# Prostar user manual

Samuel Wieczorek and Thomas Burger

#### 2025-04-15

#### Abstract

The package Prostar (Proteomics statistical analysis with R) is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required. This document covers the functionalities available in Prostar 1.40.0, DAPAR 1.40.0, DAPARdata 1.37.0.

#### Package

DAPAR 1.40.0

# Contents

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# 1 Introduction

The `DAPAR` and `Prostar` packages are a series of software dedicated to the processing of proteomics data. More precisely, they are devoted to the analysis of quantitative datasets produced by bottom-up discovery proteomics experiments with a LC-MS/MS pipeline (Liquid Chromatography and Tandem Mass spectrometry). `DAPAR` (Differential Analysis of Protein Abundance with R) is an R package that contains all the necessary functions to process the data in command lines. It can be used on its own; or as a complement to the numerous Bioconductor packages () it is compliant with; or through the `Prostar` interface. `Prostar` (Proteomics statistical analysis with R) is a web interface based on Shiny technology () that provides GUI (Graphical User Interfaces) to all the `DAPAR` functionalities, so as to guide any practitioner that is not comfortable with R programming through the complete quantitative analysis process. The experiment package `DAPARdata` contains many datasets that can be used as examples. `Prostar` functionalities make it possible to easily:

- Manage quantitative datasets: This includes import/export, conversion and report generation functionalities.
- Perform a complete data processing chain: (i) filtering and data cleaning; (ii) cross-replicate normalization; (iii) missing value imputation; (iv) aggregation of peptide intensities into protein intensities (optional); (v) null hypothesis significance testing.
- Mine the dataset at any step of the processing chain with various tools such as: (i) descriptive statistics; (ii) differential analysis; (iii) Gene Ontology (GO) analysis.

There are many ways to install , as well as its dependencies :

In addition, it is also possible to only install or , so as to work in command lines (for expert users only).

For all the desktop installs, we advise to use a machine with a minimum of 8GB of RAM (although there are no strict constraints).

This method works for any desktop machine with Microsoft Windows. It is not necessary to have R already installed. Go to and click on Zero-install download (Prostar menu). Download the zip file and unzip it. The unzipped folder contains an executable file which directly launches . Notice that at the first launch, an internet connection is necessary, so as to finish the install.

For this type of install, the operating system of the desktop machine can be of any type (Unix/Linux, Mac OS X or Windows). However, it is necessary to have the latest version of R (see Section~) installed in a directory where the user has read/write permissions. Optionally, an IDE (integrated development environment) such as R Studio () may be useful to conveniently deal with the various R package installs.

To install , enter in the R console the following instructions:

For a better experience, it is advised (but not mandatory) to install the development version of the following packages: DT and highcharter. To do so, install the devtools package and execute the following commands:

Once the package is installed, to launch , then enter:

A new window of the default web browser opens.

In the case of several users that are not confortable with R (programming or installing), it is best to have a single version of running on a Shiny server installed on a Unix/Linux server. The users will use through a web browser, exactly if it were locally installed, yet, a single install has to be administrated. In that case, has to be classically installed (see Section~), while on the other hand, the installation of is slightly different.

Shiny Server () is a server program that makes Shiny applications available over the web. If Shiny Server is not installed yet, follow Shiny installation instructions.

Once a Shiny server is available, first install as described in Section~, so as to have the dependencies installed.

Then, execute the following line to get the install directory of Prostar:

The result of this command is now referred as .

Change the owner of the Shiny Server directory and log as shiny

Create a directory named in the Shiny Server directory with the user shiny as owner and then copy the Prostar files.

Create the directory for the shiny application

Copy the ProstarApp directory within the shiny-server directory

Change the owner of the Shiny Server directory

Give the following permissions to the www directory

Check if the configuration file of Shiny Server is correct. For more details, please visit .

Now, the application should be available via a web browser at http://*servername:port*/Prostar.

This type of install should only be performed by advanced R users/programmers, or by the admin of a server version of .

To install the package from the source file with administrator rights, start R and enter:

This step will automatically install the following packages:

is automatically installed with or . However, it is possible to install it alone. Then, it follows the classical way for Bioconductor packages. In a R console, enter:

%

Right after being launched, the web page depicted in Figure~ shows up in the default browser (stand-alone install) or in a portable one (zero install). So far, the navbar only contains 3 menus: Prostar, Data manager and Help. However, as soon as data are loaded in the software, new menus contextualy appear (see Section~).

