# Single Dose Treatment Data Analysis Workflow

#### Dina Schuster

#### 2026-01-14

# Introduction

This vignette will give you an overview of how you can analyse bottom-up proteomics data or LiP-MS data using protti. If you would like to analyse dose-response data please refer to the dose-response data analysis vignette. Before analysing your data make sure that it is of sufficient quality and that you do not have any outliers. To do this you can take a look at the quality control vignette.

protti includes several functions that make it easy for the user to analyse and interpret data from bottom-up proteomics or LiP-MS experiments. The R package includes functions for

- Quality control (see quality control vignette for more detailed information)
- Data preparation
  - Median normalisation
  - Data filtering
  - Protein abundance calculation from precursor or peptide intensities
  - Imputation of missing values
  - Fetching of database information (ChEBI, GO, KEGG, MobiDB, UniProt)
  - Calculation of sequence coverage
- Data analysis
  - Statistical hypothesis tests
  - Fitting of dose-response curves (relevant for experiments with several treatment concentrations, click here for more information)
- Data visualisation
  - Volcano plots
  - Barcode plots (plots that show protein coverage and significant changes projected onto the protein sequence)
  - Wood’s plots
  - Profile plots
- Data interpretation
  - Treatment enrichment (check if your hits are enriched with known targets)
  - GO-term enrichment
  - Network analysis (based on STRING database information)
  - KEGG pathway enrichment

You can read more about specific functions and how to use them by calling e.g. `?normalise` (for the `normalise()` function). Calling `?` followed by the function name will display the function documentation and give you more detailed information about the function. This can be done for any of the functions included in the package.

This document will give you an overview of data preparation, data analysis, data visualisation and data interpretation functions included in protti. It will show you how they can be applied to your data. The examples in this file are run on a published LiP-MS data set. In the experiment a HeLa cell lysate was treated with different amounts of the drug rapamycin and the target of rapamycin (FKBP12) was successfully identified. For this vignette we are using a filtered version of the original data set where we include only the 10 μM treatment concentration and the control (untreated). For simplicity only 50 proteins are included in the data set.

The data set is produced from the output of Spectronaut™. However, if you have any other data such as DDA data that was searched with a different search engine you can still apply protti’s functions. Just make sure that your data frame contains tidy data. That means data should be contained in a long format (e.g. all sample names in one column) rather than a wide format (e.g. each sample name in its own column). You can easily achieve this by using the `pivot_longer()` function from the `tidyr` package. If you are unsure what your input data is supposed to look like, please use the `create_synthetic_data()` function and compare this to your data. You can also take a look at the input preparation vignette, there you will find all the necessary information on how to get your data into the correct format.

The input data should have a similar structure to this example:

Sample | Replicate | Peptide | Condition | log2(Intensity)
--- | --- | --- | --- | ---
sample1 | 1 | PEPTIDER | treated | 14
sample1 | 1 | PEPTI | treated | 16
sample1 | 1 | PEPTIDE | treated | 17
sample2 | 1 | PEPTIDER | untreated | 15
sample2 | 1 | PEPTI | untreated | 18
sample2 | 1 | PEPTIDE | untreated | 12

# How to use protti to analyse your data

## Getting started

Before we can start analysing our data, we need to load the protti package. This is done by using the base R function `library()`. In addition, we are also loading the packages `magrittr` and `dplyr`. Both `magrittr` and `dplyr` are part of the tidyverse, a collection of packages that provide useful functionalities for data processing and visualisation. If you use many tidyverse packages in your workflow you can easily load all at once by calling `library(tidyverse)`.

    library(protti)
    library(magrittr)
    library(dplyr)

After having loaded the required packages we will load our data set into the R environment. In order to do this for your data set you can use the function `read_protti()`. This function is a wrapper around the fast `fread()` function from the `data.table` package and the `clean_names()` function from the `janitor` package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case to make them more R-friendly. This will make it easier to remember them and to use them in your data analysis.

