arguments

Value

Iffor_plot = FALSE, protein abundances are returned, iffor_plot = TRUEalso precursor intensities are returned in a data frame. The later output is ideal for plottingwithpeptide_profile_plot() and can be filtered to only include protein abundances.

Examples

# Create example datadata <- data.frame(  sample = c(    rep("S1", 6),    rep("S2", 6),    rep("S1", 2),    rep("S2", 2)  ),  protein_id = c(    rep("P1", 12),    rep("P2", 4)  ),  precursor = c(    rep(c("A1", "A2", "B1", "B2", "C1", "D1"), 2),    rep(c("E1", "F1"), 2)  ),  peptide = c(    rep(c("A", "A", "B", "B", "C", "D"), 2),    rep(c("E", "F"), 2)  ),  intensity = c(    rnorm(n = 6, mean = 15, sd = 2),    rnorm(n = 6, mean = 21, sd = 1),    rnorm(n = 2, mean = 15, sd = 1),    rnorm(n = 2, mean = 15, sd = 2)  ))data# Calculate protein abundancesprotein_abundance <- calculate_protein_abundance(  data,  sample = sample,  protein_id = protein_id,  precursor = precursor,  peptide = peptide,  intensity_log2 = intensity,  method = "sum",  for_plot = FALSE)protein_abundance# Calculate protein abundances and retain precursor# abundances that can be used in a peptide profile plotcomplete_abundances <- calculate_protein_abundance(  data,  sample = sample,  protein_id = protein_id,  precursor = precursor,  peptide = peptide,  intensity_log2 = intensity,  method = "sum",  for_plot = TRUE)complete_abundances

Protein sequence coverage

Description

Calculate sequence coverage for each identified protein.

Usage

calculate_sequence_coverage(data, protein_sequence, peptides)

Arguments

Value

A new column in thedata data frame containing the calculated sequence coveragefor each identified protein

Examples

data <- data.frame(  protein_sequence = c("abcdefghijklmnop", "abcdefghijklmnop"),  pep_stripped_sequence = c("abc", "jklmn"))calculate_sequence_coverage(  data,  protein_sequence = protein_sequence,  peptides = pep_stripped_sequence)

Check treatment enrichment

Description

Check for an enrichment of proteins interacting with the treatment in significantly changingproteins as compared to all proteins.

Usage

calculate_treatment_enrichment(  data,  protein_id,  is_significant,  binds_treatment,  group = NULL,  treatment_name,  plot = TRUE,  fill_colours = protti::protti_colours,  fill_by_group = FALSE,  facet_n_col = 2)

Arguments

Value

A bar plot displaying the percentage of all detected proteins and all significant proteinsthat bind to the treatment. A Fisher's exact test is performed to calculate the significance ofthe enrichment in significant proteins compared to all proteins. The result is reported as ap-value. Ifplot = FALSE a contingency table in long format is returned.

Examples

# Create example datadata <- data.frame(  protein_id = c(paste0("protein", 1:50)),  significant = c(    rep(TRUE, 20),    rep(FALSE, 30)  ),  binds_treatment = c(    rep(TRUE, 10),    rep(FALSE, 10),    rep(TRUE, 5),    rep(FALSE, 25)  ),  group = c(    rep("A", 5),    rep("B", 15),    rep("A", 15),    rep("B", 15)  ))# Plot treatment enrichmentcalculate_treatment_enrichment(  data,  protein_id = protein_id,  is_significant = significant,  binds_treatment = binds_treatment,  treatment_name = "Rapamycin",  plot = TRUE)# Plot treatment enrichment by groupcalculate_treatment_enrichment(  data,  protein_id = protein_id,  group = group,  is_significant = significant,  binds_treatment = binds_treatment,  treatment_name = "Rapamycin",  plot = TRUE,  fill_by_group = TRUE)# Calculate treatment enrichmentenrichment <- calculate_treatment_enrichment(  data,  protein_id = protein_id,  is_significant = significant,  binds_treatment = binds_treatment,  plot = FALSE)enrichment

Protein abundance correction for LiP-data

Description

Performs the correction of LiP-peptides for changes in protein abundance andcalculates their significance using a t-test. This function was implemented basedon theMSstatsLiPpackage developed by the Vitek lab.

Usage

correct_lip_for_abundance(  lip_data,  trp_data,  protein_id,  grouping,  comparison = comparison,  diff = diff,  n_obs = n_obs,  std_error = std_error,  p_adj_method = "BH",  retain_columns = NULL,  method = c("satterthwaite", "no_df_approximation"))

Arguments

Value

a data frame containing corrected differential abundances (adj_diff, adjustedstandard errors (adj_std_error), degrees of freedom (df), pvalues (pval) andadjusted p-values (adj_pval)

Author(s)

Aaron Fehr

Examples

# Load librarieslibrary(dplyr)# Load example data and simulate tryptic data by summing up precursorsdata <- rapamycin_10uMdata_trp <- data %>%  dplyr::group_by(pg_protein_accessions, r_file_name) %>%  dplyr::mutate(pg_quantity = sum(fg_quantity)) %>%  dplyr::distinct(    r_condition,    r_file_name,    pg_protein_accessions,    pg_quantity  )# Calculate differential abundances for LiP and Trp datadiff_lip <- data %>%  dplyr::mutate(fg_intensity_log2 = log2(fg_quantity)) %>%  assign_missingness(    sample = r_file_name,    condition = r_condition,    intensity = fg_intensity_log2,    grouping = eg_precursor_id,    ref_condition = "control",    retain_columns = "pg_protein_accessions"  ) %>%  calculate_diff_abundance(    sample = r_file_name,    condition = r_condition,    grouping = eg_precursor_id,    intensity_log2 = fg_intensity_log2,    comparison = comparison,    method = "t-test",    retain_columns = "pg_protein_accessions"  )diff_trp <- data_trp %>%  dplyr::mutate(pg_intensity_log2 = log2(pg_quantity)) %>%  assign_missingness(    sample = r_file_name,    condition = r_condition,    intensity = pg_intensity_log2,    grouping = pg_protein_accessions,    ref_condition = "control"  ) %>%  calculate_diff_abundance(    sample = r_file_name,    condition = r_condition,    grouping = pg_protein_accessions,    intensity_log2 = pg_intensity_log2,    comparison = comparison,    method = "t-test"  )# Correct for abundance changescorrected <- correct_lip_for_abundance(  lip_data = diff_lip,  trp_data = diff_trp,  protein_id = pg_protein_accessions,  grouping = eg_precursor_id,  retain_columns = c("missingness"),  method = "satterthwaite")head(corrected, n = 10)

Creates a mass spectrometer queue for Xcalibur

Description

This function creates a measurement queue for sample acquisition for the software Xcalibur.All possible combinations of the provided information will be created to make file andsample names.

Usage

create_queue(  date = NULL,  instrument = NULL,  user = NULL,  measurement_type = NULL,  experiment_name = NULL,  digestion = NULL,  treatment_type_1 = NULL,  treatment_type_2 = NULL,  treatment_dose_1 = NULL,  treatment_dose_2 = NULL,  treatment_unit_1 = NULL,  treatment_unit_2 = NULL,  n_replicates = NULL,  number_runs = FALSE,  organism = NULL,  exclude_combinations = NULL,  inj_vol = NA,  data_path = NA,  method_path = NA,  position_row = NA,  position_column = NA,  blank_every_n = NULL,  blank_position = NA,  blank_method_path = NA,  blank_inj_vol = 1,  export = FALSE,  export_to_queue = FALSE,  queue_path = NULL)

Arguments

Value

Ifexport_to_queue = FALSE a file namedqueue.csv will be returned thatcontains the generated queue. Ifexport_to_queue = TRUE, the resulting generated queuewill be appended to an already existing queue that needs to be specified either interactivelyor through the argumentqueue_path.

Examples

create_queue(  date = c("200722"),  instrument = c("EX1"),  user = c("jquast"),  measurement_type = c("DIA"),  experiment_name = c("JPQ031"),  digestion = c("LiP", "tryptic control"),  treatment_type_1 = c("EDTA", "H2O"),  treatment_type_2 = c("Zeba", "unfiltered"),  treatment_dose_1 = c(10, 30, 60),  treatment_unit_1 = c("min"),  n_replicates = 4,  number_runs = FALSE,  organism = c("E. coli"),  exclude_combinations = list(list(    treatment_type_1 = c("H2O"),    treatment_type_2 = c("Zeba", "unfiltered"),    treatment_dose_1 = c(10, 30)  )),  inj_vol = c(2),  data_path = "D:\\2007_Data",  method_path = "C:\\Xcalibur\\methods\\DIA_120min",  position_row = c("A", "B", "C", "D", "E", "F"),  position_column = 8,  blank_every_n = 4,  blank_position = "1-V1",  blank_method_path = "C:\\Xcalibur\\methods\\blank")

Creates a contact map of all atoms from a structure file

Description

Creates a contact map of a subset or of all atom or residue distances in a structure orAlphaFold prediction file. Contact maps are a useful tool for the identification of proteinregions that are in close proximity in the folded protein. Additionally, regions that areinteracting closely with a small molecule or metal ion can be easily identified without theneed to open the structure in programs such as PyMOL or ChimeraX. For large datasets (morethan 40 contact maps) it is recommended to use theparallel_create_structure_contact_map()function instead, regardless of if maps should be created in parallel or sequential.

Usage

create_structure_contact_map(  data,  data2 = NULL,  id,  chain = NULL,  auth_seq_id = NULL,  distance_cutoff = 10,  pdb_model_number_selection = c(0, 1),  return_min_residue_distance = TRUE,  show_progress = TRUE,  export = FALSE,  export_location = NULL,  structure_file = NULL)

Arguments

Value

A list of contact maps for each PDB or UniProt ID provided in the input is returned.If theexport argument is TRUE, each contact map will be saved as a ".csv" file in thecurrent working directory or the location provided to theexport_location argument.

Examples

# Create example datadata <- data.frame(  pdb_id = c("6NPF", "1C14", "3NIR"),  chain = c("A", "A", NA),  auth_seq_id = c("1;2;3;4;5;6;7", NA, NA))# Create contact mapcontact_maps <- create_structure_contact_map(  data = data,  id = pdb_id,  chain = chain,  auth_seq_id = auth_seq_id,  return_min_residue_distance = TRUE)str(contact_maps[["3NIR"]])contact_maps

Creates a synthetic limited proteolysis proteomics dataset

Description

This function creates a synthetic limited proteolysis proteomics dataset that can be used totest functions while knowing the ground truth.

Usage

create_synthetic_data(  n_proteins,  frac_change,  n_replicates,  n_conditions,  method = "effect_random",  concentrations = NULL,  median_offset_sd = 0.05,  mean_protein_intensity = 16.88,  sd_protein_intensity = 1.4,  mean_n_peptides = 12.75,  size_n_peptides = 0.9,  mean_sd_peptides = 1.7,  sd_sd_peptides = 0.75,  mean_log_replicates = -2.2,  sd_log_replicates = 1.05,  effect_sd = 2,  dropout_curve_inflection = 14,  dropout_curve_sd = -1.2,  additional_metadata = TRUE)

Arguments

Value

A data frame that contains complete peptide intensities and peptide intensities withvalues that were created based on a probabilistic dropout curve.