The is detailed in a dedicated section (Section~).

In the menu, one has access to: In the menu, one has access to:

is under active development, so that despite the developers’ attention, bugs may remain. To signal any, as well as typos, suggestions, etc. or even to ask a question, please contact the developers.

When data are loaded for analysis, mor options are available in the navbar, as illustrated on Figure~. In addition to the navbar, the screen is composed of a large working panel (below the navbar) and a drop-down menu on the upper right corner (which purpose is detailed in Section~).

Table~ summarizes the content of the navbar. Let us note that depending on the dataset content (either proteins or peptides), the menu can slightly change: Notably, if protein-level dataset is loaded, then (which purpose is to roll up from peptides to proteins) is not proposed in the menu.

% The menu gathers all the functionalities that relates to data import, export or conversion:

As soon as one of the three first options of the menu has been used to load a dataset, the and menus appear in the navbar.

Conducting a rigorous differential analysis requires a well-defined pipeline made of several tightly connected steps. In , this pipeline has been designed to be as general as possible. Thus, the menu contains numerous steps which, for a given dataset may not all be necessary, so that some can be occasionally skipped. However, the pipeline has been assembled and organized so as to propose coherent processing, so that the respective order of the different steps should be respected. These steps are the following:

It should be carefully noted that each of these steps modifies the dataset: In other words, the menu offers a succession of data transformations which should be performed by the user in an educated way.

As opposed to the menu, the menu offers a series of tools to analyze and visualize the dataset without transforming it. Thus, there is far less restriction on how and when applying them:

As explained above, each functionality in the menu transforms the current dataset. To authorize some flexibility and to avoid unwanted data corruption, it is possible to save in the original dataset, as well as each intermediate dataset along the processing chain. Concretely, it is possible to store one original dataset, one filtered dataset, one `normalized dataset'', one`imputed dataset’’ and so on. Moreover, at any moment, it is possible to go back to a previous state of the dataset, either to restart a step that went wrong, or just to compare with the tools how the dataset was changed (rapid switching makes it easier to visualize it).

To create a new item in the dataset history, one simply has to click on the save button at the end of each processing step. Each time a new dataset is created, it is by default the one on which the processing goes on.

To navigate through the dataset history, one simply uses the drop-down menu of the upper right corner. Notice that if the user saves the current step (e.g.
imputation), then goes back to a previous step (e.g. normalization ) and start working on this older dataset (to perform another imputation) and then saves it, the new version of the processing overwrites the previous version (the older imputation is lost and only the newest one is stored in memory): in fact, only a single version of the dataset can be saved for a given processing step. As a side effect, if any processing further than imputation was already done (e.g. aggregation), then, the aggregated dataset is not coherent anymore with the imputed one (as the new imputation cannot be automatically transmitted to update the previously tuned aggregation).

Finally, let us note that the name of each dataset version (normalized, imputed, etc.) also indicates if the dataset is a protein-level or a peptide-level one (as for instance the aggregation step transforms a peptide-level dataset into a protein-level one).

The quantitative data should fit into a matrix-like representation where each line corresponds to an analyte and each column to a sample. Within the (\(i\)-th, \(j\)-th) cell of the matrix, one reads the abundance of analyte \(i\) in sample \(j\).

Although strictly speaking, there is no lower or upper bound to the number of lines, it should be recalled that the statistical tools implemented in have been chosen and tuned to fit a discovery experiment dataset with large amount of analytes, so that the result may lack of reliability on too small datasets. Conversely, very large datasets are not inherently a problem, as R algorithms are well scalable, but one should keep in mind the hardware limitations of the machine on which runs to avoid overloading.

As for the number of samples (the columns of the dataset), it is necessary to have at least 2 conditions (or groups of samples) as it is not possible to perform relative comparison otherwise. Moreover, it is necessary to have at least 2 samples per condition, as otherwise, it is not possible to compute an intra-condition variance, which is a prerequisite to numerous processing.

The data table should be formatted in a tabulated file where the first line of the text file contains the column names. It is recommended to avoid special characters such as “]”, “@”, “$”, “%”, etc. that are automatically removed. Similarly, spaces in column names are replaced by dots (“.”). Dot must be used as decimal separator for quantitative values. In addition to the columns containing quantitative values, the file may contain additional columns for metadata. Alternatively, if the data have already been processed by and saved as an MSnset file (see ), it is possible to directly reload them (see Section~).

