Package 'protti'

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 package STRINGdb this resource if provided to R users. This function provides a convenient wrapper for STRINGdb functions that allow an easy use within the protti 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. If binds_treatment was provided halos around the proteins show which proteins interact with the treatment. If plot = FALSE a data frame with interaction information is returned.

Examples

# Create example data
data <- 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 analysis
network <- analyse_functional_network(
  data,
  protein_id = uniprot_id,
  string_id = xref_string,
  organism_id = 511145,
  binds_treatment = is_known,
  plot = TRUE
)

network

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 between group 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
)

Description

The type of missingness (missing at random, missing not at random) is assigned based on the comparison 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. The comparison column contains the comparison name for the specific treatment/reference pair. The missingness column reports the type of missingness.

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

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

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

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

The type of missingness has an influence on the way values are imputeted if imputation is performed subsequently using the impute() function. How each type of missingness is specifically imputed can be found in the function description. The type of missingness assigned to a comparison does not have any influence on the statistical test in the calculate_diff_abundance() function.

Examples

set.seed(123) # Makes example reproducible

# Create example data
data <- 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 information
data_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)

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 a peptide is located at the N- or C-terminus of a protein and fulfills the criterium to be fully-tryptic otherwise, it is also considered as fully-tryptic. Peptides that only fulfill the criterium on one terminus are semi-tryptic peptides. Lastly, peptides that are not fulfilling the 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 peptide type 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)

Description

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

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 the peptides are aligned along the sequence of the corresponding protein, and the average score per amino 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 to draw 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
)

Description

Performs differential abundance calculations and statistical hypothesis tests on data frames with 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 on the grouping variable) and the associated treatment/reference pair. Depending on the method the data frame contains additional columns:

• "t-test": The std_error column contains the standard error of the differential abundances. n_obs contains the number of observations for the specific protein, peptide or precursor (depending on the grouping variable) and the associated treatment/reference pair.

• "t-test_mean_sd": Columns labeled as control refer to the second condition of the comparison pairs. Treated refers to the first condition. mean_control and mean_treated columns contain the means for the reference and treatment condition, respectively. sd_control and sd_treated columns contain the standard deviations for the reference and treatment condition, respectively. n_control and n_treated columns contain the numbers of samples for the reference and treatment condition, respectively. The std_error column contains the standard error of the differential abundances. t_statistic contains the t_statistic for the t-test.

• "moderated_t-test": CI_2.5 and CI_97.5 contain the 2.5% and 97.5% confidence interval borders for differential abundances. 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. B The B-statistic is the log-odds that the protein, peptide or precursor (depending on grouping) has a differential abundance between 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 is 4.48/(1+4.48)=0.82, i.e., the probability is about 82% that this group is differentially abundant. A B-statistic of zero corresponds to a 50-50 chance that the group is differentially abundant.n_obs contains the number of observations for the specific protein, peptide or precursor (depending on the grouping variable) and the associated treatment/reference pair.

• "proDA": The std_error column contains the standard error of the differential abundances. 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 the grouping variable) and the associated treatment/reference pair.

For all methods execept "proDA", the p-value adjustment is performed only on the proportion of data that contains a p-value that is not NA. For "proDA" the p-value adjustment is either performed on the complete dataset (filter_NA_missingness = TRUE) or on the subset of the dataset with missingness that is not NA (filter_NA_missingness = FALSE).

Examples

set.seed(123) # Makes example reproducible

# Create synthetic data
data <- create_synthetic_data(
  n_proteins = 10,
  frac_change = 0.5,
  n_replicates = 4,
  n_conditions = 2,
  method = "effect_random",
  additional_metadata = FALSE
)

# Assign missingness information
data_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 progressively
diff <- 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)