Examples

create_synthetic_data(  n_proteins = 10,  frac_change = 0.1,  n_replicates = 3,  n_conditions = 2)# determination of mean_n_peptides and size_n_peptides parameters based on real data (count)# example peptide count per proteincount <- c(6, 3, 2, 0, 1, 0, 1, 2, 2, 0)theta <- c(mu = 1, k = 1)negbinom <- function(theta) {  -sum(stats::dnbinom(count, mu = theta[1], size = theta[2], log = TRUE))}fit <- stats::optim(theta, negbinom)fit# determination of mean_log_replicates and sd_log_replicates parameters# based on real data (standard_deviations)# example standard deviations of replicatesstandard_deviations <- c(0.61, 0.54, 0.2, 1.2, 0.8, 0.3, 0.2, 0.6)theta2 <- c(meanlog = 1, sdlog = 1)lognorm <- function(theta2) {  -sum(stats::dlnorm(standard_deviations, meanlog = theta2[1], sdlog = theta2[2], log = TRUE))}fit2 <- stats::optim(theta2, lognorm)fit2

Calculate differential abundance between conditions

Description

This function was deprecated due to its name changing tocalculate_diff_abundance().

Usage

diff_abundance(...)

Value

A data frame that contains differential abundances (diff), p-values (pval)and adjusted p-values (adj_pval) for each protein, peptide or precursor (depending onthegrouping variable) and the associated treatment/reference pair. Depending on themethod the data frame contains additional columns:

• "t-test": Thestd_error column contains the standard error of the differentialabundances.n_obs contains the number of observations for the specific protein, peptideor precursor (depending on thegrouping variable) and the associated treatment/reference pair.

• "t-test_mean_sd": Columns labeled as control refer to the second condition of thecomparison pairs. Treated refers to the first condition.mean_control andmean_treatedcolumns contain the means for the reference and treatment condition, respectively.sd_controlandsd_treated columns contain the standard deviations for the reference and treatmentcondition, respectively.n_control andn_treated columns contain the numbers ofsamples for the reference and treatment condition, respectively. Thestd_error columncontains the standard error of the differential abundances.t_statistic contains thet_statistic for the t-test.

• "moderated_t-test":CI_2.5 andCI_97.5 contain the 2.5% and 97.5%confidence interval borders for differential abundances.avg_abundance contains averageabundances for treatment/reference pairs (mean of the two group means).t_statisticcontains the t_statistic for the t-test.B The B-statistic is the log-odds that theprotein, peptide or precursor (depending ongrouping) has a differential abundancebetween the two groups. Suppose B=1.5. The odds of differential abundance is exp(1.5)=4.48, i.e,about four and a half to one. The probability that there is a differential abundance is4.48/(1+4.48)=0.82, i.e., the probability is about 82% that this group is differentiallyabundant. A B-statistic of zero corresponds to a 50-50 chance that the group is differentiallyabundant.n_obs contains the number of observations for the specific protein, peptide orprecursor (depending on thegrouping variable) and the associated treatment/reference pair.

• "proDA": Thestd_error column contains the standard error of the differentialabundances.avg_abundance contains average abundances for treatment/reference pairs(mean of the two group means).t_statistic contains the t_statistic for the t-test.n_obs contains the number of observations for the specific protein, peptide or precursor(depending on thegrouping variable) and the associated treatment/reference pair.

Dose response curve helper function

Description

This function peforms the four-parameter dose response curve fit. It is the helper functionfor the fit in thefit_drc_4p function.

Usage

drc_4p(data, response, dose, log_logarithmic = TRUE, pb = NULL)

Arguments

Value

An object of classdrc. If no fit was performed a character vector with content"no_fit".

Plotting of four-parameter dose response curves

Description

Function for plotting four-parameter dose response curves for each group (precursor, peptide orprotein), based on output fromfit_drc_4p function.

Usage

drc_4p_plot(  data,  grouping,  response,  dose,  targets,  unit = "uM",  y_axis_name = "Response",  facet_title_size = 15,  facet = TRUE,  scales = "free",  x_axis_scale_log10 = TRUE,  x_axis_limits = c(NA, NA),  colours = NULL,  export = FALSE,  export_height = 25,  export_width = 30,  export_name = "dose-response_curves")

Arguments

Value

Iftargets = "all" a list containing plots for every unique identifier in thegrouping variable is created. Otherwise a plot for the specified targets is created withmaximally 20 facets.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 2,  frac_change = 1,  n_replicates = 3,  n_conditions = 8,  method = "dose_response",  concentrations = c(0, 1, 10, 50, 100, 500, 1000, 5000),  additional_metadata = FALSE)# Perform dose response curve fitdrc_fit <- fit_drc_4p(  data = data,  sample = sample,  grouping = peptide,  response = peptide_intensity_missing,  dose = concentration,  retain_columns = c(protein))str(drc_fit)# Plot dose response curvesif (!is.null(drc_fit)) {  drc_4p_plot(    data = drc_fit,    grouping = peptide,    response = peptide_intensity_missing,    dose = concentration,    targets = c("peptide_2_1", "peptide_2_3"),    unit = "pM"  )}

Extract metal-binding protein information from UniProt

Description

Information of metal binding proteins is extracted from UniProt data retrieved withfetch_uniprot as well as QuickGO data retrieved withfetch_quickgo.

Usage

extract_metal_binders(  data_uniprot,  data_quickgo,  data_chebi = NULL,  data_chebi_relation = NULL,  data_eco = NULL,  data_eco_relation = NULL,  show_progress = TRUE)

Arguments

Value

A data frame containing information on protein metal binding state. It contains thefollowing columns:

• accession: UniProt protein identifier.

• most_specific_id: ChEBI ID that is most specific for the position after combining information from all sources.Can be multiple IDs separated by "," if a position appears multiple times due to multiple fitting IDs.

• most_specific_id_name: The name of the ID in themost_specific_id column. This information is based onChEBI.

• ligand_identifier: A ligand identifier that is unique per ligand per protein. It consists of the ligand ID andligand name. The ligand ID counts the number of ligands of the same type per protein.

• ligand_position: The amino acid position of the residue interacting with the ligand.

• binding_mode: Contains information about the way the amino acid residue interacts with the ligand. If it is"covalent" then the residue is not in contact with the metal directly but only the cofactor that binds the metal.

• metal_function: Contains information about the function of the metal. E.g. "catalytic".

• metal_id_part: Contains a ChEBI ID that identifiers the metal part of the ligand. This is always the metal atom.

• metal_id_part_name: The name of the ID in themetal_id_part column. This information is based onChEBI.

• note: Contains notes associated with information based on cofactors.

• chebi_id: Contains the original ChEBI IDs the information is based on.

• source: Contains the sources of the information. This can consist of "binding", "cofactor", "catalytic_activity"and "go_term".

• eco: If there is evidence the annotation is based on it is annotated with an ECO ID, which is split by source.

• eco_type: The ECO identifier can fall into the "manual_assertion" group for manually curated annotations or the"automatic_assertion" group for automatically generated annotations. If there is no evidence it is annotated as"automatic_assertion". The information is split by source.

• evidence_source: The original sources (e.g. literature, PDB) of evidence annotations split by source.

• reaction: Contains information about the chemical reaction catalysed by the protein that involves the metal.Can contain the EC ID, Rhea ID, direction specific Rhea ID, direction of the reaction and evidence for the direction.

• go_term: Contains gene ontology terms if there are any metal related ones associated with the annotation.

• go_name: Contains gene ontology names if there are any metal related ones associated with the annotation.

• assigned_by: Contains information about the source of the gene ontology term assignment.

• database: Contains information about the source of the ChEBI annotation associated with gene ontology terms.

For each protein identifier the data frame contains information on the bound ligand as well as on its position if it is known.Since information about metal ligands can come from multiple sources, additional information (e.g. evidence) is nested in the returneddata frame. In order to unnest the relevant information the following steps have to be taken: It ispossible that there are multiple IDs in the "most_specific_id" column. This means that one position cannot be uniquelyattributed to one specific ligand even with the same ligand_identifier. Apart from the "most_specific_id" column, inwhich those instances are separated by ",", in other columns the relevant information is separated by "||". Theninformation should be split based on the source (not thesource column, that one can be removed from the dataframe). There are certain columns associated with specific sources (e.g.go_term is associatedwith the"go_term" source). Values of columns not relevant for a certain source should be replaced withNA.Since amost_specific_id can have multiplechebi_ids associated with it we need to unnest thechebi_idcolumn and associated columns in which information is separated by "|". Afterwards evidence and additional information can beunnested by first splitting data for ";;" and then for ";".

Examples

# Create example datauniprot_ids <- c("P00393", "P06129", "A0A0C5Q309", "A0A0C9VD04")## UniProt datadata_uniprot <- fetch_uniprot(  uniprot_ids = uniprot_ids,  columns = c(    "ft_binding",    "cc_cofactor",    "cc_catalytic_activity"  ))## QuickGO datadata_quickgo <- fetch_quickgo(  id_annotations = uniprot_ids,  ontology_annotations = "molecular_function")## ChEBI data (2 and 3 star entries)data_chebi <- fetch_chebi(stars = c(2, 3))data_chebi_relation <- fetch_chebi(relation = TRUE)## ECO dataeco <- fetch_eco()eco_relation <- fetch_eco(return_relation = TRUE)# Extract metal binding informationmetal_info <- extract_metal_binders(  data_uniprot = data_uniprot,  data_quickgo = data_quickgo,  data_chebi = data_chebi,  data_chebi_relation = data_chebi_relation,  data_eco = eco,  data_eco_relation = eco_relation)metal_info

Fetch AlphaFold aligned error

Description

Fetches the aligned error for AlphaFold predictions for provided proteins.The aligned error is useful for assessing inter-domain accuracy. In detail itrepresents the expected position error at residue x (scored residue), whenthe predicted and true structures are aligned on residue y (aligned residue).

Usage

fetch_alphafold_aligned_error(  uniprot_ids = NULL,  error_cutoff = 20,  timeout = 30,  max_tries = 1,  return_data_frame = FALSE,  show_progress = TRUE)

Arguments

Value

A list that contains aligned errors for AlphaFold predictions. If return_data_frame isTRUE, a data frame with this information is returned instead. The data frame contains thefollowing columns:

• scored_residue: The error for this position is calculated based on the alignment to thealigned residue.

• aligned_residue: The residue that is aligned for the calculation of the error of the scoredresidue

• error: The predicted aligned error computed by alpha fold.

• accession: The UniProt protein identifier.

Examples

aligned_error <- fetch_alphafold_aligned_error(  uniprot_ids = c("F4HVG8", "O15552"),  error_cutoff = 5,  return_data_frame = TRUE)head(aligned_error, n = 10)

Fetch AlphaFold prediction

Description

Fetches atom level data for AlphaFold predictions either for selected proteins or wholeorganisms.