The allows it to open, import or export quantitative datasets. relies on the MSnSet format which is part of the package :

It is either possible to load existing MSnSet files (see Section~), or to import text (-tabulated) and Excel files (see Section~). The menu allows it to load the datasets of the package as examples to discover functionalities (see Section~).

To reload a dataset that was already formated into an MSnSet file, click on . This opens a pop-up window, so as to let the user choose the appropriate file. Once the file is uploaded, a short summary of the dataset is shown (see Figure~): It includes the number of samples, the number of proteins (or peptides) in the dataset, the percentage of missing values and the number of lines which only contain missing values. Once done, the menu displaying the version of the dataset appears and display “Original - peptide” or “Original - protein”, depending on whether the file contains peptide-level or protein-level quantitative information (see Section~). Similarly, the Data processing and Data mining menus become available.

To upload data from tabular file (i.e. stored in a file with one of the following extensions: .txt, .csv, .tsv, .xls, or .xlsx) click on the upper menu then chose .

, go to the tab (see Figure~): Click on the button and select the tabular file of interest (If an Excel file is chosen, a drop-down menu appears to select the spreadsheet containing the data). Once the upload is complete, indicate whether it is a protein level dataset (i.e., each line of the data table should correspond to a single protein) or a peptide-level one. Indicate if the data are already log-transformed or not. If not they will be automatically log-transformed. If the quantification software uses 0 in places of missing values, tick the last option ``Replace all 0 and NaN by NA’’ (as in , 0 is considered a value, not a missing value).

, move on to the tab (see Figure~)
  If the dataset already contains an ID column (a column where each cell has a unique content, which can serve as an ID for the peptides/proteins), select its name in the drop-down menu. Otherwise, it is possible to use the first option of the drop-down menu, that is the , which creates an artificial index. Finally, if the dataset is a peptide-level one, it is in addition important to indicate the column containing the IDs of the parent proteins, so as to prepare for future peptide to protein aggregation.

(see Figure~), referred to as , select the columns which contain the protein abundances (one column for each sample of each condition). To select several column names in a row, click-on on the first one, and click-off on the last one. Alternatively, to select several names which are not continuously displayed, use the key to maintain the selection. If, for each sample, a column of the dataset provides information on the identification method (e.g. by direct MS/MS evidence, or by mapping) check the corresponding tick box. Then, for each sample, select the corresponding column. If none of these pieces of information is given, or, on the contrary, if all of them are specified with a different column name, a green logo appears, indicating it is possible to proceed (however, the content of the specified columns are not checked, so that it is the user’s responsibility to select the correct ones). Otherwise (i.e. the identification method is given only for a subset of samples, or a same identification method is referenced for two different samples), then a red mark appears, indicating some corrections are mandatory.

, (see Figure~). This tab guides the user through the definition of the experimental design. Fill the empty columns with as different names as biological conditions to compare (minimum 2 conditions and 2 samples per condition) and click on . If necessary, correct until the conditions are valid. When achieved, a green logo appears and the sample are reordered according to the conditions. Choose the number of levels in the experimental design (either 1, 2 or 3), and fill the additional column(s) of the table. Once the design is valid (a green check logo appears), move on to the last tab.

, move on to the tab (see Figure~). Provide a name to the dataset to be created and click on the button. As a result, a new MSnset structure is created and automatically loaded. This can be checked with the name of the file appearing in the upper right hand side of the screen, as a title to a new drop-down menu. So far, it only contains `Original - protein'' or`Original - peptide’’, but other versions of the dataset will be added along the course of the processing. Pay attention to any red message appears below the button, which indicates a mistake or an incomplete parameter tuning that must be sorted out before converting the data.

To ease discovery, a “demo mode” is proposed. In this mode, the datasets contained in the package can be directly uploaded to test functionalities. To do so, simply click on in the (Figure~). Note that it possible to display the PDF vignette of the dataset directly from screen (``Dataset documentation (pdf)’’ link).

The menu from the gathers all the functionality to save a dataset in various formats, or to compile the results in a scientific report.