    # To read in your own data you can use read_protti()
    your_data <- read_protti(filename = "mydata/data.csv")

For this example we are going to use the `rapamycin_10uM` test data set included in protti. To read in the file we are simply going to use the `utils` function `data()`.

    data("rapamycin_10uM")

## Data preparation

### Log2 transformation, median normalisation and CV filtering

After inspecting the data and performing quality control (see quality control vignette for more information) we will now start to prepare the data for the analysis.

First, we remove decoy hits (used for false discovery rate estimation). Our example data set contains a column called `eg_is_decoy` that consists of logicals indicating whether or not the peptide is a decoy hit. To remove decoys we will use the `dplyr` function `filter()`.

Next, we log2 transform our intensities, then normalise the data to the median value of all runs. To transform the intensities we use the `dplyr` function `mutate()` which creates a new column while maintaining the original column.

Note that we are also using the pipe operator `%>%` included in the R package `magrittr`. `%>%` takes the output of the preceding function and supplies it as the first argument of the following function. Using `%>%` makes code easier to read and follow.

For normalisation we are using the protti function `normalise()`. For this example we will use median normalisation (`method = "median"`). The function normalises intensities for each run to the median of all runs. This is only necessary if your search algorithm does not already median normalise your intensities. For the example data we have disabled median normalisation in Spectronaut, therefore we need to median normalise now. The formula for median normalisation is:

\[median ~ normalised ~ intensity = intensity - median ( run ~ intensity ) + median ( global ~ intensity) \]

To ensure that only good peptide measurements will be used for further analysis, we will also filter our data based on coefficients of variation (CV). In order to do this we are using the function `filter_cv()`. For this example we are retaining peptides with a CV < 25 % in at least one of the two conditions.

The CVs are calculated within the function according to the formula:

\[CV = \frac{standard ~ deviation}{mean} * 100\]

Note: The use of the `filter_cv()` function is optional. It might remove a lot of your data if your experiment was noisy. However, especially in these cases, the function will remove peptides with poor quality and should improve the result. It is very important that if you use this function you should not use the moderated t-test or proDA algorithm on your data for differential abundance estimation and significance testing. This likely will lead to an inflated false positive rate because it alters the distributional assumptions of these tests (Bourgon 2010).

    data_normalised <- rapamycin_10uM %>%
      filter(eg_is_decoy == FALSE) %>%
      mutate(intensity_log2 = log2(fg_quantity)) %>%
      normalise(
        sample = r_file_name,
        intensity_log2 = intensity_log2,
        method = "median"
      )

    data_filtered <- data_normalised %>%
      filter_cv(
        grouping = eg_precursor_id,
        condition = r_condition,
        log2_intensity = intensity_log2,
        cv_limit = 0.25,
        min_conditions = 1
      )

### Remove non-proteotypic peptides

For LiP-MS analysis we commonly remove non-proteotypic peptides (i.e. peptides that could come from more than one protein). If you detect a change in non-proteotypic peptides it is not possible to clearly assign which protein it comes from and therefore which protein is affected by your treatment. If you are using the output from Spectronaut you will find a column called “pep_is_proteotypic”. This column contains logicals indicating whether your peptide is proteotypic or not.

To filter out non proteotypic peptides we are using the `dplyr` function `filter()`.

    data_filtered_proteotypic <- data_filtered %>%
      filter(pep_is_proteotypic == TRUE)

### Fetching database information and assigning peptide types

In order to obtain more information about our identified proteins we are going to use the function `fetch_uniprot()` to download information from UniProt directly.

`fetch_uniprot()` uses a vector of UniProt IDs as its input. We produce this vector by using the base R function `unique()` which will extract all unique elements in the selected protein ID column. In this case we want to download the full protein name, gene IDs, GO terms associated with molecular function, StringDB IDs, information on known interacting proteins, location of the active site, location of binding sites, PDB entries, protein length and protein sequence. There are more options for columns to add (for more information on possible columns to add click here).

