TITLE: Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research
AUTHORS: Juergen Cox, Stefka Tyanova

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block">Mass spectrometry-based proteomics is a continuously growing field marked by technological and methodological improvements. Cancer proteomics is aimed at pursuing goals such as accurate diagnosis, patient stratification, and biomarker discovery, relying on the richness of information of quantitative proteome profiles. Translating these high-dimensional data into biological findings of clinical importance necessitates the use of robust and powerful computational tools and methods. In this chapter, we provide a detailed description of standard analysis steps for a clinical proteomics dataset performed in Perseus, a software for functional analysis of large-scale quantitative omics data.</div></div>

[STEPS]
?. The Methods section contains several modules covering the most frequently performed steps in the analysis of proteomics data. Often, a proteomics study benefits from a global overview of the data, which usually includes the total number of identified and quantified proteins, dynamic range, coverage of specific pathways, and groups of proteins. A good practice in data analysis is to start with exploratory statistics in order to check for biases in the data, undesirable outliers, and experiments with poor quality data and to make sure that all requirements for performing the subsequent statistical tests are met. Once the data are filtered and normalized appropriately, statistical and bioinformatic analyses are performed in order to identify proteins that are likely to be functionally-important. When the list of such proteins is small enough and direct links to the question of interest can be inferred using prior knowledge, follow-up experiments can be performed after this step to confirm the results of the statistical analysis. However, one of the advantages of mass spectrometry-based proteomics is the ability to unravel new discoveries in an unbiased way, for instance, through functional analysis. This analysis is often based on enrichment tests, which can highlight guiding biological processes and mechanisms.
?. [3.1 Loading the Data]
Go to the “Load” section in Perseus and click the “Generic matrix upload” button.
?. [3.1 Loading the Data]
In the pop-up window, navigate to the file to be loaded.
The input file format of Perseus is a tab-delimited txt file that contains a header row with the names of all columns. The type of data is specified during file loading. Make sure that the “Regional and Language Options” are set to English to avoid errors while reading numerical data such as decimal separators being wrongly interpreted.
?. [3.1 Loading the Data]
Select all the expression columns and transfer them to the Main columns window. Select all additional numerical data that may be needed in the analysis and transfer them to the Numerical columns window. Make sure that the columns containing identifiers (e.g., protein IDs) are selected as Text columns. Click ok.
Different expression and meta data can be imported in Perseus and used for subsequent analysis. Common expression data are in one of the following formats: normalized intensities (e.g., LFQ intensity as described in [4], iBAQ as described in [22]) or ratios between heavy standard and light/non-labeled sample. Other data types that can be analyzed with Perseus are shown in Fig.2.
?. [3.2 Summary Statistics]
In the workflow panel, change the name of the data matrix from matrix 1 to InitialData by right-clicking the node and changing the Alternative name box. Close the pop-up window. Explore the right-most panel of Perseus, which contains useful information such as number of main columns and number of rows.
Get familiar with the Software and its five main sections: Load, Processing, Analysis, Multi-processing, and Export (see Fig.2).
?. [3.2 Summary Statistics]
Go to “Processing ➔ Filter rows ➔ Filter rows based on categorical column” to exclude proteins identified by site, matching to the reverse database or contaminants.
Reverse, identified by site and contaminant proteins have to be marked in a categorical column before these filters can be applied. These are automatically set when MaxQuant output tables are used for analysis in Perseus. Additional filtering options can be used to remove proteins based on a quantitative measure such as a minimum number of quantified peptides or a maximum q-value.
?. [3.2 Summary Statistics]
Transform the data to a logarithmic scale by going to “Processing ➔ Basic ➔ Transform” and specifying the transformation function (e.g., log2(x)).
?. [3.2 Summary Statistics]
In the “Processing” section, select the “Basic” menu and click on the “Summary statistics (columns)” button. Select all expression columns by transferring them to the right-hand side. Click ok and explore the new matrix.
?. [3.3 Filtering]
Use the workflow window to select the InitialData matrix data by clicking on it (see Note 5).
Different activities have different output results including a data matrix with the same expression values and additional columns containing the results of the analysis or a new data matrix containing only the output of an analysis activity. An activity is always performed on the data matrix and specific tab for that matrix that is active at the moment.
?. [3.3 Filtering]
In the “Processing” section, go to the “Filter rows” menu and select “Filter rows based on valid values.” Change the Min. valids parameter to Percentage and keep the default value of70% for the Min. percentage of values parameter. Click ok. Check how many protein groups were retained after the filtering.
Depending on the nature of missing values, different filtering strategies may be employed and are supported in Perseus. For example, if large differences between groups are expected with proteins having very low expression level in one of the groups, filtering based on a minimum number of valid values in at least one group would be a more suitable approach than filtering for a minimum number of valid values in the complete matrix.
?. [3.4 Exploratory Analysis]
To visually inspect the data, go to “Analysis ➔ Visualization ➔ Histograms.” Select all the samples of interest by transferring them to the right-hand side. Click ok.
?. [3.4 Exploratory Analysis]
Explore the visualization options in the Histogram panel by testing the functionality of each of the buttons (e.g.,Properties, Fit width, Fit height).
?. [3.4 Exploratory Analysis]
Click on the pdf button to export the plot.
All the plots can be exported in figure-ready formats such as pdf, tiff, or png.
?. [3.4 Exploratory Analysis]
Switch the view to the “Data” tab.
?. [3.4 Exploratory Analysis]
Go to “Analysis ➔ Visualization ➔ Multi scatter plot.” Select the desired samples by transferring them to the right-hand side. Click ok (see Fig.3).