As importing a new dataset from a tabular file is a tedious procedure, we advise to save the dataset as an MSnset binary file right after the conversion (The, it becomes easy to reload it, as detailed in Section~). This makes it possible to restart the statistical analysis from scratch if a problem occurs without having to convert the data another time. Moreover, it is also possible to export the dataset as an Excel spreadsheet (in xlsx format) or as a series of tabulated files grouped in a zipped folder. Any any case, the procedure is similar: First, choose the version of the dataset to be saved. Then, choose the desired file format and provide a file name. Then, click on (Figure~). Once the downloading is over, store the file in the appropriate directory.

The automatic reporting functionalities are under active development. However, they are still in Beta version and amenable to numerous modifications. This vignette will be completed with an exhaustive description of reporting functionality in a near future.

The menu contains the 5 predefined steps of a quantitative analysis. They are designed to be used in a specific order:

For each step, several algorithms or parameters are available, all of them being thoroughly detailed in the sequel of this section.

During each of these steps, it is possible to test several options, and to observe the influence of the processing in the descriptive statistics menu (see Section~), which is dynamically updated.

Finally, once the ultimate tuning is chosen for a given step, it is advised to save the processing. By doing so, another dataset appears in the Dataset versions list (see Section~). Thus, it is possible to go back to any previous step of the analysis if necessary, without starting back the analysis from scratch.

In this step, the user may decide to delete several peptides or proteins according to two criteria: First is the amount of missing values (if it is too important to expect confident processing, see tab 1); Second is string-based filtering (some analyte can be deleted after having been tagged, such as for instance reverse sequences in target-decoy approaches, or known contaminants, see tab 2).

To filter the missing values (first tab called ), the choice of the lines to be deleted is made by different options (see Figure~):

To visualize the effect of the filtering options (without saving the changes or impacting the current dataset), just click on . If the filtering does not produce the expected effect, it is possible to test another one. To do so, one simply has to choose another method in the list and click again on . The plots are automatically updated. This action does not modify the dataset but offers a preview of the filtered data. The user can visualize as many times he/she wants several filtering options.

Afterward, proceed to the , where it is possible to filter out proteins according to information stored in the metadata. To do so: Among the columns constituting the protein metadata listed in the drop-down menu, select the one containing the information of interest (for instance, Contaminant or Reverse). Then, specify in each case the prefix chain of characters that identifies the proteins to filter. Click on to remove the corresponding proteins. A new line appears in the table listing all the filters that have been applied. If other string-based filters must be applied, iterate the same process as many times as necessary.

Once the filtering is appropriately tuned,go to the last tab (called ) (see Figure~), to visualize the set of analytes that have been filtered. Finally, click on . A new dataset is created; it becomes the new current dataset and its name appears in the dropdown menu upper right corner of the screen. All plots and tables available in are automatically updated.

The next processing step proposed by Prostar is data normalization. Its objective is to reduce the biases introduced at any preliminary stage (such as for instance batch effects). offers a number of different normalization routines that are described below.

To visualize the influence of the normalization, three plots are displayed (see Figure~): The first two plots are those of the tab of the (see Section~). The last one depicts the distortion induced by the chosen normalization method on each sample.

Choose the normalization method among the following ones:

Then, for each normalization method, the interface is automatically updated to display the method parameters that must be tuned. Notably, for most of the methods, it is necessary to indicate whether the method should apply to the entire dataset at once (the tuning), or whether each condition should be normalized independently of the others (the tuning).

Other parameters are method specific:

Once the method is correctly parametrized, click on . Observe the influence of the normalization method on the graphs. %Optionally, click on “Show plot options”, so as to tune the graphics for a %better visualization. If the result of the normalization does not correspond to the expectations, change the normalization method or change its tuning. Once the normalization is effective, click on . Check that a new version appears in the dataset version drop-down menu, referred to as or .

Classically, missing values are categorized according to their underlying missingness mechanism: However, it also makes sense to classify the missing value according to the analyte they impact, regardless of the underlying mechanism. This is why, in , it has been decided to separate:

All the missing values for a given protein in a given condition are considered POVs . Alternatively, , the missing values are considered MECs. As a result, each missing values is either POV or MEC. Moreover, for a given protein across several conditions, the missing values can split into POVs and MECs, even though within a same condition they are all of the same type.

With the default color setting, POVs are depicted in light blue and MECs in light orange.

In , the following assumptions are made:

As a whole, it is advised to work at peptide-level rather than protein-level, and to use the refined imputation mechanism of .