Description

Analyses enrichment of gene ontology terms associated with proteins in the fraction of significant proteins compared to all detected proteins. A two-sided Fisher's exact test is performed to test significance of enrichment or depletion. GO annotations can be provided to this function either through UniProt go_annotations_uniprot, through a table obtained with fetch_go in the go_data argument or GO annotations are fetched automatically by the function by providing ontology_type and organism_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 on plot_style). By default the bar plot displays negative log10 adjusted p-values for the top 10 enriched or deenriched gene ontology terms. Alternatively, plot cutoffs can be chosen individually with the plot_cutoff argument. Bars are colored according to the direction of the enrichment (enriched or deenriched). If a heatmap is returned, terms are organised on the y-axis, while the colour of each tile represents the negative log10 adjusted p-value (default). If a group column is provided the x-axis contains all groups. If plot = FALSE, a data frame is returned. P-values are adjusted with Benjamini-Hochberg.

Examples

# Load libraries
library(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)
}

Description

calculate_imputation is a helper function that is used in the impute function. Depending on the type of missingness and method, it samples values from a normal distribution that 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 specifics are applied to input parameters prior to sampling.

Description

Analyses enrichment of KEGG pathways associated with proteins in the fraction of significant proteins compared to all detected proteins. A Fisher's exact test is performed to test significance 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. If plot = FALSE, a data frame is returned.

Examples

# Load libraries
library(dplyr)

set.seed(123) # Makes example reproducible

# Create example data
kegg_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)
}

Description

Determines relative protein abundances from ion quantification. Only proteins with at least three peptides are considered for quantification. The three peptide rule applies for each sample 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

If for_plot = FALSE, protein abundances are returned, if for_plot = TRUE also precursor intensities are returned in a data frame. The later output is ideal for plotting with peptide_profile_plot() and can be filtered to only include protein abundances.

Examples

# Create example data
data <- 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 abundances
protein_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 plot
complete_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

Description

Calculate sequence coverage for each identified protein.

Usage

calculate_sequence_coverage(data, protein_sequence, peptides)

Arguments

Value

A new column in the data data frame containing the calculated sequence coverage for 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
)

Description

Check for an enrichment of proteins interacting with the treatment in significantly changing proteins 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 proteins that bind to the treatment. A Fisher's exact test is performed to calculate the significance of the enrichment in significant proteins compared to all proteins. The result is reported as a p-value. If plot = FALSE a contingency table in long format is returned.

Examples

# Create example data
data <- 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 enrichment
calculate_treatment_enrichment(
  data,
  protein_id = protein_id,
  is_significant = significant,
  binds_treatment = binds_treatment,
  treatment_name = "Rapamycin",
  plot = TRUE
)

# Plot treatment enrichment by group
calculate_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 enrichment
enrichment <- calculate_treatment_enrichment(
  data,
  protein_id = protein_id,
  is_significant = significant,
  binds_treatment = binds_treatment,
  plot = FALSE
)

enrichment

Description

Performs the correction of LiP-peptides for changes in protein abundance and calculates their significance using a t-test. This function was implemented based on the MSstatsLiP package 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, adjusted standard errors (adj_std_error), degrees of freedom (df), pvalues (pval) and adjusted p-values (adj_pval)

Author(s)

Aaron Fehr

Examples

# Load libraries

library(dplyr)

# Load example data and simulate tryptic data by summing up precursors

data <- rapamycin_10uM

data_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 data

diff_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 changes

corrected <- 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)

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 and sample 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

If export_to_queue = FALSE a file named queue.csv will be returned that contains the generated queue. If export_to_queue = TRUE, the resulting generated queue will be appended to an already existing queue that needs to be specified either interactively or through the argument queue_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"
)

Description

Creates a contact map of a subset or of all atom or residue distances in a structure or AlphaFold prediction file. Contact maps are a useful tool for the identification of protein regions that are in close proximity in the folded protein. Additionally, regions that are interacting closely with a small molecule or metal ion can be easily identified without the need to open the structure in programs such as PyMOL or ChimeraX. For large datasets (more than 40 contact maps) it is recommended to use the parallel_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 the export argument is TRUE, each contact map will be saved as a ".csv" file in the current working directory or the location provided to the export_location argument.