`fetch_uniprot()` returns a new data frame. In order to be able to merge this with our original data frame we have to rename the ID column to match the name of the protein ID column of our original data frame. To do this we use `dplyr`’s `rename()` function.

To merge the two data frames we use the `dplyr` function `left_join()`. We match the two data frames by the column “pg_protein_accessions”. By using `left_join()` we retain all rows from our original data frame while adding the columns from the data fame generated with `fetch_uniprot()`.

Note: you can also directly join the UniProt data frame with your data without the need to rename its `id` column. You can specify in the `by` argument in `left_join()` that two columns are differently named.

Next, we would like to assign the trypticity of our peptides (i.e. if the peptides are fully-tryptic, semi-tryptic or non-tryptic). In order to do this we first need to define the peptide positions in the protein and find the preceding and following amino acids. To obtain this information we use the protti function `find_peptide()`. The output of this function can then be used in the function `assign_peptide_type()` which will add an additional column with the peptide trypticity information. By using the function `calculate_sequence_coverage()` we add an additional column to the data frame containing information on how much of the protein sequence we identified in our experiment.

    uniprot_ids <- unique(data_filtered_proteotypic$pg_protein_accessions)

    uniprot <-
      fetch_uniprot(
        uniprot_ids = uniprot_ids,
        columns = c(
          "protein_name",
          "gene_names",
          "go_f",
          "xref_string",
          "cc_interaction",
          "ft_act_site",
          "ft_binding",
          "xref_pdb",
          "length",
          "sequence"
        )
      ) %>%
      rename(pg_protein_accessions = accession)

    data_filtered_uniprot <- data_filtered_proteotypic %>%
      left_join(
        y = uniprot,
        by = "pg_protein_accessions"
      ) %>%
      find_peptide(
        protein_sequence = sequence,
        peptide_sequence = pep_stripped_sequence
      ) %>%
      assign_peptide_type(
        aa_before = aa_before,
        last_aa = last_aa,
        aa_after = aa_after,
        protein_id = pg_protein_accessions
      ) %>%
      calculate_sequence_coverage(
        protein_sequence = sequence,
        peptides = pep_stripped_sequence
      )

With the `qc_sequence_coverage()` function, you check how sequence coverage is distributed over all proteins in the sample. Usually, the center of the distribution is low due to many proteins with poor coverage. For this small data set with only 40 proteins the sequence coverage is distributed relatively evenly.

    qc_sequence_coverage(
      data = data_filtered_uniprot,
      protein_identifier = pg_protein_accessions,
      coverage = coverage
    )

## Data analysis

### Statistical hypothesis test

To test if there is a difference between the peptide abundances in our two conditions (i.e. rapamycin treated and untreated) we use a moderated t-test based on the `limma` R package.

Before the statistical hypothesis test we have to define the types of missing values present in our data set. We are going to use the function `assign_missingness()` which will return a column with information on the types of missingness we have in our data (i.e. complete, missing at random (MAR) or missing not at random (MNAR)). We use the default parameters of this function which assumes that missing values are MAR when the conditions are at least 70 % complete (adjusted downward). Missing values are assumed to be MNAR when less than 20 % of values are present (adjusted_downward) in one condition if the other condition is complete. If not “complete” all other comparisons are label as `NA`. If imputation is performed, these are the comparisons that will not be imputed. The type of missingness assigned to a comparison does not have any influence on the statistical test. However, by default (can be changed) comparisons with missingness `NA` are filtered out prior to p-value adjustment. This means that in addition to imputation the user can use missingness cutoffs also in order to define which comparisons are too incomplete to be trustworthy even if significant.

After assigning the types of data missingness we use the function `calculate_diff_abundance()`. By selecting `method = t-test` the function will perform a Welch’s t-test. There are also options included to perform a moderated t-test based on the R package `limma` or to detect differential abundances based on the algorithm implemented in the R package `proDA`. The algorithm used for `proDA` is based on a probabilistic dropout model which facilitates hypothesis testing (using a moderated t-test) while eliminating the need for imputation.