:

As a consequence of the previous paragraph, in protein-level datasets, proposes to use an MCAR-devoted imputation algorithm for POVs, and an MNAR-devoted one for MECs.

On the first tab (see Figure~), select the algorithm to impute POV values, among the following ones, and tune its parameter accordingly:

According to our expertise, we advise to select the algorithm from but the other methods can also be of interest in specific situations.

The first distribution plot depicts the mean intensity of each condition conditionally to the number of missing values it contains. It is useful to check that more values are missing in the lower intensity range (due to left censorship).

The heatmap on the right hand side clusters the proteins according to their distribution of missing values across the conditions. Each line of the map depicts a protein. On the contrary, the columns do not depicts the replicates anymore, as the abundance values have been reordered so as to cluster the missing values together. Similarly, the proteins have been reordered, so as to cluster the proteins that have a similar amount of missing values distributed in the same way over the conditions. Each line is colored so as to depicts the mean abundance value within each condition. This heatmap is also helpful to decide what is the main origin of missing values (MCAR or MNAR).

Click on . A short text shows up to summarize the result of the imputation, but the graphics are not updated. However, the next tab is enabled, on which the plots are updated with the imputation results.

After POVs, it is possible to deal with MECs. As a matter of fact, it is always dangerous to impute them, as in absence of any value to rely on, the imputation is arbitrary and risks to spoil the dataset with maladjusted values. As an alternative, it is possible to (1) keep the MEC as is in the dataset, yet, it may possibly impede further processing, (2) discard them at the filter step (see Section~) so as to process them separately. However, this will make it impossible to include these proteins (and their processing) in the final statistics, such as for instance FDR.

For MEC imputation, several methods are available (see Figure~):

Based on our experience, we advise to use algorithm.

Click on . A short text shows up to summarize the result of the imputation, but the graphics are not updated. As the imputation is finished, the updated plots would not be informative, so they are not displayed on the final tab (referred to as ). Note that when displaying or exporting the data, the color code used with missing values is still used for the imputed values, so that at any moment, it is possible to trace which proteins were imputed.

Notice that at peptide level, many lines of the dataset may corresponds to identified peptides with no quantification values. In order to avoid spoiling the dataset with their meaningless imputed values, it is demanded to filter them before proceeding (see Section~).

Two plots are available in order to facilitate the understanding of the missing value distribution. These plots are the same as for protein-level imputation:

The distribution plot on the left depicts the mean intensity of each condition conditionally to the number of missing values it contains. It is useful to check that more values are missing in the lower intensity range (due to left censorship).

The heatmap on the right hand side clusters the proteins according to their distribution of missing values across the conditions. Each line of the map depicts a protein. On the contrary, the columns do not depicts the replicates anymore, as the abundance values have been reordered so as to cluster the missing values together. Similarly, the proteins have been reordered, so as to cluster the proteins that have a similar amount of missing values distributed in the same way over the conditions. Each line is colored so as to depicts the mean abundance value within each condition. This heatmap is also helpful to decide what is the main origin of missing values (MCAR or MNAR).

To impute the missing peptide intensity values, it is either possible to rely on classical methods of the state of the art, or to use . The former ones are available by choosing in the drop-down menu (see Figure~). Another drop-down menu appears proposing one of the following methods: All these methods are applied on all missing values, without diagnosing their nature, as explained in Section~. Alternatively, it is possible to rely on (see Figure~). It works as follows:

It is possible to directly visualize the effect of an imputation method on the updated plots. If the imputation does not produce the expected effect, it is possible to test another one. To do so, one chooses another method in the list and click on . This action does not modify the dataset but offers a preview of the imputed quantitative data. It is possible to visualize as many imputation methods/tuning as desired. Once the correct one is found, one validates the choice by clicking on . Then, a new “imputed” dataset is created and loaded in memory. This new dataset becomes the new current dataset and the “Imputed” version appears in the upper right drop-down menu. All plots and tables in other menus are automatically updated.

This steps only hold for peptide-level dataset. Its purpose is to build a protein-level dataset on the basis of the peptide intensities that have been processed in .

If this step has not been fulfilled at the initial data conversion (see Section~), it is first necessary to specify which column metadata of the peptide-level dataset contains the parent protein IDs (which will be used to index the protein-level dataset that is going to be created).

Once the parent protein IDs are specified, two barplots are displayed (see Figure~). They provide the distribution of proteins according to their number of peptides (either all of them, or only those which are specific to a single protein). These statistics are helpful to understand the distribution of shared peptides, as well as the peptide/protein relationships.