Examples

# Create example data
data <- 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 map
contact_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

Description

This function creates a synthetic limited proteolysis proteomics dataset that can be used to test 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 with values 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 protein
count <- 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 replicates
standard_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

Description

This function peforms the four-parameter dose response curve fit. It is the helper function for the fit in the fit_drc_4p function.

Usage

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

Arguments

Value

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

Description

Function for plotting four-parameter dose response curves for each group (precursor, peptide or protein), based on output from fit_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

If targets = "all" a list containing plots for every unique identifier in the grouping variable is created. Otherwise a plot for the specified targets is created with maximally 20 facets.

Examples

set.seed(123) # Makes example reproducible

# Create example data
data <- 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 fit
drc_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 curves
if (!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"
  )
}

Description

Information of metal binding proteins is extracted from UniProt data retrieved with fetch_uniprot as well as QuickGO data retrieved with fetch_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 the following 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 the most_specific_id column. This information is based on ChEBI.

• ligand_identifier: A ligand identifier that is unique per ligand per protein. It consists of the ligand ID and ligand 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 the metal_id_part column. This information is based on ChEBI.

• 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 returned data frame. In order to unnest the relevant information the following steps have to be taken: It is possible that there are multiple IDs in the "most_specific_id" column. This means that one position cannot be uniquely attributed to one specific ligand even with the same ligand_identifier. Apart from the "most_specific_id" column, in which those instances are separated by ",", in other columns the relevant information is separated by "||". Then information should be split based on the source (not the source column, that one can be removed from the data frame). There are certain columns associated with specific sources (e.g. go_term is associated with the "go_term" source). Values of columns not relevant for a certain source should be replaced with NA. Since a most_specific_id can have multiple chebi_ids associated with it we need to unnest the chebi_id column and associated columns in which information is separated by "|". Afterwards evidence and additional information can be unnested by first splitting data for ";;" and then for ";".

Examples

# Create example data

uniprot_ids <- c("P00393", "P06129", "A0A0C5Q309", "A0A0C9VD04")

## UniProt data
data_uniprot <- fetch_uniprot(
  uniprot_ids = uniprot_ids,
  columns = c(
    "ft_binding",
    "cc_cofactor",
    "cc_catalytic_activity"
  )
)

## QuickGO data
data_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 data
eco <- fetch_eco()
eco_relation <- fetch_eco(return_relation = TRUE)

# Extract metal binding information
metal_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

Description

Fetches the aligned error for AlphaFold predictions for provided proteins. The aligned error is useful for assessing inter-domain accuracy. In detail it represents the expected position error at residue x (scored residue), when the 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 is TRUE, a data frame with this information is returned instead. The data frame contains the following columns:

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

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

• 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)

Description

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

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 is TRUE, a data frame with this information is returned instead. The data frame contains the following columns:

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

• label_comp_id: A chemical identifier for the residue. This is the three- letter code for 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. The numbering 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 as label_seq_id. But of type character.

• auth_comp_id: Same as label_comp_id.

• auth_asym_id: Same as label_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)

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)

Description

Fetches all evidence & conclusion ontology (ECO) information from the QuickGO EBI database. The ECO project is maintained through a public GitHub 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 the biological research domain that arise from laboratory experiments, computational methods, literature curation, or other means. Researchers use evidence to support conclusions that arise out of scientific research. Documenting evidence during scientific research is 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, for example that a protein has a particular function (i.e. a protein functional annotation) or that 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 are essential to navigating the ever-growing (in size and complexity) corpus of scientific information."

More information can be found in their publication.

Value

A data frame that contains descriptive information about each ECO term in the EBI database. If either return_relation or return_history is set to TRUE, the respective information is returned instead of the usual output.

Examples

eco <- fetch_eco()

head(eco)

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 original file 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)

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 a selected organism.

Examples

kegg <- fetch_kegg(species = "hsa")

head(kegg)

Description

Fetches information about protein-metal binding sites from the MetalPDB database. A complete list of different possible search queries 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 data frame contains some columns that might not be self explanatory.