It has been shown that generally moderated t-tests perform much better also in proteomics data, as compared to t-tests (Kammers et al. 2015). Therefore, we will use a moderated t-test in this example.

Please note that in this example we are not imputing missing values. You can, however, do so by using the function `impute()`. This function uses the output of `assign_missingness()` as its input. You can use two different imputation methods:

- `method = ludovic` will sample values that are MNAR from a normal distribution around a value that is 3 (log2) lower than the mean intensity of the non-missing condition. The method is was developed by our colleague Ludovic Gillet.
- `method = noise` will sample MNAR values from a normal distribution around the mean noise of the complete condition. This requires you to have an additional column with information on the noise, which can be obtained from Spectronaut.

Both methods impute MAR data using the mean and variance of the condition with the missing data. Missingness assigned as `NA` will not be imputed.

Note: If data is imputed this can lead to invalid inferential conclusions due to underestimating statistical uncertainty or it can cause loss of statistical power (Ahlmann-Eltze et al. 2020). Therefore, we do not recommend using a moderated t-test or the proDA algorithm after imputation.

Since we are dealing with a LiP-MS data set we perform the statistical analysis on the precursor* level. For protein abundance data you can simply use protein abundances as your intensities and select your protein groups column for the grouping argument. Make sure to retain any columns you need for further data analysis with the `retain_columns` argument of both functions.

_*A peptide precursor is the actual molecular unit that was detected on the mass spectrometer. This is a peptide with one specific charge state and its modification(s)._

Note: Although it is not required for the data set analysed in this vignette, analysis of LiP-MS data frequently requires correction of LiP peptide intensities for changes in protein abundance. This can be done using the steps outlined in Schopper et al. 2017.

    diff_abundance_data <- data_filtered_uniprot %>%
      assign_missingness(
        sample = r_file_name,
        condition = r_condition,
        grouping = eg_precursor_id,
        intensity = normalised_intensity_log2,
        ref_condition = "control",
        completeness_MAR = 0.7,
        completeness_MNAR = 0.25,
        retain_columns = c(
          pg_protein_accessions,
          go_f,
          xref_string,
          start,
          end,
          length,
          coverage
        )
      ) %>%
      calculate_diff_abundance(
        sample = r_file_name,
        condition = r_condition,
        grouping = eg_precursor_id,
        intensity_log2 = normalised_intensity_log2,
        missingness = missingness,
        comparison = comparison,
        method = "moderated_t-test",
        retain_columns = c(
          pg_protein_accessions,
          go_f,
          xref_string,
          start,
          end,
          length,
          coverage
        )
      )

### p-value distribution

The p-value calculated with the moderated t-test is automatically adjusted for multiple testing using the Benjamini-Hochberg correction. This assures that we keep the false discovery rate low. An assumption of this correction is however, that p-values should have an overall uniform distribution. If there is an effect in the data, there will be an increased frequency of low p-values. You can check this by using the protti function `pval_distribution_plot()`. This also helps you assess whether your p-value distribution fulfills the assumptions for your selected FDR control. The cp4p R package is another great way to check the assumptions underlying FDR control in quantitative experiments.

    pval_distribution_plot(
      data = diff_abundance_data,
      grouping = eg_precursor_id,
      pval = pval
    )

For this subset of data the distribution of p-values is relatively flat and there is no large increase in values in the low p-value range (the distribution is uniform when a lot of your hypotheses are null). This is likely because, for this experiment, only a very small fraction of peptides show changes.

It is recommended to always check the (non-adjusted) p-value distribution. A histogram that does not have a uniform distribution with or without a specific enrichment for very low p-values (due to a treatment induced effect) indicates a failure of the theoretical null distribution, which could have several causes (Efron 2010, chapter 6).

You can read more about p-value distributions here.