Aggregation requires to tune the following parameters:

Once the aggregation is appropriately tuned, click on .

On the second tab, one selects the columns of the peptide dataset that should be kept in the metadata of the protein dataset (e.g. the sequence of the peptides). For any given parent-protein, the corresponding information of all of its child-peptides will be grouped and stored in the protein dataset metadata. Once done, one clicks on . This creates a new “aggregated” dataset that is directly loaded in memory.

As the new dataset is a protein one, the menu has been disabled. Thus, the interface automatically switches to the homepage. However, the aggregated dataset is accessible and can be analyzed (in the menu) and processed (in the menu).

The aggregation being more computationaly demanding than other processing steps, the current version of does not provide the same flexibility regarding the parameter tuning. Here, it is necessary to save the aggregation result first, then, check the results in the , and possibly to go back to the imputed dataset with the dataset versions dropdown menu to test another aggregation tuning. Contrarily to other processing steps, it is not possible to visualize on-the-fly the consequences of the parameter tuning, and to save it afterward.

Naturally, the output of this step is not a peptide dataset anymore, but a protein dataset. As a result, all the plots available in are deeply modified. Notably, all those which are meaningless at a protein-level are not displayed anymore.

For datasets that do not contain any missing values, or for those where these missing values have been imputed, it is possible to test whether each protein is significantly differentially abundant between the conditions. To do so, click on in the menu (see Figure~).

First, choose the test contrasts. In case of 2 conditions to compare, there is only one possible contrast. However, in case of \(N \geq 3\) conditions, several pairwise contrasts are possible. Notably, it is possible to perform \(N\) tests of the type, or \(N(N-1)/2\) tests of the type. Then, choose the type of statistical test, between limma (see the package) or t-test (either Welch or Student, up to the user’s choice). This makes appear a density plot representing the log fold-change (logFC) (as many density curves on the plot as contrasts). Thanks to the FC density plot, tune the . We advise to tune the logFC threshold conservatively by avoiding discarding to many proteins with it. Moreover, it is important tune the logFC to a small enough value, so as to avoid discarding too many proteins. In fact, it is important to keep enough remaining proteins for the next coming FDR computation step (see Section~), as FDR estimation is more reliable with many proteins, FDR, which relates to a percentage, does not make sense on too few proteins. Finally, run the tests and save the dataset to preserve the results (i.e. all the computed p-values). Then, a new dataset containing the p-values and logFC cut-off for the desired contrasts, can be explored in the tab available in the menu.

Several plots are proposed to help the user have a quick and as complete as possible overview of the dataset. This menu is an essential element for to check that each processing step gives the expected result.

This panel simply displays a table summarizing various quantitative elements on the datasets (see Figure~). It roughly amounts to the data summary that is displayed along with each demo dataset (see Section~).

On the second tab, barplots depicts the distribution of missing values: the left hand side barplot represents the number of missing values in each sample. The different colors correspond to the different conditions (or groups, or labels). The second barplot (in the middle) displays the distribution of missing values; the red bar represents the empty protein count (i.e. the number of lines in the quantitative data that are only made of missing values). The last barplot represents the same information as the previous one, yet, condition-wise. Let us note that protein with no missing values are represented in the last barplot while not on the second one (to avoid a too large Y-scale).

The third tab %is the data explorer (see Figure 4): it makes it possible to view the content of the MSnset structure. It is made of three tables, which can be displayed one at a time thanks to the radio button menu. The first one, named contains the intensity values (see Figure~).

The missing values are represented by colored empty cells. The second one is referred to . It contains all the column dataset that are not the quantitative data (see Figure~).

The third tab, , summarizes the experimental design, as defined when converting the data (see Section~).

In this tab, it is possible to visualize the extent to which the replicates correlate or not (see Figure~). The contrast of the correlation matrix can be tuned thanks to the color scale on the left hand side menu.

A heatmap as well as the associated dendrogram is depicted on the fifth tab (see Figure~). The colors represent the intensities: red for high intensities and green for low intensities. White color corresponds to missing values. The dendrogram shows a hierarchical classification of the samples, so as to check that samples are related according to the experimental design. It is possible to tune the clustering algorithm that produces the dendrogram by adjusting the two parameters of the function:

A Principal Component Analysis visualization is provided by wrapping the package (see Figure~). To better interpret the PCA displays, the reader is referred to the documentation.