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

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

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

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

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

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

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

• auth_atom_id_ligand: Unique residue specific atom identifier of the metal-coordinating ligand residues, which is provided by the author of the structure in order to match the identification 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")

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 protein regions. The feature column contains information on the source of this annotation. More information on the source can be found here.

Examples

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

Description

Fetches structure metadata from RCSB. If you want to retrieve atom data such as positions, use the function fetch_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 frame contains some columns that might not be self explanatory.

• auth_asym_id: Chain identifier provided by the author of the structure in order to match 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 a position in the structure sequence (pdb_sequence) that matches the position given in ref_beg_seq_id, which is a position within the protein sequence (not included in the data frame). length identifies the stretch of sequence for which positions match accordingly between structure and protein sequence. entity_beg_seq_id is a residue ID based on the standardised convention for mmCIF files.

• auth_seq_id: Residue identifier provided by the author of the structure in order to match the identification used in the publication that describes the structure. This character vector has the same length as the pdb_sequence and each position is the identifier for the matching amino acid position in pdb_sequence. The contained values are not necessarily numbers and the values do not have to be positive.

• modified_monomer: Is composed of first the composition ID of the modification, followed by the label_seq_id position. In parenthesis are the parent monomer identifiers as they appear in the sequence.

• ligand_*: Any column starting with the ligand_* prefix contains information about the position, identity and donors for ligand binding sites. If there are multiple entities of ligands 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 in the model. Individual regions are separated by ";".

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

Examples

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

head(pdb)

Description

Fetches atom data for a PDB structure from RCSB. If you want to retrieve metadata about PDB structures, use the function fetch_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 is TRUE, a data frame with this information is returned instead. The data frame contains the following columns:

• label_id: Uniquely identifies every atom in the structure following the standardised convention 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 an underscore with the additional proviso that digits designate an oxidation state and must be followed by a + or - character. Example values: "C", "Cu2+", "H(SDS)", "dummy", "FeNi".

• label_atom_id: Uniquely identifies every atom for the given residue following the standardised 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 is the 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 the crystallographic structure. Usually all different types of molecular entities such as polymer entities, non-polymer entities or water molecules are numbered once for each structure. Each type of non-polymer entity has its own number. Thus, the highest number in this column represents the number of different molecule types in the structure.

• label_seq_id: Uniquely and sequentially identifies residues for each label_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 for each atom.

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

• auth_seq_id: An alternative residue identifier (label_seq_id) provided by the author of the structure in order to match the identification used in the publication that describes 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 the author of the structure in order to match the identification used in the publication that describes the structure.

• auth_asym_id: An alternative chain identifier (label_asym_id) provided by the author of the structure in order to match the identification used in the publication that describes 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)

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. When terms are retrieved, a complete list of all GO terms is returned. For the generation of a slim dataset you can provide GO IDs that should be considered. A slim dataset is a subset GO 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 slims depending on what the input "type" was.

Examples

# Annotations
annotations <- fetch_quickgo(
  type = "annotations",
  id = c("P63328", "Q4FFP4"),
  ontology = "molecular_function"
)

head(annotations)

# Terms
terms <- fetch_quickgo(type = "terms")

head(terms)

# Slims
slims <- fetch_quickgo(
  type = "slims",
  go_id_slims = c("GO:0046872", "GO:0051540")
)

head(slims)

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 in columns for the proteins provided. The input_id column contains the provided UniProt IDs. If an invalid ID was provided that contains a valid UniProt ID, the valid portion of the ID is still fetched and present in the accession column, while the input_id column contains the original not completely valid ID.

Examples

fetch_uniprot(c("P36578", "O43324", "Q00796"))

# Not completely valid ID
fetch_uniprot(c("P02545", "P02545;P20700"))

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 in columns for the organism of choice.