Usage

fetch_alphafold_prediction(  uniprot_ids = NULL,  organism_name = NULL,  version = "v4",  timeout = 3600,  max_tries = 5,  return_data_frame = FALSE,  show_progress = TRUE)

Arguments

Value

A list that contains atom level data for AlphaFold predictions. If return_data_frame isTRUE, a data frame with this information is returned instead. The data frame contains thefollowing columns:

• label_id: Uniquely identifies every atom in the prediction following the standardisedconvention for mmCIF files.

• type_symbol: The code used to identify the atom species representing this atom type.This code is the element symbol.

• label_atom_id: Uniquely identifies every atom for the given residue following thestandardised convention for mmCIF files.

• label_comp_id: A chemical identifier for the residue. This is the three- letter codefor the amino acid.

• label_asym_id: Chain identifier following the standardised convention for mmCIF files.Since every prediction only contains one protein this is always "A".

• label_seq_id: Uniquely and sequentially identifies residues for each protein. Thenumbering corresponds to the UniProt amino acid positions.

• x: The x coordinate of the atom.

• y: The y coordinate of the atom.

• z: The z coordinate of the atom.

• prediction_score: Contains the prediction score for each residue.

• auth_seq_id: Same aslabel_seq_id. But of type character.

• auth_comp_id: Same aslabel_comp_id.

• auth_asym_id: Same aslabel_asym_id.

• uniprot_id: The UniProt identifier of the predicted protein.

• score_quality: Score annotations.

Examples

alphafold <- fetch_alphafold_prediction(  uniprot_ids = c("F4HVG8", "O15552"),  return_data_frame = TRUE)head(alphafold, n = 10)

Fetch ChEBI database information

Description

Fetches information from the ChEBI database.

Usage

fetch_chebi(relation = FALSE, stars = c(3), timeout = 60)

Arguments

Value

A data frame that contains information about each molecule in the ChEBI database.

Examples

chebi <- fetch_chebi()head(chebi)

Fetch evidence & conclusion ontology

Description

Fetches all evidence & conclusion ontology (ECO) information from the QuickGO EBI database. The ECO project ismaintained through a publicGitHub repository.

Usage

fetch_eco(  return_relation = FALSE,  return_history = FALSE,  show_progress = TRUE)

Arguments

Details

According to the GitHub repository ECO is defined as follows:

"The Evidence & Conclusion Ontology (ECO) describes types of scientific evidence within thebiological research domain that arise from laboratory experiments, computational methods,literature curation, or other means. Researchers use evidence to support conclusionsthat arise out of scientific research. Documenting evidence during scientific researchis essential, because evidence gives us a sense of why we believe what we think we know.Conclusions are asserted as statements about things that are believed to be true, forexample that a protein has a particular function (i.e. a protein functional annotation) orthat a disease is associated with a particular gene variant (i.e. a phenotype-gene association).A systematic and structured (i.e. ontological) classification of evidence allows us to store,retreive, share, and compare data associated with that evidence using computers, which areessential to navigating the ever-growing (in size and complexity) corpus of scientificinformation."

More information can be found in their publication (doi:10.1093/nar/gky1036).

Value

A data frame that contains descriptive information about each ECO term in the EBI database.If eitherreturn_relation orreturn_history is set toTRUE, the respective information isreturned instead of the usual output.

Examples

eco <- fetch_eco()head(eco)

Fetch gene ontology information from geneontology.org

Description

Fetches gene ontology data from geneontology.org for the provided organism ID.

Usage

fetch_go(organism_id)

Arguments

Value

A data frame that contains gene ontology mappings to UniProt or SGD IDs. The originalfile is a .GAF file. A detailed description of all columns can be found here:http://geneontology.org/docs/go-annotation-file-gaf-format-2.1/

Examples

go <- fetch_go("9606")head(go)

Fetch KEGG pathway data from KEGG

Description

Fetches gene IDs and corresponding pathway IDs and names for the provided organism.

Usage

fetch_kegg(species)

Arguments

Value

A data frame that contains gene IDs with corresponding pathway IDs and names for aselected organism.

Examples

kegg <- fetch_kegg(species = "hsa")head(kegg)

Fetch structural information about protein-metal binding from MetalPDB

Description

Fetches information about protein-metal binding sites from theMetalPDB database. A complete list of different possible searchqueries can be found on their website.

Usage

fetch_metal_pdb(  id_type = "uniprot",  id_value,  site_type = NULL,  pfam = NULL,  cath = NULL,  scop = NULL,  representative = NULL,  metal = NULL,  ligands = NULL,  geometry = NULL,  coordination = NULL,  donors = NULL,  columns = NULL,  show_progress = TRUE)

Arguments

Value

A data frame that contains information about protein-metal binding sites. The dataframe contains some columns that might not be self explanatory.

• auth_id_metal: Unique structure atom identifier of the metal, which is provided bythe author of the structure in order to match the identification used in the publicationthat describes the structure.

• auth_seq_id_metal: Residue identifier of the metal, which is provided by the author ofthe structure in order to match the identification used in the publication that describes thestructure.

• pattern: Metal pattern for each metal bound by the structure.

• is_representative: A representative site is a site selected to represent a cluster ofequivalent sites. The selection is done by choosing the PDB structure with the best X-rayresolution among those containing the sites in the cluster. NMR structures are generallydiscarded in favor of X-ray structures, unless all the sites in the cluster are found in NMRstructures.

• auth_asym_id_ligand: Chain identifier of the metal-coordinating ligand residues, whichis provided by the author of the structure in order to match the identification used in thepublication that describes the structure.

• auth_seq_id_ligand: Residue identifier of the metal-coordinating ligand residues, whichis provided by the author of the structure in order to match the identification used in thepublication that describes the structure.

• auth_id_ligand: Unique structure atom identifier of the metal-coordinating ligand residues, which is provided by the author of the structure in order to match the identificationused in the publication that describes the structure.

• auth_atom_id_ligand: Unique residue specific atom identifier of the metal-coordinatingligand residues, which is provided by the author of the structure in order to match theidentification used in the publication that describes the structure.

Examples

head(fetch_metal_pdb(id_value = c("P42345", "P00918")))fetch_metal_pdb(id_type = "pdb", id_value = c("1g54"), metal = "Zn")

Fetch protein disorder and mobility information from MobiDB

Description

Fetches information about disordered and flexible protein regions from MobiDB.

Usage

fetch_mobidb(  uniprot_ids = NULL,  organism_id = NULL,  show_progress = TRUE,  timeout = 60,  max_tries = 2)

Arguments

Value

A data frame that contains start and end positions for disordered and flexible proteinregions. Thefeature column contains information on the source of thisannotation. More information on the source can be foundhere.

Examples

fetch_mobidb(  uniprot_ids = c("P0A799", "P62707"))

Fetch structure information from RCSB

Description

Fetches structure metadata from RCSB. If you want to retrieve atom data such as positions, usethe functionfetch_pdb_structure().

Usage

fetch_pdb(pdb_ids, batchsize = 100, show_progress = TRUE)

Arguments

Value

A data frame that contains structure metadata for the PDB IDs provided. The data framecontains some columns that might not be self explanatory.

• auth_asym_id: Chain identifier provided by the author of the structure in order tomatch the identification used in the publication that describes the structure.

• label_asym_id: Chain identifier following the standardised convention for mmCIF files.

• entity_beg_seq_id, ref_beg_seq_id, length, pdb_sequence:entity_beg_seq_id is aposition in the structure sequence (pdb_sequence) that matches the position given inref_beg_seq_id, which is a position within the protein sequence (not included in thedata frame).length identifies the stretch of sequence for which positions matchaccordingly between structure and protein sequence.entity_beg_seq_id is a residue IDbased on the standardised convention for mmCIF files.

• auth_seq_id: Residue identifier provided by the author of the structure in order tomatch the identification used in the publication that describes the structure. This charactervector has the same length as thepdb_sequence and each position is the identifier forthe matching amino acid position inpdb_sequence. The contained values are notnecessarily numbers and the values do not have to be positive.

• modified_monomer: Is composed of first the composition ID of the modification, followedby thelabel_seq_id position. In parenthesis are the parent monomer identifiers asthey appear in the sequence.

• ligand_*: Any column starting with theligand_* prefix contains information aboutthe position, identity and donors for ligand binding sites. If there are multiple entities ofligands they are separated by "|". Specific donor level information is separated by ";".

• secondar_structure: Contains information about helix and sheet secondary structure elements.Individual regions are separated by ";".

• unmodeled_structure: Contains information about unmodeled or partially modeled regions inthe model. Individual regions are separated by ";".

• auth_seq_id_original: In some cases the sequence positions do not match the number of residuesin the sequence either because positions are missing or duplicated. This always coincides with modifiedresidues, however does not always occur when there is a modified residue in the sequence. This columncontains the originalauth_seq_id information that does not have these positions corrected.

Examples

pdb <- fetch_pdb(pdb_ids = c("6HG1", "1E9I", "6D3Q", "4JHW"))head(pdb)

Fetch PDB structure atom data from RCSB

Description

Fetches atom data for a PDB structure from RCSB. If you want to retrieve metadata about PDBstructures, use the functionfetch_pdb(). The information retrieved is based on the.cif file of the structure, which may vary from the .pdb file.

Usage

fetch_pdb_structure(pdb_ids, return_data_frame = FALSE, show_progress = TRUE)

Arguments

Value

A list that contains atom data for each PDB structures provided. If return_data_frame isTRUE, a data frame with this information is returned instead. The data frame contains thefollowing columns:

• label_id: Uniquely identifies every atom in the structure following the standardisedconvention for mmCIF files. Example value: "5", "C12", "Ca3g28", "Fe3+17", "H*251", "boron2a","C a phe 83 a 0", "Zn Zn 301 A 0"

• type_symbol: The code used to identify the atom species representing this atom type.Normally this code is the element symbol. The code may be composed of any character except anunderscore with the additional proviso that digits designate an oxidation state and must befollowed by a + or - character. Example values: "C", "Cu2+", "H(SDS)", "dummy", "FeNi".

• label_atom_id: Uniquely identifies every atom for the given residue following thestandardised convention for mmCIF files. Example values: "CA", "HB1", "CB", "N"

• label_comp_id: A chemical identifier for the residue. For protein polymer entities,this is the three- letter code for the amino acid. For nucleic acid polymer entities, this isthe one-letter code for the base. Example values: "ala", "val", "A", "C".

• label_asym_id: Chain identifier following the standardised convention for mmCIF files.Example values: "1", "A", "2B3".

• entity_id: Records details about the molecular entities that are present in thecrystallographic structure. Usually all different types of molecular entities such as polymerentities, non-polymer entities or water molecules are numbered once for each structure. Eachtype of non-polymer entity has its own number. Thus, the highest number in this columnrepresents the number of different molecule types in the structure.

• label_seq_id: Uniquely and sequentially identifies residues for eachlabel_asym_id.This is always a number and the sequence of numbers always progresses in increasing numerical order.

• x: The x coordinate of the atom.

• y: The y coordinate of the atom.

• z: The z coordinate of the atom.

• site_occupancy: The fraction of the atom type present at this site.

• b_iso_or_equivalent: Contains the B-factor or isotopic atomic displacement factor foreach atom.