### Volcano plot

Next we are going to visualise the output of the previously performed hypothesis test to assess the results of our experiment. For this we are going to plot a volcano plot with fold-changes on the x-axis and the p-value on the y-axis. The output of the previously used `calculate_diff_abundance()` function is ideal to use for the `volcano_plot()` function as it contains all the information we need: precursor IDs, protein IDs, fold changes (`diff`), p-values (`pval`) and adjusted p-values (`adj_pval`). We are going to highlight the peptides of the known target of rapamycin FKBP12 (UniProt ID = P62942) in blue to quickly find the peptides in the plot. You can also make the plot interactive by setting `interactive = TRUE`. This will help you quickly obtain more information on each point in the plot.

Since adjusted p-values are related to unadjusted p-values often in a complex way, it makes them hard to be interpret if they would be used for the y-axis. To nevertheless use the information of adjusted p-values for the plot, you can provide the column name of the adjusted p-values to the `significance_cutoff` argument next to the desired cutoff. The function will look for the closest adjusted p-values above and below the set cutoff and take the mean of the corresponding p-value as the cutoff line. If there is no adjusted p-value in the data that is below the set cutoff no line is displayed. This allows you to display volcano plots using p-values while using adjusted p-values for the cutoff criteria.

    volcano_plot(
      data = diff_abundance_data,
      grouping = eg_precursor_id,
      log2FC = diff,
      significance = pval,
      method = "target",
      target_column = pg_protein_accessions,
      target = "P62942",
      x_axis_label = "log2(fold change) Rapamycin treated vs. untreated",
      significance_cutoff = c(0.05, "adj_pval")
    )

    # The significance_cutoff argument can also just be used for a
    # regular cutoff line by just providing the cutoff value, e.g.
    # signficiance_cutoff = 0.05

### Barcode plot

For LiP-MS experiments a good way to see where on the protein the changes due to binding or conformational changes occur is to plot a barcode plot. A barcode plot can be created with the protti function `barcode_plot()`. The detected peptides are coloured in grey and the changing peptides are highlighted in blue.

In order to produce a barcode plot only for our target FKBP12 we create a data frame that contains only information for our target protein using `dplyr`’s `filter()` function. The filtered data frame is then used as the input for the plot.

    FKBP12 <- diff_abundance_data %>%
      filter(pg_protein_accessions == "P62942")

    barcode_plot(
      data = FKBP12,
      start_position = start,
      end_position = end,
      protein_length = length,
      coverage = coverage,
      colouring = diff,
      cutoffs = c(diff = 1, adj_pval = 0.05),
      protein_id = pg_protein_accessions
    )

### Wood’s plot

An additional way to plot LiP-MS changes is the Woods’ plot. This plot will show the extent of the precursor fold changes along the protein sequence. The precursors are located on the x-axis based on their start and end positions. The position on the y-axis displays the fold change. The vertical size (y-axis) of the box representing the precurors does not have any meaning.

To produce a Woods’ plot we use the function `woods_plot()` and colour the peptides according to their adjusted p-values. We are highlighting significant adjusted p-values (< 0.01) with an asterisk. Peptides can also be coloured by another categorical or continous variable. Asterisks can be added for any logical (binary) variable.

    FKBP12 <- FKBP12 %>%
      mutate(significant = ifelse(adj_pval < 0.01, TRUE, FALSE))

    woods_plot(
      data = FKBP12,
      fold_change = diff,
      start_position = start,
      end_position = end,
      protein_length = length,
      coverage = coverage,
      colouring = adj_pval,
      protein_id = pg_protein_accessions,
      facet = FALSE,
      fold_change_cutoff = 1,
      highlight = significant
    )

### Peptide profile plots

To see how the individual precursors in our target protein are changing with the treatment we plot profile plots by using the function `peptide_profile_plot()`. This is particularly useful as you can quickly see if your whole protein changes in abundance or only a fraction of precursors/peptides. If you have protein abundance data you can also use the plot to show changes in protein abundance over your treatment condition(s). By selecting multiple targets (as a vector) you can produce the plot for multiple proteins.