Examples

head(fetch_uniprot_proteome(9606))

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 for data 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 or protein for each condition and removes peptides, precursors or proteins that have a CV above the cutoff in less than the (user-defined) required number of conditions. Since the user-defined cutoff is fixed and does not depend on the number of conditions that have detected values, the function 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 data
data <- 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 variation
data_filtered <- filter_cv(
  data = data,
  grouping = peptide,
  condition = condition,
  log2_intensity = peptide_intensity_missing,
  cv_limit = 0.25,
  min_conditions = 2
)

Description

For a given ID, find all sub IDs and their sub IDs etc. The type of relationship 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. It contains one element per input ID.

Description

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

Usage

find_chebis(chebi_data, pattern)

Arguments

Value

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

Description

The position of the given peptide sequence is searched within the given protein sequence. In addition 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 peptide start and end position, the last amino acid and the amino acid before the peptide.

Examples

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

# Find peptide
find_peptide(
  data = data,
  protein_sequence = protein_sequence,
  peptide_sequence = peptide_sequence
)

Description

Finds peptide positions in a PDB structure. Often positions of peptides in UniProt and a PDB structure are different due to different lengths of structures. This function maps a peptide based on its UniProt positions onto a PDB structure. This method is superior to sequence alignment of the peptide to the PDB structure sequence, since it can also match the peptide if there are truncations or mismatches. This function also provides an easy way to check if a peptide 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 a peptide is not found in any structure or no structure is associated with the protein, the data frame contains NAs values for the output columns. The data frame contains the following and additional columns:

• auth_asym_id: Chain identifier provided by the author of the structure in order to match 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 the peptide only maps partially, then only the part of the sequence that maps on the structure is returned.

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

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

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

• auth_seq_id_start: Contains the first residue position of the peptide in the structure based on the alternative residue identifier provided by the author of the structure in order to match the identification used in the publication that describes the structure. This does not need to be numeric and is therefore of type character.

• auth_seq_id_end: Contains the last residue position of the peptide in the structure based on the alternative residue identifier provided by the author of the structure in order to match the identification used in the publication that describes the structure. This does not need to be numeric and is therefore of type character.

• auth_seq_id: Contains all positions (separated by ";") of the peptide in the structure based on the alternative residue identifier provided by the author of the structure in order to match the identification used in the publication that describes the structure. This does not need to be numeric and is therefore of type character.

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

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

Examples

# Create example data
peptide_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 structure
peptide_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)

Description

Function for fitting four-parameter dose response curves for each group (precursor, peptide or protein). In addition it can annotate data based on completeness, the completeness distribution and statistical testing using ANOVA. Filtering by the function is only performed based on completeness if 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, the adjusted ANOVA p-value cutoff and a correlation cutoff. Completeness of features is determined based on the n_replicate_completeness and n_condition_completeness arguments. The completeness distribution determines if there is a distribution of not random missingness of data along the dose. For this it is checked if half of a features values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall into this category, the values that fulfill the completeness cutoff and the ones that do not fulfill it need 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 difference between the two groups is tested for statistical significance using a Welch's t-test and a cutoff of p <= 0.1 (we want to mainly discard curves that falsely fit the other criteria but that have clearly non-significant differences in mean). This allows curves to be considered that have missing values in half of their observations due to a decrease in intensity. It can be thought of as conditions that are missing not at random (MNAR). It is often the case that those entities do not have a significant p-value since half of their conditions are not considered due to data missingness. The ANOVA test is performed on the features by concentration. If it is significant it is likely that there is some response. However, this test would also be significant even if there is one outlier concentration so it should only be used only in combination with other cutoffs to determine if a feature is significant. The passed_filter column is TRUE for all the features 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 the correlation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score an entity can have is 1 with both the highest correlation and adjusted p-value. The rank is corresponding to this score. Please note, that entities with MNAR conditions might have a lower score due to the missing or non-significant ANOVA p-value. If no score could be calculated the usual way these cases receive a score of 0. You should have a look at curves that are TRUE for dose_MNAR in more detail.

If the "pre" option is selected for the filter argument then the data is filtered for completeness prior 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, but also with rather high confidence true negative results. This is because the filtered data has a high degree 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 present to ensure that there is potential for a good curve fit. This is done independent of the selected filtering option.