• formal_charge: The net integer charge assigned to this atom. This is the formal chargeassignment normally found in chemical diagrams. It is currently only assigned in a small subsetof structures.

• auth_seq_id: An alternative residue identifier (label_seq_id) provided by theauthor of the structure in order to match the identification used in the publication thatdescribes the structure. This does not need to be numeric and is therefore of type character.

• auth_comp_id: An alternative chemical identifier (label_comp_id) provided by theauthor of the structure in order to match the identification used in the publication thatdescribes the structure.

• auth_asym_id: An alternative chain identifier (label_asym_id) provided by theauthor of the structure in order to match the identification used in the publication thatdescribes the structure.

• pdb_model_number: The PDB model number.

• pdb_id: The protein database identifier for the structure.

Examples

pdb_structure <- fetch_pdb_structure(  pdb_ids = c("6HG1", "1E9I", "6D3Q", "4JHW"),  return_data_frame = TRUE)head(pdb_structure, n = 10)

Fetch information from the QuickGO API

Description

Fetches gene ontology (GO) annotations, terms or slims from the QuickGO EBI database.Annotations can be retrieved for specific UniProt IDs or NCBI taxonomy identifiers. Whenterms are retrieved, a complete list of all GO terms is returned. For the generation ofa slim dataset you can provide GO IDs that should be considered. A slim dataset is a subsetGO dataset that considers all child terms of the supplied IDs.

Usage

fetch_quickgo(  type = "annotations",  id_annotations = NULL,  taxon_id_annotations = NULL,  ontology_annotations = "all",  go_id_slims = NULL,  relations_slims = c("is_a", "part_of", "regulates", "occurs_in"),  timeout = 1200,  max_tries = 2,  show_progress = TRUE)

Arguments

Value

A data frame that contains descriptive information about gene ontology annotations, terms or slimsdepending on what the input "type" was.

Examples

# Annotationsannotations <- fetch_quickgo(  type = "annotations",  id = c("P63328", "Q4FFP4"),  ontology = "molecular_function")head(annotations)# Termsterms <- fetch_quickgo(type = "terms")head(terms)# Slimsslims <- fetch_quickgo(  type = "slims",  go_id_slims = c("GO:0046872", "GO:0051540"))head(slims)

Fetch protein data from UniProt

Description

Fetches protein metadata from UniProt.

Usage

fetch_uniprot(  uniprot_ids,  columns = c("protein_name", "length", "sequence", "gene_names", "xref_geneid",    "xref_string", "go_f", "go_p", "go_c", "cc_interaction", "ft_act_site", "ft_binding",    "cc_cofactor", "cc_catalytic_activity", "xref_pdb"),  batchsize = 200,  max_tries = 10,  timeout = 20,  show_progress = TRUE)

Arguments

Value

A data frame that contains all protein metadata specified incolumns for theproteins provided. Theinput_id column contains the provided UniProt IDs. If an invalid IDwas provided that contains a valid UniProt ID, the valid portion of the ID is still fetched andpresent in theaccession column, while theinput_id column contains the original not completelyvalid ID.

Examples

fetch_uniprot(c("P36578", "O43324", "Q00796"))# Not completely valid IDfetch_uniprot(c("P02545", "P02545;P20700"))

Fetch proteome data from UniProt

Description

Fetches proteome data from UniProt for the provided organism ID.

Usage

fetch_uniprot_proteome(  organism_id,  columns = c("accession"),  reviewed = TRUE,  timeout = 120,  max_tries = 5)

Arguments

Value

A data frame that contains all protein metadata specified incolumns for theorganism of choice.

Examples

head(fetch_uniprot_proteome(9606))

Data filtering based on coefficients of variation (CV)

Description

Filters the input data based on precursor, peptide or protein intensity coefficients of variation.The function should be used to ensure that only robust measurements and quantifications are used fordata analysis. It is advised to use the function after inspection of raw values (quality control)and median normalisation. Generally, the function calculates CVs of each peptide, precursor orprotein for each condition and removes peptides, precursors or proteins that have a CV abovethe cutoff in less than the (user-defined) required number of conditions. Since the user-definedcutoff is fixed and does not depend on the number of conditions that have detected values, thefunction might bias for data completeness.

Usage

filter_cv(  data,  grouping,  condition,  log2_intensity,  cv_limit = 0.25,  min_conditions,  silent = FALSE)

Arguments

Value

The CV filtered data frame.

Examples

set.seed(123) # Makes example reproducible# Create synthetic datadata <- create_synthetic_data(  n_proteins = 50,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random",  additional_metadata = FALSE)# Filter coefficients of variationdata_filtered <- filter_cv(  data = data,  grouping = peptide,  condition = condition,  log2_intensity = peptide_intensity_missing,  cv_limit = 0.25,  min_conditions = 2)

Find all sub IDs of an ID in a network

Description

For a given ID, find all sub IDs and their sub IDs etc. The type ofrelationship can be selected too. This is a helper function for other functions.

Usage

find_all_subs(  data,  ids,  main_id = id,  type = type,  accepted_types = "is_a",  exclude_parent_id = FALSE)

Arguments

Value

A list of character vectors containing the provided ID and all of its sub IDs. Itcontains one element per input ID.

Find ChEBI IDs for name patterns

Description

Search for chebi IDs that match a specific name pattern. A list of corresponding ChEBI IDs isreturned.

Usage

find_chebis(chebi_data, pattern)

Arguments

Value

A list of character vectors containing ChEBI IDs that have a name matching the suppliedpattern. It contains one element per pattern.

Find peptide location

Description

The position of the given peptide sequence is searched within the given protein sequence. Inaddition the last amino acid of the peptide and the amino acid right before are reported.

Usage

find_peptide(data, protein_sequence, peptide_sequence)

Arguments

Value

A data frame that contains the input data and four additional columns with peptidestart and end position, the last amino acid and the amino acid before the peptide.

Examples

# Create example datadata <- data.frame(  protein_sequence = c("abcdefg"),  peptide_sequence = c("cde"))# Find peptidefind_peptide(  data = data,  protein_sequence = protein_sequence,  peptide_sequence = peptide_sequence)

Finds peptide positions in a PDB structure based on positional matching

Description

Finds peptide positions in a PDB structure. Often positions of peptides in UniProt and a PDBstructure are different due to different lengths of structures. This function maps a peptidebased on its UniProt positions onto a PDB structure. This method is superior to sequencealignment of the peptide to the PDB structure sequence, since it can also match the peptide ifthere are truncations or mismatches. This function also provides an easy way to check if apeptide is present in a PDB structure.

Usage

find_peptide_in_structure(  peptide_data,  peptide,  start,  end,  uniprot_id,  pdb_data = NULL,  retain_columns = NULL)

Arguments

Value

A data frame that contains peptide positions in the corresponding PDB structures. If apeptide is not found in any structure or no structure is associated with the protein, the dataframe contains NAs values for the output columns. The data frame contains the following andadditional columns:

• auth_asym_id: Chain identifier provided by the author of the structure in order tomatch the identification used in the publication that describes the structure.

• label_asym_id: Chain identifier following the standardised convention for mmCIF files.

• peptide_seq_in_pdb: The sequence of the peptide mapped to the structure. If thepeptide only maps partially, then only the part of the sequence that maps on the structure isreturned.

• fit_type: The fit type is either "partial" or "fully" and it indicates if the completepeptide or only part of it was found in the structure.

• label_seq_id_start: Contains the first residue position of the peptide in the structurefollowing the standardised convention for mmCIF files.

• label_seq_id_end: Contains the last residue position of the peptide in the structurefollowing the standardised convention for mmCIF files.

• auth_seq_id_start: Contains the first residue position of the peptide in the structurebased on the alternative residue identifier provided by the author of the structure in orderto match the identification used in the publication that describes the structure. This doesnot need to be numeric and is therefore of type character.

• auth_seq_id_end: Contains the last residue position of the peptide in the structurebased on the alternative residue identifier provided by the author of the structure in orderto match the identification used in the publication that describes the structure. This doesnot need to be numeric and is therefore of type character.

• auth_seq_id: Contains all positions (separated by ";") of the peptide in the structurebased on the alternative residue identifier provided by the author of the structure in orderto match the identification used in the publication that describes the structure. This doesnot need to be numeric and is therefore of type character.

• n_peptides: The number of peptides from one protein that were searched for within thecurrent structure.

• n_peptides_in_structure: The number of peptides from one protein that were found withinthe current structure.

Examples

# Create example datapeptide_data <- data.frame(  uniprot_id = c("P0A8T7", "P0A8T7", "P60906"),  peptide_sequence = c(    "SGIVSFGKETKGKRRLVITPVDGSDPYEEMIPKWRQLNV",    "NVFEGERVER",    "AIGEVTDVVEKE"  ),  start = c(1160, 1197, 55),  end = c(1198, 1206, 66))# Find peptides in protein structurepeptide_in_structure <- find_peptide_in_structure(  peptide_data = peptide_data,  peptide = peptide_sequence,  start = start,  end = end,  uniprot_id = uniprot_id)head(peptide_in_structure, n = 10)

Fitting four-parameter dose response curves

Description

Function for fitting four-parameter dose response curves for each group (precursor, peptide orprotein). In addition it can annotate data based on completeness, the completeness distributionand statistical testing using ANOVA. Filtering by the function is only performed based on completenessif selected.

Usage

fit_drc_4p(  data,  sample,  grouping,  response,  dose,  filter = "post",  replicate_completeness = 0.7,  condition_completeness = 0.5,  n_replicate_completeness = NULL,  n_condition_completeness = NULL,  complete_doses = NULL,  anova_cutoff = 0.05,  correlation_cutoff = 0.8,  log_logarithmic = TRUE,  include_models = FALSE,  retain_columns = NULL)

Arguments

Details

If data filtering options are selected, data is annotated based on multiple criteria.If"post" is selected the data is annotated based on completeness, the completeness distribution, theadjusted ANOVA p-value cutoff and a correlation cutoff. Completeness of features is determined based onthen_replicate_completeness andn_condition_completeness arguments. The completeness distribution determinesif there is a distribution of not random missingness of data along the dose. For this it is checked if half of afeatures values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall intothis category, the values that fulfill the completeness cutoff and the ones that do not fulfill itneed to be consecutive, meaning located next to each other based on their concentration values. Furthermore,the values that do not pass the completeness cutoff need to be lower in intensity. Lastly, the differencebetween the two groups is tested for statistical significance using a Welch's t-test and acutoff of p <= 0.1 (we want to mainly discard curves that falsely fit the other criteria but thathave clearly non-significant differences in mean). This allows curves to be considered that havemissing values in half of their observations due to a decrease in intensity. It can be thoughtof as conditions that are missing not at random (MNAR). It is often the case that those entitiesdo not have a significant p-value since half of their conditions are not considered due to datamissingness. The ANOVA test is performed on the features by concentration. If it is significant it islikely that there is some response. However, this test would also be significant even if there is oneoutlier concentration so it should only be used only in combination with other cutoffs to determineif a feature is significant. Thepassed_filter column isTRUE for all thefeatures that pass the above mentioned criteria and that have a correlation greater than the cutoff(default is 0.8) and the adjusted ANOVA p-value below the cutoff (default is 0.05).