    FKBP12_intensity <- data_filtered_uniprot %>%
      filter(pg_protein_accessions == "P62942")

    peptide_profile_plot(
      data = FKBP12_intensity,
      sample = r_file_name,
      peptide = eg_precursor_id,
      intensity_log2 = normalised_intensity_log2,
      grouping = pg_protein_accessions,
      targets = "P62942",
      protein_abundance_plot = FALSE
    )

## Additional helpful functions

protti includes additional helpful functions that do not make sense to use for this data set but apply to data sets of full size that have global changes. These functions include the `calculate_go_enrichment()`function that helps you check if your hits are enriched for specific gene ontology (GO) terms, the `analyse_functional_network()` function that plots a String network based on information from StringDB for your hits and the `calculate_kegg_enrichment()` function which checks for enriched pathways in your hits. Furthermore, you can directly check for enrichment of a self defined treatment with the `calculate_treatment_enrichment()` function.

For GO enrichment you would add an additional column to your data frame containing information on whether your hit is significant or not. You can do this by using the `dplyr` function `mutate()`. Here we want the column to contain logicals that are either TRUE when the adjusted p-value is below 0.05 and the log2(fold change) is below -1 or above 1 or to be FALSE if this is not the case. We use the `ifelse()` function to produce the logicals. Furthermore, we annotate if the hit is true positive by marking peptides of the known rapamycin binding protein FKBP12.

For the network analysis we filter the previously produced data frame containing the `is_significant` column for significant hits. This data frame can then be used as an input for `analyse_functional_network()` to check if the proteins can be found in an interaction network based on StringDB information.

For `calculate_kegg_enrichment()` you need to first use the function `fetch_kegg()` to obtain the KEGG pathway identifiers for your data set. You can then use `dplyr`’s `right_join()`to join the output with the previously produced data frame containing a column indicating whether your hits are significant or not.

If you know all known interactors of your specific treatment you can check for an enrichment of these with the `calculate_treatment_enrichment()` function. This is particularly useful if your treatment has an effect on many proteins.

    diff_abundance_significant <- diff_abundance_data %>%
      # mark significant peptides
      mutate(is_significant = ifelse((adj_pval < 0.01 & abs(diff) > 1), TRUE, FALSE)) %>%
      # mark true positive hits
      mutate(binds_treatment = pg_protein_accessions == "P62942")

    ### GO enrichment using "molecular function" annotation from UniProt

    calculate_go_enrichment(
      data = diff_abundance_significant,
      protein_id = pg_protein_accessions,
      is_significant = is_significant,
      go_annotations_uniprot = go_f
    )

    ### Network analysis

    network_input <- diff_abundance_significant %>%
      filter(is_significant == TRUE)

    analyse_functional_network(
      data = network_input,
      protein_id = pg_protein_accessions,
      string_id = xref_string,
      binds_treatment = binds_treatment,
      organism_id = 9606
    )

    ### KEGG pathway enrichment

    # First you need to load KEGG pathway annotations from the KEGG database
    # for your specific organism of interest. In this case HeLa cells were
    # used, therefore the organism of interest is homo sapiens (hsa)

    kegg <- fetch_kegg(species = "hsa")

    # Next we need to annotate our data with KEGG pathway IDs and perform enrichment analysis

    diff_abundance_significant %>%
      # columns containing proteins IDs are named differently
      left_join(kegg, by = c("pg_protein_accessions" = "uniprot_id")) %>%
      calculate_kegg_enrichment(
        protein_id = pg_protein_accessions,
        is_significant = is_significant,
        pathway_id = pathway_id,
        pathway_name = pathway_name
      )

    ### Treatment enrichment analysis

    calculate_treatment_enrichment(diff_abundance_significant,
      protein_id = pg_protein_accessions,
      is_significant = is_significant,
      binds_treatment = binds_treatment,
      treatment_name = "Rapamycin"
    )