Value

If include_models = FALSE a data frame is returned that contains correlations of predicted to measured values as a measure of the goodness of the curve fit, an associated p-value and the four parameters of the model for each group. Furthermore, input data for plots is returned in the columns plot_curve (curve and confidence interval) and plot_points (measured points). If include_models = TURE, a list is returned that contains:

• fit_objects: The fit objects of type drc for each group.

• correlations: The correlation data frame described above

Examples

# Load libraries
library(dplyr)

set.seed(123) # Makes example reproducible

# Create example data
data <- 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 fit
drc_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)

Description

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

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 an imputed_intensity and imputed column in addition to the required input columns. The imputed column indicates if a value was imputed. The imputed_intensity column contains imputed intensity values for previously missing intensities.

Examples

set.seed(123) # Makes example reproducible

# Create example data
data <- 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 information
data_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 imputation
data_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)

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

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 with values that allow highlighting of peptides, protein regions or amino acids when the structure is coloured by B-factor. In addition to simply highlighting peptides, protein regions or amino acids, a continuous variable such as fold changes associated with them can be mapped onto the structure 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 been replaced with scaled (50-100) values provided in the map_value column.

Examples

# Load libraries
library(dplyr)

# Create example data
peptide_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 structures
positions_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()
)

Description

A list that contains all ChEBI IDs that appear in UniProt and that contain either a metal atom in 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 using fetch_chebi(stars = c(2, 3)), filtered using symbols in the metal_list and manual annotation of metal related ChEBI IDs that do not contain a formula.

Source

UniProt (cc_cofactor, cc_catalytic_activity, ft_binding) and ChEBI

Description

A subset of molecular function gene ontology terms related to metals that was created using 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 terms that are related to metal binding. The slims_from_id column contains all IDs relevant in this subset while the slims_to_ids column contains the starting IDs. If ChEBI IDs have been annotated manually this is indicated in the database column.

Source

QuickGO and ChEBI

Description

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

Usage

metal_list

Format

A data.frame containing the columns atomic_number, symbol, name, type, chebi_id.

Source

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

Description

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

Usage

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

Arguments

Value

A data frame with a column called normalised_intensity_log2 containing the normalised 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"
)

Description

This function is a wrapper around create_structure_contact_map() that allows the use of all system cores for the creation of contact maps. Alternatively, it can be used for sequential processing of large datasets. The benefit of this function over create_structure_contact_map() is that it processes contact maps in batches, which is recommended for large datasets. If used for 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 with future::plan(multisession) or automatically by the function (maximum number of workers is 12 in this case). If workers are set up manually the processing_type argument should be set to "parallel manual". In this case workers can be terminated after completion with future::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 the export argument is TRUE, each contact map will be saved as a ".csv" file in the current working directory or the location provided to the export_location argument.

Examples

## Not run: 
# Create example data
data <- 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 map
contact_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)

Description

This function is a wrapper around fit_drc_4p that allows the use of all system cores for model fitting. It should only be used on systems that have enough memory available. Workers can either be set up manually before running the function with future::plan(multisession) or automatically by the function (maximum number of workers is 12 in this case). If workers are set up manually the number of cores should be provided to n_cores. Worker can be terminated after completion with future::plan(sequential). It is not possible to export the individual fit objects when using this function as compared to the non parallel function as they 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, the adjusted ANOVA p-value cutoff and a correlation cutoff. Completeness of features is determined based on the n_replicate_completeness and n_condition_completeness arguments. The completeness distribution determines if there is a distribution of not random missingness of data along the dose. For this it is checked if half of a features values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall into this category, the values that fulfill the completeness cutoff and the ones that do not fulfill it need 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 difference between the two groups is tested for statistical significance using a Welch's t-test and a cutoff of p <= 0.1 (we want to mainly discard curves that falsely fit the other criteria but that have clearly non-significant differences in mean). This allows curves to be considered that have missing values in half of their observations due to a decrease in intensity. It can be thought of as conditions that are missing not at random (MNAR). It is often the case that those entities do not have a significant p-value since half of their conditions are not considered due to data missingness. The ANOVA test is performed on the features by concentration. If it is significant it is likely that there is some response. However, this test would also be significant even if there is one outlier concentration so it should only be used only in combination with other cutoffs to determine if a feature is significant. The passed_filter column is TRUE for all the features 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 the correlation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score an entity can have is 1 with both the highest correlation and adjusted p-value. The rank is corresponding to this score. Please note, that entities with MNAR conditions might have a lower score due to the missing or non-significant ANOVA p-value. If no score could be calculated the usual way these cases receive a score of 0. You should have a look at curves that are TRUE for dose_MNAR in more detail.