The final list is ranked based on a score calculated on entities that pass the filter.The score is the negative log10 of the adjusted ANOVA p-value scaled between 0 and 1 and thecorrelation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score anentity can have is 1 with both the highest correlation and adjusted p-value. The rank iscorresponding to this score. Please note, that entities with MNAR conditions might have alower score due to the missing or non-significant ANOVA p-value. If no score could be calculatedthe usual way these cases receive a score of 0. You should have a look at curves that are TRUEfordose_MNAR in more detail.

If the"pre" option is selected for thefilter argument then the data is filtered for completenessprior to curve fitting and the ANOVA test. Otherwise annotation is performed exactly as mentioned above.We recommend the"pre" option because it leaves you with not only the likely hits of your treatment, butalso with rather high confidence true negative results. This is because the filtered data has a highdegree of completeness making it unlikely that a real dose-response curve is missed due to data missingness.

Please note that in general, curves are only fitted if there are at least 5 conditions with data points presentto ensure that there is potential for a good curve fit. This is done independent of the selected filtering option.

Value

Ifinclude_models = FALSE a data frame is returned that contains correlationsof predicted to measured values as a measure of the goodness of the curve fit, an associatedp-value and the four parameters of the model for each group. Furthermore, input data for plotsis returned in the columnsplot_curve (curve and confidence interval) andplot_points(measured points). Ifinclude_models = TURE, a list is returned that contains:

• fit_objects: The fit objects of typedrc for each group.

• correlations: The correlation data frame described above

Examples

# Load librarieslibrary(dplyr)set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 2,  frac_change = 1,  n_replicates = 3,  n_conditions = 8,  method = "dose_response",  concentrations = c(0, 1, 10, 50, 100, 500, 1000, 5000),  additional_metadata = FALSE)# Perform dose response curve fitdrc_fit <- fit_drc_4p(  data = data,  sample = sample,  grouping = peptide,  response = peptide_intensity_missing,  dose = concentration,  n_replicate_completeness = 2,  n_condition_completeness = 5,  retain_columns = c(protein, change_peptide))glimpse(drc_fit)head(drc_fit, n = 10)

Perform gene ontology enrichment analysis

Description

This function was deprecated due to its name changing tocalculate_go_enrichment().

Usage

go_enrichment(...)

Value

A bar plot displaying negative log10 adjusted p-values for the top 10 enriched ordepleted gene ontology terms. Alternatively, plot cutoffs can be chosen individually with theplot_cutoff argument. Bars are colored according to the direction of the enrichment(enriched or deenriched). Ifplot = FALSE, a data frame is returned. P-values areadjusted with Benjamini-Hochberg.

Imputation of missing values

Description

impute is calculating imputation values for missing data depending on the selectedmethod.

Usage

impute(  data,  sample,  grouping,  intensity_log2,  condition,  comparison = comparison,  missingness = missingness,  noise = NULL,  method = "ludovic",  skip_log2_transform_error = FALSE,  retain_columns = NULL)

Arguments

Value

A data frame that contains animputed_intensity andimputed column inaddition to the required input columns. Theimputed column indicates if a value wasimputed. Theimputed_intensity column contains imputed intensity values for previouslymissing intensities.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 10,  frac_change = 0.5,  n_replicates = 4,  n_conditions = 2,  method = "effect_random",  additional_metadata = FALSE)head(data, n = 24)# Assign missingness informationdata_missing <- assign_missingness(  data,  sample = sample,  condition = condition,  grouping = peptide,  intensity = peptide_intensity_missing,  ref_condition = "all",  retain_columns = c(protein, peptide_intensity))head(data_missing, n = 24)# Perform imputationdata_imputed <- impute(  data_missing,  sample = sample,  grouping = peptide,  intensity_log2 = peptide_intensity_missing,  condition = condition,  comparison = comparison,  missingness = missingness,  method = "ludovic",  retain_columns = c(protein, peptide_intensity))head(data_imputed, n = 24)

Perform KEGG pathway enrichment analysis

Description

This function was deprecated due to its name changing tocalculate_kegg_enrichment().

Usage

kegg_enrichment(...)

Value

A bar plot displaying negative log10 adjusted p-values for the top 10 enriched pathways.Bars are coloured according to the direction of the enrichment. Ifplot = FALSE, a dataframe is returned.

Viridis colour scheme

Description

A perceptually uniform colour scheme originally created for the Seaborn python package.

Usage

mako_colours

Format

A vector containing 256 colours

Source

created for the Seaborn statistical data visualization package for Python

Maps peptides onto a PDB structure or AlphaFold prediction

Description

Peptides are mapped onto PDB structures or AlphaFold prediction based on their positions.This is accomplished by replacing the B-factor information in the structure file withvalues that allow highlighting of peptides, protein regions or amino acids when the structureis coloured by B-factor. In addition to simply highlighting peptides, protein regions or aminoacids, a continuous variable such as fold changes associated with them can be mapped onto thestructure as a colour gradient.

Usage

map_peptides_on_structure(  peptide_data,  uniprot_id,  pdb_id,  chain,  auth_seq_id,  map_value,  file_format = ".cif",  scale_per_structure = TRUE,  export_location = NULL,  structure_file = NULL,  show_progress = TRUE)

Arguments

Value

The function exports a modified ".pdb" or ".cif" structure file. B-factors have beenreplaced with scaled (50-100) values provided in themap_value column.

Examples

# Load librarieslibrary(dplyr)# Create example datapeptide_data <- data.frame(  uniprot_id = c("P0A8T7", "P0A8T7", "P60906"),  peptide_sequence = c(    "SGIVSFGKETKGKRRLVITPVDGSDPYEEMIPKWRQLNV",    "NVFEGERVER",    "AIGEVTDVVEKE"  ),  start = c(1160, 1197, 55),  end = c(1198, 1206, 66),  map_value = c(70, 100, 100))# Find peptide positions in structurespositions_structure <- find_peptide_in_structure(  peptide_data = peptide_data,  peptide = peptide_sequence,  start = start,  end = end,  uniprot_id = uniprot_id,  retain_columns = c(map_value)) %>%  filter(pdb_ids %in% c("6UU2", "2EL9"))# Map peptides on structures# You can determine the preferred output location# with the export_location argument. Currently it# is saved in the working directory.map_peptides_on_structure(  peptide_data = positions_structure,  uniprot_id = uniprot_id,  pdb_id = pdb_ids,  chain = auth_asym_id,  auth_seq_id = auth_seq_id,  map_value = map_value,  file_format = ".pdb",  export_location = getwd())

Intensity normalisation

Description

This function was deprecated due to its name changing tonormalise().The normalisation method in the new function needs to be provided as an argument.

Usage

median_normalisation(...)

Value

A data frame with a column callednormalised_intensity_log2 containing thenormalised intensity values.

List of metal-related ChEBI IDs in UniProt

Description

A list that contains all ChEBI IDs that appear in UniProt and that contain either a metal atomin their formula or that do not have a formula but the ChEBI term is related to metals.This was last updated on the 19/02/24.

Usage

metal_chebi_uniprot

Format

A data.frame containing information retrieved from ChEBI usingfetch_chebi(stars = c(2, 3)),filtered using symbols in themetal_list and manual annotation of metal related ChEBI IDs that do notcontain a formula.

Source

UniProt (cc_cofactor, cc_catalytic_activity, ft_binding) and ChEBI

Molecular function gene ontology metal subset

Description

A subset of molecular function gene ontology terms related to metals that was createdusing the slimming process provided by the QuickGO EBI database.This was last updated on the 19/02/24.

Usage

metal_go_slim_subset

Format

A data.frame containing a slim subset of molecular function gene ontology termsthat are related to metal binding. Theslims_from_id column contains all IDs relevantin this subset while theslims_to_ids column contains the starting IDs. If ChEBI IDshave been annotated manually this is indicated in thedatabase column.

Source

QuickGO and ChEBI

List of metals

Description

A list of all metals and metalloids in the periodic table.

Usage

metal_list

Format

A data.frame containing the columnsatomic_number,symbol,name,type,chebi_id.

Source

https://en.wikipedia.org/wiki/Metal and https://en.wikipedia.org/wiki/Metalloid

Analyse protein interaction network for significant hits

Description

This function was deprecated due to its name changing toanalyse_functional_network().

Usage

network_analysis(...)

Value

A network plot displaying interactions of the provided proteins. Ifbinds_treatment was provided halos around the proteins show which proteins interact withthe treatment. Ifplot = FALSE a data frame with interaction information is returned.

Intensity normalisation

Description

Performs normalisation on intensities. For median normalisation the normalised intensity is theoriginal intensity minus the run median plus the global median. This is also the way it isimplemented in the Spectronaut search engine.

Usage

normalise(data, sample, intensity_log2, method = "median")

Arguments

Value

A data frame with a column callednormalised_intensity_log2 containing thenormalised intensity values.

Examples

data <- data.frame(  r_file_name = c("s1", "s2", "s3", "s1", "s2", "s3"),  intensity_log2 = c(18, 19, 17, 20, 21, 19))normalise(data,  sample = r_file_name,  intensity_log2 = intensity_log2,  method = "median")

Creates a contact map of all atoms from a structure file (using parallel processing)

Description

This function is a wrapper aroundcreate_structure_contact_map() that allows the use of allsystem cores for the creation of contact maps. Alternatively, it can be used for sequentialprocessing of large datasets. The benefit of this function overcreate_structure_contact_map()is that it processes contact maps in batches, which is recommended for large datasets. If usedfor parallel processing it should only be used on systems that have enough memory available.Workers can either be set up manually before running the function withfuture::plan(multisession) or automatically by the function (maximum number of workersis 12 in this case). If workers are set up manually theprocessing_type argument shouldbe set to "parallel manual". In this case workers can be terminated after completion withfuture::plan(sequential).

Usage

parallel_create_structure_contact_map(  data,  data2 = NULL,  id,  chain = NULL,  auth_seq_id = NULL,  distance_cutoff = 10,  pdb_model_number_selection = c(0, 1),  return_min_residue_distance = TRUE,  export = FALSE,  export_location = NULL,  split_n = 40,  processing_type = "parallel")

Arguments

Value

A list of contact maps for each PDB or UniProt ID provided in the input is returned.If theexport argument is TRUE, each contact map will be saved as a ".csv" file in thecurrent working directory or the location provided to theexport_location argument.

Examples

## Not run: # Create example datadata <- data.frame(  pdb_id = c("6NPF", "1C14", "3NIR"),  chain = c("A", "A", NA),  auth_seq_id = c("1;2;3;4;5;6;7", NA, NA))# Create contact mapcontact_maps <- parallel_create_structure_contact_map(  data = data,  id = pdb_id,  chain = chain,  auth_seq_id = auth_seq_id,  split_n = 1,)str(contact_maps[["3NIR"]])contact_maps## End(Not run)

Fitting four-parameter dose response curves (using parallel processing)

Description

This function is a wrapper aroundfit_drc_4p that allows the use of all system cores formodel fitting. It should only be used on systems that have enough memory available. Workers caneither be set up manually before running the function withfuture::plan(multisession) orautomatically by the function (maximum number of workers is 12 in this case). If workers are setup manually the number of cores should be provided ton_cores. Worker can be terminatedafter completion withfuture::plan(sequential). It is not possible to export theindividual fit objects when using this function as compared to the non parallel function asthey are too large for efficient export from the workers.