These plots shows the distribution of the log-intensity of proteins for each condition (see Figure~).

The left hand plot represents a smoothed histogram of each sample intensity. The right hand side plot display the same information under the form of a boxplot or a violin plot, depending on the user’s choice. In both cases, it is possible to ajust the plot legends, as well as to specify the color code (one color per condition or per sample).

Finally, the last tabs display a density plot of the variance (within each condition) conditionally to the log-intensities (see Figure~). As is, the plot is often difficult to read, as the high variances are concentrated on the lower intensity values. However, it is possible to interactively zoom in on any part of the plot by clicking and dragging (see Figure~).

If one clicks on in the menu, it is possible to analyze the protein-level outputs of all statistical tests (see Section~). Such analysis follows 5 steps, each corresponding to a separate tab in the menu.

On the first tab, select a pairwise comparison of interest. The corresponding volcano plot is displayed. By clicking on a protein in the volcano plot, one or several tables appears below the plot. In any case, a table containing the intensity values of the protein across the samples is displayed, with the same color code as in the Data explorer (for missing values for instance). In addition, if peptide-level information has been uploaded in the session, then, the intensity values of the protein can be linked to the original peptides of the dataset. Thus, the peptides intensities (for both protein-specific and shared peptides) are displayed in the 2 other tables.

Possibly, swap the FC axis with the corresponding tick-box, depending on layout preferences. Possibly, push some p-values to 1, as detailed in Section~ (see Figure~).

Then move on to the next tab, referred to as (see Figure~).
Possibly, tune the calibration method, as as detailed in Section~.

Move on to the next tab and adjust the FDR threshold (see Figure~), which corresponds to the horizontal dashed line on the volcano plot. The checkbox `Show p-value table'' displays a table below the volcano plot, where each line represents a protein along with its p-value and logFC. Moreover, it is indicated by a binary variable if, according to the FDR threshold, the protein is deemed differentially abundant or not. For better visualization, this binary variable also encodes the color code applied to each line. The`Tooltip’’ parameter amounts to the list of the avialable meta-data. It is possible to select several of them. The selected one will (1) appear beside the mouse pointer when flying over each protein point in the volcano plot; (2) add the corresponding column in the table below the volcano plot. This table can be easily exported: The user only has to choose whether all the proteins should appear in the exported table, or only the differentially abundant ones. The volcano plot can be saved thanks to the menu available in the upper right corner of the plot.

Move on to the last tab (referred to as ) to have a comprehensive overview of the differential analysis parameters (see Figure~). If necessary copy/paste this table for later use.

Possibly, go back to the first tab, so as to select another pairwise comparison and process it. Alternatively, it is possible to continue with the current protein list so as to explore the functional profiles of the proteins found statistically differentially abundant between the compared conditions (see Section~).

When working on more than two conditions to compare, the missing value filtering options may be difficult to tune, as a given protein can be well-observed in a group of conditions (with no or few missing values) and largely missing in the remaining conditions. To avoid missing relevant proteins with such a behavior, it is advised to be rather loose at the filtering step (for instance by filtering only proteins that are never seen in the majority of the samples of each condition).

However, with loose filters, proteins with many missing values are kept in the dataset and are then imputed, so as to present a complete intensity distribution for the differential analysis. Therefore, when focusing on a specific comparison among the multiple possible ones, it is thus possible to face proteins which have largely been imputed in the two conditions, so that in practice, one analyses the differential abundance of a protein that is probabibly missing in the two conditions. From an analytical viewpoint, such protein must not appears as differentially abundant, even if its p-value is exceptional, for it is too largely based on imputed values that are not trustworthy.

This is why, the push p-value option is proposed: it makes it possible to force the p-values of these proteins to 1, and prevent them to become false discoveries. Concretely, one simply has to tune parameters of the menu, which are similar to those of the missing value filtering (Section~). However, instead of producing discarding some proteins, it keeps them with a p-value forced to 1 (i.e. a log(p-value) to 0).