If the "pre" option is selected for the filter argument then the data is filtered for completeness prior 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, but also with rather high confidence true negative results. This is because the filtered data has a high degree 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 present to 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 as a measure of the goodness of the curve fit, an associated p-value and the four parameters of the model for each group. Furthermore, input data for plots is returned in the columns plot_curve (curve and confidence interval) and plot_points (measured points).

Examples

## Not run: 
# Load libraries
library(dplyr)

set.seed(123) # Makes example reproducible

# Create example data
data <- 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 fit
drc_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)

Description

Creates a plot of peptide abundances across samples. This is helpful to investigate effects of peptide 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 data
data <- 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 plot
complete_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 precursors
peptide_profile_plot(
  data = complete_abundances,
  sample = sample,
  peptide = precursor,
  intensity_log2 = intensity,
  grouping = protein_id,
  targets = c("P1"),
  protein_abundance_plot = TRUE
)

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 is for example useful in order to know which parts of protein predictions are likely in a fixed relative position 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 his GitHub 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. If return_data_frame is TRUE, a data frame with this information is returned instead. The data frame contains the following 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 errors
aligned_error <- fetch_alphafold_aligned_error(
  uniprot_ids = c("F4HVG8", "O15552"),
  error_cutoff = 4
)

# Predict protein domains
af_domains <- predict_alphafold_domain(
  pae_list = aligned_error,
  return_data_frame = TRUE
)

head(af_domains, n = 10)

Description

A colour scheme for protti that contains 100 colours.

Usage

protti_colours

Format

A vector containing 100 colours

Source

Dina's imagination.

Description

Example data used for the vignette about structural analysis. The data was obtained from Cappelletti 2021 and corresponds to two separate experiments. Both experiments were limited proteolyis coupled to mass spectrometry (LiP-MS) experiments conducted on purified proteins. The first protein is phosphoglycerate kinase 1 (pgk) and it was treated with 25mM 3-phosphoglyceric acid (3PG). The second protein is phosphoenolpyruvate-protein phosphotransferase (ptsI) and it was treated with 25mM fructose 1,6-bisphosphatase (FBP). From both experiments only peptides belonging to either protein were used for this data set. The ptsI data set contains precursor level data while the pgk data set contains peptide level data. The pgk data can be obtained from supplementary table 3 from the tab named "pgk+3PG". The ptsI data is only included as raw data and was analysed using the functions of this package.

Usage

ptsi_pgk

Format

A data frame containing differential abundances and adjusted p-values for peptides/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 high resolution 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.

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 data
data <- data.frame(
  peptide = paste0("peptide", 1:1000),
  pval = runif(n = 1000)
)

# Plot p-values
pval_distribution_plot(
  data = data,
  grouping = peptide,
  pval = pval
)

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 either all counts or intensities of peptides or precursors of the corresponding charge state (depending on which method is chosen).

Examples

# Load libraries
library(dplyr)

set.seed(123) # Makes example reproducible

# Create example data
data <- 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 percentages
qc_charge_states(
  data = data,
  sample = sample,
  grouping = peptide,
  charge_states = charge,
  intensity = intensity_non_log2,
  method = "intensity",
  plot = FALSE
)

# Plot charge states
qc_charge_states(
  data = data,
  sample = sample,
  grouping = peptide,
  charge_states = charge,
  intensity = intensity_non_log2,
  method = "intensity",
  plot = TRUE
)