Usage

parallel_fit_drc_4p(  data,  sample,  grouping,  response,  dose,  filter = "post",  replicate_completeness = 0.7,  condition_completeness = 0.5,  n_replicate_completeness = NULL,  n_condition_completeness = NULL,  complete_doses = NULL,  anova_cutoff = 0.05,  correlation_cutoff = 0.8,  log_logarithmic = TRUE,  retain_columns = NULL,  n_cores = NULL)

Arguments

Details

If data filtering options are selected, data is annotated based on multiple criteria.If"post" is selected the data is annotated based on completeness, the completeness distribution, theadjusted ANOVA p-value cutoff and a correlation cutoff. Completeness of features is determined based onthen_replicate_completeness andn_condition_completeness arguments. The completeness distribution determinesif there is a distribution of not random missingness of data along the dose. For this it is checked if half of afeatures values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall intothis category, the values that fulfill the completeness cutoff and the ones that do not fulfill itneed to be consecutive, meaning located next to each other based on their concentration values. Furthermore,the values that do not pass the completeness cutoff need to be lower in intensity. Lastly, the differencebetween the two groups is tested for statistical significance using a Welch's t-test and acutoff of p <= 0.1 (we want to mainly discard curves that falsely fit the other criteria but thathave clearly non-significant differences in mean). This allows curves to be considered that havemissing values in half of their observations due to a decrease in intensity. It can be thoughtof as conditions that are missing not at random (MNAR). It is often the case that those entitiesdo not have a significant p-value since half of their conditions are not considered due to datamissingness. The ANOVA test is performed on the features by concentration. If it is significant it islikely that there is some response. However, this test would also be significant even if there is oneoutlier concentration so it should only be used only in combination with other cutoffs to determineif a feature is significant. Thepassed_filter column isTRUE for all thefeatures that pass the above mentioned criteria and that have a correlation greater than the cutoff(default is 0.8) and the adjusted ANOVA p-value below the cutoff (default is 0.05).

The final list is ranked based on a score calculated on entities that pass the filter.The score is the negative log10 of the adjusted ANOVA p-value scaled between 0 and 1 and thecorrelation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score anentity can have is 1 with both the highest correlation and adjusted p-value. The rank iscorresponding to this score. Please note, that entities with MNAR conditions might have alower score due to the missing or non-significant ANOVA p-value. If no score could be calculatedthe usual way these cases receive a score of 0. You should have a look at curves that are TRUEfordose_MNAR in more detail.

If the"pre" option is selected for thefilter argument then the data is filtered for completenessprior to curve fitting and the ANOVA test. Otherwise annotation is performed exactly as mentioned above.We recommend the"pre" option because it leaves you with not only the likely hits of your treatment, butalso with rather high confidence true negative results. This is because the filtered data has a highdegree of completeness making it unlikely that a real dose-response curve is missed due to data missingness.

Please note that in general, curves are only fitted if there are at least 5 conditions with data points presentto ensure that there is potential for a good curve fit. This is done independent of the selected filtering option.

Value

A data frame is returned that contains correlations of predicted to measured values asa measure of the goodness of the curve fit, an associated p-value and the four parameters ofthe model for each group. Furthermore, input data for plots is returned in the columnsplot_curve(curve and confidence interval) andplot_points (measured points).

Examples

## Not run: # Load librarieslibrary(dplyr)set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 2,  frac_change = 1,  n_replicates = 3,  n_conditions = 8,  method = "dose_response",  concentrations = c(0, 1, 10, 50, 100, 500, 1000, 5000),  additional_metadata = FALSE)# Perform dose response curve fitdrc_fit <- parallel_fit_drc_4p(  data = data,  sample = sample,  grouping = peptide,  response = peptide_intensity_missing,  dose = concentration,  n_replicate_completeness = 2,  n_condition_completeness = 5,  retain_columns = c(protein, change_peptide))glimpse(drc_fit)head(drc_fit, n = 10)## End(Not run)

Peptide abundance profile plot

Description

Creates a plot of peptide abundances across samples. This is helpful to investigate effects ofpeptide and protein abundance changes in different samples and conditions.

Usage

peptide_profile_plot(  data,  sample,  peptide,  intensity_log2,  grouping,  targets,  complete_sample = FALSE,  protein_abundance_plot = FALSE,  interactive = FALSE,  export = FALSE,  export_name = "peptide_profile_plots")

Arguments

Value

A list of peptide profile plots.

Examples

# Create example datadata <- data.frame(  sample = c(    rep("S1", 6),    rep("S2", 6),    rep("S1", 2),    rep("S2", 2)  ),  protein_id = c(    rep("P1", 12),    rep("P2", 4)  ),  precursor = c(    rep(c("A1", "A2", "B1", "B2", "C1", "D1"), 2),    rep(c("E1", "F1"), 2)  ),  peptide = c(    rep(c("A", "A", "B", "B", "C", "D"), 2),    rep(c("E", "F"), 2)  ),  intensity = c(    rnorm(n = 6, mean = 15, sd = 2),    rnorm(n = 6, mean = 21, sd = 1),    rnorm(n = 2, mean = 15, sd = 1),    rnorm(n = 2, mean = 15, sd = 2)  ))# Calculate protein abundances and retain precursor# abundances that can be used in a peptide profile plotcomplete_abundances <- calculate_protein_abundance(  data,  sample = sample,  protein_id = protein_id,  precursor = precursor,  peptide = peptide,  intensity_log2 = intensity,  method = "sum",  for_plot = TRUE)# Plot protein abundance profile# protein_abundance_plot can be set to# FALSE to to also colour precursorspeptide_profile_plot(  data = complete_abundances,  sample = sample,  peptide = precursor,  intensity_log2 = intensity,  grouping = protein_id,  targets = c("P1"),  protein_abundance_plot = TRUE)

Assign peptide type

Description

This function was deprecated due to its name changing toassign_peptide_type().

Usage

peptide_type(...)

Value

A data frame that contains the input data and an additional column with the peptidetype information.

Perform gene ontology enrichment analysis

Description

This function was deprecated due to its name changing todrc_4p_plot().

Usage

plot_drc_4p(...)

Value

Iftargets = "all" a list containing plots for every unique identifier in thegrouping variable is created. Otherwise a plot for the specified targets is created withmaximally 20 facets.

Peptide abundance profile plot

Description

This function was deprecated due to its name changing topeptide_profile_plot().

Usage

plot_peptide_profiles(...)

Value

A list of peptide profile plots.

Plot histogram of p-value distribution

Description

This function was deprecated due to its name changing topval_distribution_plot().

Usage

plot_pval_distribution(...)

Value

A histogram plot that shows the p-value distribution.

Predict protein domains of AlphaFold predictions

Description

Uses the predicted aligned error (PAE) of AlphaFold predictions to find possible protein domains.A graph-based community clustering algorithm (Leiden clustering) is used on the predicted error(distance) between residues of a protein in order to infer pseudo-rigid groups in the protein. This isfor example useful in order to know which parts of protein predictions are likely in a fixed relativeposition towards each other and which might have varying distances.This function is based on python code written by Tristan Croll. The original code can be found on hisGitHub page.

Usage

predict_alphafold_domain(  pae_list,  pae_power = 1,  pae_cutoff = 5,  graph_resolution = 1,  return_data_frame = FALSE,  show_progress = TRUE)

Arguments

Value

A list of the provided proteins that contains domain assignments for each residue. Ifreturn_data_frame isTRUE, a data frame with this information is returned instead. The data frame contains thefollowing columns:

• residue: The protein residue number.

• domain: A numeric value representing a distinct predicted domain in the protein.

• accession: The UniProt protein identifier.

Examples

# Fetch aligned errorsaligned_error <- fetch_alphafold_aligned_error(  uniprot_ids = c("F4HVG8", "O15552"),  error_cutoff = 4)# Predict protein domainsaf_domains <- predict_alphafold_domain(  pae_list = aligned_error,  return_data_frame = TRUE)head(af_domains, n = 10)

Colour scheme for protti

Description

A colour scheme for protti that contains 100 colours.

Usage

protti_colours

Format

A vector containing 100 colours

Source

Dina's imagination.

Structural analysis example data

Description

Example data used for the vignette about structural analysis. The data was obtained fromCappelletti et al. 2021 (doi:10.1016/j.cell.2020.12.021)and corresponds to two separate experiments. Both experiments were limited proteolyis coupled tomass spectrometry (LiP-MS) experiments conducted on purified proteins. The first protein isphosphoglycerate kinase 1 (pgk) and it was treated with 25mM 3-phosphoglyceric acid (3PG).The second protein is phosphoenolpyruvate-protein phosphotransferase (ptsI) and it was treatedwith 25mM fructose 1,6-bisphosphatase (FBP). From both experiments only peptides belonging toeither protein were used for this data set. The ptsI data set contains precursor level datawhile the pgk data set contains peptide level data. The pgk data can be obtained fromsupplementary table 3 from the tab named "pgk+3PG". The ptsI data is only included as raw dataand was analysed using the functions of this package.

Usage

ptsi_pgk

Format

A data frame containing differential abundances and adjusted p-values forpeptides/precursors of two proteins.

Source

Cappelletti V, Hauser T, Piazza I, Pepelnjak M, Malinovska L, Fuhrer T, Li Y, Dörig C,Boersema P, Gillet L, Grossbach J, Dugourd A, Saez-Rodriguez J, Beyer A, Zamboni N, Caflisch A,de Souza N, Picotti P. Dynamic 3D proteomes reveal protein functional alterations at highresolution in situ. Cell. 2021 Jan 21;184(2):545-559.e22.doi:10.1016/j.cell.2020.12.021.Epub 2020 Dec 23. PMID: 33357446; PMCID: PMC7836100.

Plot histogram of p-value distribution

Description

Plots the distribution of p-values derived from any statistical test as a histogram.

Usage

pval_distribution_plot(data, grouping, pval, facet_by = NULL)

Arguments

Value

A histogram plot that shows the p-value distribution.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- data.frame(  peptide = paste0("peptide", 1:1000),  pval = runif(n = 1000))# Plot p-valuespval_distribution_plot(  data = data,  grouping = peptide,  pval = pval)

Check charge state distribution

Description

Calculates the charge state distribution for each sample (by count or intensity).

Usage

qc_charge_states(  data,  sample,  grouping,  charge_states,  intensity = NULL,  remove_na_intensities = TRUE,  method = "count",  plot = FALSE,  interactive = FALSE)

Arguments

Value

A data frame that contains the calculated percentage made up by the sum of eitherall counts or intensities of peptides or precursors of the corresponding charge state(depending on which method is chosen).