As an illustration, Figure~ displays a case were all the proteins with less than 2 observed (i.e. not imputed) values within each condition have their p-values pushed to 1. These proteins appear as points on the horizontal axis of the graph. Notably, the snapshot was made when the mouse pointer had clicked on one of these proteins which p-value was pushed (the one with an intensity value between 5.5 and 6). Its intensity values across the samples appears in the table below, where one observes that 5 out of 6 values are colored as POV or MEC (see Section~). Clearly, such a protein as very poor mass spectrometry evidence, so that even if the imputed values are of different magnitude (leading to a theoretically appealing p-value), it makes sense to discard it.

The primary goal of a calibration plot is to check that the p-values have a distribution that is compliant with FDR computation theory. Given a supposedly known proportion of non-diffentially abundant proteins (generally noted \(\pi_0\)), a good calibration plot is supposed to depict a curve that follows a straight line of slope \(\pi_0\) except for a sharp increase on the right hand side, as depicted on Figure~. The sharp increase, which is the sign of a good discrimination between proteins which are differentially abundant and those which are not, is depicted in green. On the contrary, any bulb of the curve above the \(\pi_0\) line (in blue), anywhere in the middle of the graph is depicted in red, for it is the sign of an insufficient calibration. On Figure~, the calibration deviation is almost immaterial and the sharpness of the right hand side increase is real: both can be noticed thanks to colored caption (Uniformity underestimation relates to possible red bulps and DA protein concentration relates to the sharpness of the green region; the last caption, in blue, being \(\pi_0\)).

However, it is possible to face situations where the right hand side is not sharp enough (see Figure~), or where the entire distribution is ill-calibrated (see Figure~). In such a cases, FDR computation would lead to spurious values and to biologically irrelevant conclusions. However, if the calibration is not too bad, it is possible to compensate it, by overestimating \(\pi_0\). This what the first calibration is made for (see Figure~): several different estimates are depicted by lines of various slope, so as to help the practitioner to choose the most adapted one to the p-value distribution at hand. In the worst case, it is always possible to chose the Benjamini-Hochberg options (corresponding to the safer case of \(\pi_0=1\)), which is not represented on the graph for it always corresponds to a diagonal line.

For more details, the interested reader is referred to the package and the companion publication available in the ``Useful links’’ page of (as well as our tutorial on FDR published by JPR in 2017, also referenced on the same page).

The Gene Ontology (GO, ) is a controlled vocabulary for annotating three biological aspects of gene products. This ontology is made of three parts : Molecular Function (MF), Biological Process (BP) and Cellular Component (CC).

is proposed in the menu. It aims to provide the user with a global view of what is impacted (in a biological point of view) in the experiment, by showing which GO terms are represented (GO classification tab), or over-represented compared to a reference (GO enrichment tab).

relies on the package to perform both GO Classification and GO Enrichment. We propose a GO analysis interface with four separated tabs (see Figure~): The left-hand side of the tab allows it to set the input parameters, namely:

Once these parameters filled, clicking on launches the mapping of the IDs onto the GO categories of the annotation package. Then, on the right-hand side of the panel, the proportion of proteins that cannot be mapped onto the annotation package is indicated (this informative ouput does not interrupt the process, unless no protein maps). Next step is to perform either GO Classification or GO Enrichment (or both).

In the tab (see Figure~), one has to indicate which level(s) of the ontology to consider. Then clicking on the “Perform GO grouping” button launches the analysis (function of the package). The graphics shows the most represented GO categories for a user-defined ontology at (a) user-defined level(s).

The tab (see Figure~) allows it to know which GO categories are significantly enriched in the users list, compared to a chosen reference (‘background’ or ‘universe’). This background can either be :

The enrichment tab calls the function of the package. This function performs a significance test for each category, followed by a multiple test correction at a user-defined level. Concretely, this level is tuned thanks to the “FDR (BH Adjusted \(P\)-value cutoff)” field. Analysis is launched by clicking the button.

Once the analysis has been performed, the result is displayed via two graphics on the right-hand side of the panel (see Figure~). The first one (top) is a barplot showing the five most significant categories. The length of each bar represents the number of proteins within the corresponding category. The second one (bottom) is a dotplot ranked by decreasing , which reads: \[ {\textit{GeneRatio}} = \frac{\#(\mbox{Genes of the input list in this category})} {\#(\mbox{Total number of Genes in the category})}. \]

The last tab is the one. It allows saving the results: GO classification, GO enrichment, or both (see Figure~). Then, a new GOAnalysis dataset is created and loaded in memory.

As usual in Prostar, it is possible to export this new dataset via the menu, either in MSnSet or in Excel format.

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