Examples

# Load librarieslibrary(dplyr)set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random") %>%  mutate(intensity_non_log2 = 2^peptide_intensity_missing)# Calculate charge percentagesqc_charge_states(  data = data,  sample = sample,  grouping = peptide,  charge_states = charge,  intensity = intensity_non_log2,  method = "intensity",  plot = FALSE)# Plot charge statesqc_charge_states(  data = data,  sample = sample,  grouping = peptide,  charge_states = charge,  intensity = intensity_non_log2,  method = "intensity",  plot = TRUE)

Percentage of contaminants per sample

Description

Calculates the percentage of contaminating proteins as the share of total intensity.

Usage

qc_contaminants(  data,  sample,  protein,  is_contaminant,  intensity,  n_contaminants = 5,  plot = TRUE,  interactive = FALSE)

Arguments

Value

A bar plot that displays the percentage of contaminating proteins over all samples.Ifplot = FALSE a data frame is returned.

Examples

data <- data.frame(  sample = c(rep("sample_1", 10), rep("sample_2", 10)),  leading_razor_protein = c(rep(c("P1", "P1", "P1", "P2", "P2", "P2", "P2", "P3", "P3", "P3"), 2)),  potential_contaminant = c(rep(c(rep(TRUE, 7), rep(FALSE, 3)), 2)),  intensity = c(rep(1, 2), rep(4, 4), rep(6, 4), rep(2, 3), rep(3, 5), rep(4, 2)))qc_contaminants(  data,  sample = sample,  protein = leading_razor_protein,  is_contaminant = potential_contaminant,  intensity = intensity)

Check CV distribution

Description

Calculates and plots the coefficients of variation for the selected grouping.

Usage

qc_cvs(  data,  grouping,  condition,  intensity,  plot = TRUE,  plot_style = "density",  max_cv = 200)

Arguments

Value

Either a data frame with the median CVs in % or a plot showing the distribution of the CVsis returned.

Examples

# Load librarieslibrary(dplyr)set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random") %>%  mutate(intensity_non_log2 = 2^peptide_intensity_missing)# Calculate coefficients of variationqc_cvs(  data = data,  grouping = peptide,  condition = condition,  intensity = intensity_non_log2,  plot = FALSE)# Plot coefficients of variation# Different plot styles are availableqc_cvs(  data = data,  grouping = peptide,  condition = condition,  intensity = intensity_non_log2,  plot = TRUE,  plot_style = "violin")

Data completeness

Description

Calculates the percentage of data completeness. That means, what percentage of all detectedprecursors is present in each sample.

Usage

qc_data_completeness(  data,  sample,  grouping,  intensity,  digestion = NULL,  plot = TRUE,  interactive = FALSE)

Arguments

Value

A bar plot that displays the percentage of data completeness over all samples.Ifplot = FALSE a data frame is returned. Ifinteractive = TRUE, the plot isinteractive.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random")# Determine data completenessqc_data_completeness(  data = data,  sample = sample,  grouping = peptide,  intensity = peptide_intensity_missing,  plot = FALSE)# Plot data completenessqc_data_completeness(  data = data,  sample = sample,  grouping = peptide,  intensity = peptide_intensity_missing,  plot = TRUE)

Check number of precursor, peptide or protein IDs

Description

Returns a plot or table of the number of IDs for each sample. The default settings removegrouping variables without quantitative information (intensity is NA). These will not becounted as IDs.

Usage

qc_ids(  data,  sample,  grouping,  intensity,  remove_na_intensities = TRUE,  condition = NULL,  title = "ID count per sample",  plot = TRUE,  interactive = FALSE)

Arguments

Value

A bar plot with the height corresponding to the number of IDs, each bar represents onesample (ifplot = TRUE). Ifplot = FALSE a table with ID counts is returned.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random")# Calculate number of identificationsqc_ids(  data = data,  sample = sample,  grouping = peptide,  intensity = peptide_intensity_missing,  condition = condition,  plot = FALSE)# Plot number of identificationsqc_ids(  data = data,  sample = sample,  grouping = peptide,  intensity = peptide_intensity_missing,  condition = condition,  plot = TRUE)

Check intensity distribution per sample and overall

Description

Plots the overall or sample-wise distribution of all peptide intensities as a boxplot orhistogram.

Usage

qc_intensity_distribution(  data,  sample = NULL,  grouping,  intensity_log2,  plot_style)

Arguments

Value

A histogram or boxplot that shows the intensity distribution over all samples or bysample.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random")# Plot intensity distribution# The plot style can be changedqc_intensity_distribution(  data = data,  sample = sample,  grouping = peptide,  intensity_log2 = peptide_intensity_missing,  plot_style = "boxplot")

Median run intensities

Description

Median intensities per run are returned either as a plot or a table.

Usage

qc_median_intensities(  data,  sample,  grouping,  intensity,  plot = TRUE,  interactive = FALSE)

Arguments

Value

A plot that displays median intensity over all samples. Ifplot = FALSE a dataframe containing median intensities is returned.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random")# Calculate median intensitiesqc_median_intensities(  data = data,  sample = sample,  grouping = peptide,  intensity = peptide_intensity_missing,  plot = FALSE)# Plot median intensitiesqc_median_intensities(  data = data,  sample = sample,  grouping = peptide,  intensity = peptide_intensity_missing,  plot = TRUE)

Check missed cleavages

Description

Calculates the percentage of missed cleavages for each sample (by count or intensity). Thedefault settings remove grouping variables without quantitative information (intensity is NA).These will not be used for the calculation of missed cleavage percentages.

Usage

qc_missed_cleavages(  data,  sample,  grouping,  missed_cleavages,  intensity,  remove_na_intensities = TRUE,  method = "count",  plot = FALSE,  interactive = FALSE)

Arguments

Value

A data frame that contains the calculated percentage made up by the sum of all peptidesor precursors containing the corresponding amount of missed cleavages.

Examples

library(dplyr)set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random") %>%  mutate(intensity_non_log2 = 2^peptide_intensity_missing)# Calculate missed cleavage percentagesqc_missed_cleavages(  data = data,  sample = sample,  grouping = peptide,  missed_cleavages = n_missed_cleavage,  intensity = intensity_non_log2,  method = "intensity",  plot = FALSE)# Plot missed cleavagesqc_missed_cleavages(  data = data,  sample = sample,  grouping = peptide,  missed_cleavages = n_missed_cleavage,  intensity = intensity_non_log2,  method = "intensity",  plot = TRUE)

Plot principal component analysis

Description

Plots a principal component analysis based on peptide or precursor intensities.

Usage

qc_pca(  data,  sample,  grouping,  intensity,  condition,  components = c("PC1", "PC2"),  digestion = NULL,  plot_style = "pca")

Arguments

Value

A principal component analysis plot showing PC1 and PC2. Ifplot_style = "scree", ascree plot for all dimensions is returned.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,)# Plot scree plotqc_pca(  data = data,  sample = sample,  grouping = peptide,  intensity = peptide_intensity_missing,  condition = condition,  plot_style = "scree")# Plot principal componentsqc_pca(  data = data,  sample = sample,  grouping = peptide,  intensity = peptide_intensity_missing,  condition = condition)

Peak width over retention time

Description

Plots one minute binned median precursor elution peak width over retention time for each sample.

Usage

qc_peak_width(  data,  sample,  intensity,  retention_time,  peak_width = NULL,  retention_time_start = NULL,  retention_time_end = NULL,  remove_na_intensities = TRUE,  interactive = FALSE)

Arguments

Value

A line plot displaying one minute binned median precursor elution peak width overretention time for each sample.

Examples

data <- data.frame(  r_file_name = c(rep("sample_1", 10), rep("sample2", 10)),  fg_quantity = c(rep(2000, 20)),  eg_mean_apex_rt = c(rep(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 2)),  eg_start_rt = c(0.5, 1, 3, 4, 5, 6, 7, 7.5, 8, 9, 1, 2, 2, 3, 4, 5, 5, 8, 9, 9),  eg_end_rt = c(    1.5, 2, 3.1, 4.5, 5.8, 6.6, 8, 8, 8.4,    9.1, 3, 2.2, 4, 3.4, 4.5, 5.5, 5.6, 8.3, 10, 12  ))qc_peak_width(  data,  sample = r_file_name,  intensity = fg_quantity,  retention_time = eg_mean_apex_rt,  retention_time_start = eg_start_rt,  retention_time_end = eg_end_rt)

Check peptide type percentage share

Description

Calculates the percentage share of each peptide types (fully-tryptic, semi-tryptic,non-tryptic) for each sample.

Usage

qc_peptide_type(  data,  sample,  peptide,  pep_type,  intensity,  remove_na_intensities = TRUE,  method = "count",  plot = FALSE,  interactive = FALSE)

Arguments

Value

A data frame that contains the calculated percentage shares of each peptide type persample. Thecount column contains the number of peptides with a specific type. Thepeptide_type_percent column contains the percentage share of a specific peptide type.

Examples

# Load librarieslibrary(dplyr)set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random") %>%  mutate(intensity_non_log2 = 2^peptide_intensity_missing)# Determine peptide type percentagesqc_peptide_type(  data = data,  sample = sample,  peptide = peptide,  pep_type = pep_type,  intensity = intensity_non_log2,  method = "intensity",  plot = FALSE)# Plot peptide typeqc_peptide_type(  data = data,  sample = sample,  peptide = peptide,  pep_type = pep_type,  intensity = intensity_non_log2,  method = "intensity",  plot = TRUE)

Proteome coverage per sample and total

Description

Calculates the proteome coverage for each samples and for all samples combined. In other words the fraction of detected proteins to all proteins in the proteome is calculated.

Usage

qc_proteome_coverage(  data,  sample,  protein_id,  organism_id,  reviewed = TRUE,  plot = TRUE,  interactive = FALSE)

Arguments

Value

A bar plot showing the percentage of of the proteome detected and undetected in totaland for each sample. Ifplot = FALSE a data frame containing the numbers is returned.

Examples

# Create example dataproteome <- data.frame(id = 1:4518)data <- data.frame(  sample = c(rep("A", 101), rep("B", 1000), rep("C", 1000)),  protein_id = c(proteome$id[1:100], proteome$id[1:1000], proteome$id[1000:2000]))# Calculate proteome coverageqc_proteome_coverage(  data = data,  sample = sample,  protein_id = protein_id,  organism_id = 83333,  plot = FALSE)# Plot proteome coverageqc_proteome_coverage(  data = data,  sample = sample,  protein_id = protein_id,  organism_id = 83333,  plot = TRUE)

Check ranked intensities

Description

Calculates and plots ranked intensities for proteins, peptides or precursors.

Usage

qc_ranked_intensities(  data,  sample,  grouping,  intensity_log2,  facet = FALSE,  plot = FALSE,  y_axis_transformation = "log10",  interactive = FALSE)

Arguments

Value

A data frame containing the ranked intensities is returned. Ifplot = TRUE a plotis returned. The intensities are log10 transformed for the plot.

Examples

set.seed(123) # Makes example reproducible# Create synthetic datadata <- create_synthetic_data(  n_proteins = 50,  frac_change = 0.05,  n_replicates = 4,  n_conditions = 3,  method = "effect_random",  additional_metadata = FALSE)# Plot ranked intensities for all samples combinedqc_ranked_intensities(  data = data,  sample = sample,  grouping = peptide,  intensity_log2 = peptide_intensity,  plot = TRUE,)# Plot ranked intensities for each sample separatelyqc_ranked_intensities(  data = data,  sample = sample,  grouping = peptide,  intensity_log2 = peptide_intensity,  plot = TRUE,  facet = TRUE)

Correlation based hirachical clustering of samples

Description

A correlation heatmap is created that uses hirachical clustering to determine sample similarity.

Usage

qc_sample_correlation(  data,  sample,  grouping,  intensity_log2,  condition,  digestion = NULL,  run_order = NULL,  method = "spearman",  interactive = FALSE)

Arguments

Value

A correlation heatmap that compares each sample. The dendrogram is sorted by optimalleaf ordering.

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random")# Create sample correlation heatmapqc_sample_correlation(  data = data,  sample = sample,  grouping = peptide,  intensity_log2 = peptide_intensity_missing,  condition = condition)

Protein coverage distribution

Description

Plots the distribution of protein coverages in a histogram.

Usage

qc_sequence_coverage(  data,  protein_identifier,  coverage,  sample = NULL,  interactive = FALSE)

Arguments

Value

A protein coverage histogram with 5 percent binwidth. The vertical dotted lineindicates the median.

See Also

sequence_coverage

Examples

set.seed(123) # Makes example reproducible# Create example datadata <- create_synthetic_data(  n_proteins = 100,  frac_change = 0.05,  n_replicates = 3,  n_conditions = 2,  method = "effect_random")# Plot sequence coverageqc_sequence_coverage(  data = data,  protein_identifier = protein,  coverage = coverage)

Randomise samples in MS queue

Description

This function randomises the order of samples in an MS queue. QC and Blank samples are left inplace. It is also possible to randomise only parts of the queue. Before running this make sureto set a specific seed with theset.seed() function. This ensures that the randomisationof the result is consistent if the function is run again.

Usage

randomise_queue(data = NULL, rows = NULL, export = FALSE)

Arguments

Value

Ifexport = TRUE a"randomised_queue.csv" file will be saved in theworking directory. Ifexport = FALSE a data frame that contains the randomised queueis returned.

Examples

queue <- create_queue(  date = c("200722"),  instrument = c("EX1"),  user = c("jquast"),  measurement_type = c("DIA"),  experiment_name = c("JPQ031"),  digestion = c("LiP", "tryptic control"),  treatment_type_1 = c("EDTA", "H2O"),  treatment_type_2 = c("Zeba", "unfiltered"),  treatment_dose_1 = c(10, 30, 60),  treatment_unit_1 = c("min"),  n_replicates = 4,  number_runs = FALSE,  organism = c("E. coli"),  exclude_combinations = list(list(    treatment_type_1 = c("H2O"),    treatment_type_2 = c("Zeba", "unfiltered"),    treatment_dose_1 = c(10, 30)  )),  inj_vol = c(2),  data_path = "D:\\2007_Data",  method_path = "C:\\Xcalibur\\methods\\DIA_120min",  position_row = c("A", "B", "C", "D", "E", "F"),  position_column = 8,  blank_every_n = 4,  blank_position = "1-V1",  blank_method_path = "C:\\Xcalibur\\methods\\blank")head(queue, n = 20)randomised_queue <- randomise_queue(  data = queue,  export = FALSE)head(randomised_queue, n = 20)

Rapamycin 10 uM example data

Description

Rapamycin example data used for the vignette about binary control/treated data. The data wasobtained fromPiazza 2020and corresponds to experiment 18. FKBP1A the rapamycin binding protein and 49 other randomlysampled proteins were used for this example dataset. Furthermore, only the DMSO control and the10 uM condition were used.

Usage

rapamycin_10uM

Format

A data frame containing peptide level data from a Spectronaut report.

Source

Piazza, I., Beaton, N., Bruderer, R. et al. A machine learning-based chemoproteomicapproach to identify drug targets and binding sites in complex proteomes. Nat Commun 11, 4200(2020).doi:10.1038/s41467-020-18071-x

Rapamycin dose response example data

Description

Rapamycin example data used for the vignette about dose response data. The data was obtainedfromPiazza 2020 and correspondsto experiment 18. FKBP1A the rapamycin binding protein and 39 other randomly sampled proteinswere used for this example dataset. The concentration range includes the following points:0 (DMSO control), 10 pM, 100 pM, 1 nM, 10 nM, 100 nM, 1 uM, 10 uM and 100 uM.

Usage

rapamycin_dose_response

Format

A data frame containing peptide level data from a Spectronaut report.

Source

Piazza, I., Beaton, N., Bruderer, R. et al. A machine learning-based chemoproteomicapproach to identify drug targets and binding sites in complex proteomes. Nat Commun 11, 4200(2020).doi:10.1038/s41467-020-18071-x

Read, clean and convert

Description

The function uses the very fastfread function form thedata.table package. Thecolumn names of the resulting data table are made more r-friendly usingclean_names fromthejanitor package. It replaces "." and " " with "_" and converts names to lower casewhich is also known as snake_case. In the end the data table is converted to a tibble.

Usage

read_protti(filename, ...)

Arguments

Value

A data frame (with class tibble) that contains the content of the specified file.

Examples

## Not run: read_protti("folder\\filename")## End(Not run)

Replace identified positions in protein sequence by "x"

Description

Helper function for the calculation of sequence coverage, replaces identified positions with an"x" within the protein sequence.

Usage

replace_identified_by_x(sequence, positions_start, positions_end)

Arguments

Value

A character vector that contains the modified protein sequence with each identifiedposition replaced by "x".

Scaling a vector

Description

scale_protti is used to scale a numeric vector either between 0 and 1 or around acentered value using the standard deviation. If a vector containing only one value orrepeatedly the same value is provided, 1 is returned as the scaled value formethod = "01"and 0 is returned formetod = "center".

Usage

scale_protti(x, method)

Arguments

Value

A scaled numeric vector.

Examples

scale_protti(c(1, 2, 1, 4, 6, 8), method = "01")

Protein sequence coverage

Description

This function was deprecated due to its name changing tocalculate_sequence_coverage().

Usage

sequence_coverage(...)

Value

A new column in thedata data frame containing the calculated sequence coveragefor each identified protein

Convert metal names to search pattern

Description

Converts a vector of metal names extracted from theft_metal columnobtained withfetch_uniprot to a pattern that can be used to search for correspondingChEBI IDs. This is used as a helper function for other functions.

Usage

split_metal_name(metal_names)

Arguments

Value

A character vector with metal name search patterns.

Check treatment enrichment

Description

This function was deprecated due to its name changing tocalculate_treatment_enrichment().

Usage

treatment_enrichment(...)

Value

A bar plot displaying the percentage of all detect proteins and all significant proteinsthat bind to the treatment. A Fisher's exact test is performed to calculate the significance ofthe enrichment in significant proteins compared to all proteins. The result is reported as ap-value. Ifplot = FALSE a contingency table in long format is returned.

Query from URL

Description

Downloads data table from URL. If an error occurs during the query (for example due to noconnection) the function waits 3 seconds and tries again. If no result could be obtainedafter the given number of tries a message indicating the problem is returned.

Usage

try_query(  url,  max_tries = 5,  silent = TRUE,  type = "text/tab-separated-values",  timeout = 60,  accept = NULL,  ...)

Arguments

Value

A data frame that contains the table from the url.

Perform Welch's t-test

Description

Performs a Welch's t-test and calculates p-values between two groups.

Usage

ttest_protti(mean1, mean2, sd1, sd2, n1, n2, log_values = TRUE)

Arguments

Value

A data frame that contains the calculated differences of means, standard error, tstatistic and p-values.

Examples

ttest_protti(  mean1 = 10,  mean2 = 15.5,  sd1 = 1,  sd2 = 0.5,  n1 = 3,  n2 = 3)

Viridis colour scheme

Description

A colour scheme by the viridis colour scheme from the viridis R package.

Usage

viridis_colours

Format

A vector containing 256 colours

Source

viridis R package, created by Stéfan van der Walt (stefanv) and Nathaniel Smith (njsmith)

Volcano plot

Description

Plots a volcano plot for the given input.

Usage

volcano_plot(  data,  grouping,  log2FC,  significance,  method,  target_column = NULL,  target = NULL,  facet_by = NULL,  facet_scales = "fixed",  title = "Volcano plot",  x_axis_label = "log2(fold change)",  y_axis_label = "-log10(p-value)",  legend_label = "Target",  colour = NULL,  log2FC_cutoff = 1,  significance_cutoff = 0.01,  interactive = FALSE)

Arguments

Value

Depending on the method used a volcano plot with either highlighted targets(method = "target") or highlighted significant proteins (method = "significant")is returned.

Examples

set.seed(123) # Makes example reproducible# Create synthetic datadata <- create_synthetic_data(  n_proteins = 10,  frac_change = 0.5,  n_replicates = 4,  n_conditions = 3,  method = "effect_random",  additional_metadata = FALSE)# Assign missingness informationdata_missing <- assign_missingness(  data,  sample = sample,  condition = condition,  grouping = peptide,  intensity = peptide_intensity_missing,  ref_condition = "all",  retain_columns = c(protein, change_peptide))# Calculate differential abundancesdiff <- calculate_diff_abundance(  data = data_missing,  sample = sample,  condition = condition,  grouping = peptide,  intensity_log2 = peptide_intensity_missing,  missingness = missingness,  comparison = comparison,  method = "t-test",  retain_columns = c(protein, change_peptide))volcano_plot(  data = diff,  grouping = peptide,  log2FC = diff,  significance = pval,  method = "target",  target_column = change_peptide,  target = TRUE,  facet_by = comparison,  significance_cutoff = c(0.05, "adj_pval"))

Volcano plot

Description

This function was deprecated due to its name changing tovolcano_plot().

Usage

volcano_protti(...)

Value

Depending on the method used a volcano plot with either highlighted targets(method = "target") or highlighted significant proteins (method = "significant")is returned.

Woods' plot

Description

Creates a Woods' plot that plots log2 fold change of peptides or precursors along the proteinsequence. The peptides or precursors are located on the x-axis based on their start and endpositions. The position on the y-axis displays the fold change. The vertical size (y-axis) ofthe box representing the peptides or precursors do not have any meaning.

Usage

woods_plot(  data,  fold_change,  start_position,  end_position,  protein_length,  coverage = NULL,  protein_id,  targets = "all",  facet = TRUE,  colouring = NULL,  fold_change_cutoff = 1,  highlight = NULL,  export = FALSE,  export_name = "woods_plots")

Arguments

Value

A list containing Woods' plots is returned. Plotting peptide or precursor log2 foldchanges along the protein sequence.

Examples

# Create example datadata <- data.frame(  fold_change = c(2.3, 0.3, -0.4, -4, 1),  pval = c(0.001, 0.7, 0.9, 0.003, 0.03),  start = c(20, 30, 45, 90, 140),  end = c(33, 40, 64, 100, 145),  protein_length = c(rep(150, 5)),  protein_id = c(rep("P1", 5)))# Plot Woods' plotwoods_plot(  data = data,  fold_change = fold_change,  start_position = start,  end_position = end,  protein_length = protein_length,  protein_id = protein_id,  colouring = pval)