?. [3.4 Exploratory Analysis]
Adjust the plot using the Fit width and Fit height options and resizing the plot window.
?. [3.4 Exploratory Analysis]
In the drop-down menu “Display in plots” in the plot window, select Pearson correlation.
?. [3.4 Exploratory Analysis]
Select a scatter plot by clicking on it. The selected plot will be shown in an enlarged view.
?. [3.4 Exploratory Analysis]
Select a number of proteins from the “Point” table on the right of the multi scatter plot and examine their position in all pairwise sample comparisons.
?. [3.4 Exploratory Analysis]
Switch back to the “Data” tab to continue with the analysis.
?. [3.4 Exploratory Analysis]
“Go to Processing ➔ Basic ➔ Column correlation.” Make sure that theType is set to Pearson correlation. The output table contains all pairwise correlations between the selected columns.
?. [3.4 Exploratory Analysis]
To visualize the sample correlations, go to “Analysis ➔ Clustering/PCA ➔ Hierarchical clustering.” Use the Change color gradient to set a continuous gradient similar to the one in Fig.3a.
?. [3.4 Exploratory Analysis]
Export the plot by clicking on the pdf button.
?. [3.4 Exploratory Analysis]
Navigate back to the previous data matrix by clicking on it in the workflow panel.
?. [3.4 Exploratory Analysis]
Principal component analysis requires all values to be valid. To remove all protein groups with missing values, repeat Section 3.3, step 2 setting the percentage parameter to100.
Very stringent filtering is usually not recommended, as a large amount of the data will be lost. Instead milder filtering combined with imputation may be more appropriate.
?. [3.4 Exploratory Analysis]
Go to “Analysis ➔ Clustering/PCA ➔ Principal component analysis” and click ok. Explore the sample separation (dot plot in the upper panel) and the corresponding loadings (dot plot in the lower panel).
?. [3.4 Exploratory Analysis]
In the table on the right of the PCA plot, select a set of samples (e.g., all samples that belong to one experimental condition) and change their color by clicking on the Symbol color button and selecting the desired color.
?. [3.4 Exploratory Analysis]
Check the contribution of other components by substituting Component 1 and 2 with other components from the drop-down menu. Find the components that show sample separation according to the experimental conditions (see Fig.3c).
?. [3.4 Exploratory Analysis]
Explore the proteins driving this separation. In the loadings plot beneath the PCA, change the selection Mode to rectangular selection. Hold the left mouse key down and draw a rectangle around the dots in the upper right corner and then release the mouse. The selected proteins are highlighted in the table to the right and their labels are displayed in the plot.
?. [3.5 Normalization]
Navigate back to the data matrix before filtering for 100% valid values (Section 3.3, step 2).
?. [3.5 Normalization]
Go to “Processing ➔ Normalization ➔ Z-score.” Change the Matrix access parameter to Columns and select the Use median option. In the new data table, plot histograms for the same subset of samples as in Section 3.4, step 1.
Data normalization is not always necessary. Different types of normalization can be applied on the data to correct for systematic shifts or skewness and to make samples comparable.
?. [3.6 Experimental Design]
Go to “Processing ➔ Annot. rows ➔ Categorical annotation rows.” Use the Create action option to manually specify the experimental condition to which a sample belongs (i.e., indicate control versus stimulus, or different stages of a disease). All the samples belonging to one condition should have the same annotation. A new row will be added under the column names in the newly generated data matrix.
Regular expressions can be used to derive the experimental design from the sample names (“Action ➔ Create from experiment name”). Additionally, a template txt file can be written out, edited in an external editor program, and read in to indicate the experimental design.
?. [3.7 Loading Annotations]
Go to the drop-down menu indicated with a white arrow at the top left corner of Perseus and select “Annotation download.”
?. [3.7 Loading Annotations]
Click on the link in the pop-up window. Select the appropriate annotation file (e.g., “PerseusAnnotaion ➔ FrequentlyUsed ➔ mainAnnot.homo_sapiens.txt.gz,” if the organism of interest is homo sapiens).
?. [3.7 Loading Annotations]
Download the file to the Perseus/conf/annotations folder.
?. [3.7 Loading Annotations]
Go to “Processing ➔ Annot. columns ➔ Add annotation.” Select the file from the previous step as a Source.
?. [3.7 Loading Annotations]
Set the UniProt column parameter to the column that contains UniProt identifiers. These identifiers will be used for overlaying the annotation data with the expression matrix (e.g., Protein IDs).
?. [3.7 Loading Annotations]
Select several categories of interest to be overlaid with the main matrix and move them to the right-hand side. Click ok.
?. [3.8 Differential Expression Analysis]
Go to “Processing ➔ Tests.” From the menu select the appropriate test. For the data set used in this chapter, the Multiple-sample tests option should be chosen, as there are more than two conditions that are compared. The default parameters do not have to be changed.
Analysis of differentially expressed proteins depends on the number of compared conditions, the underlying distribution properties, and the availability of biological replicates. For example, data sets with one condition with replicates should be analyzed withOne-sample tests, with two conditions—withTwo-sample tests, and with more than two conditions—Multiple-sample tests. Paired samples test and tests abolishing the requirement for equal variance are also available.
?. [3.8 Differential Expression Analysis]
Specify the categorical row that contains information about the experimental conditions of the samples that will be used in the differential analysis in the Grouping parameter.
?. [3.8 Differential Expression Analysis]
Keep the default value of 0 for the S0 parameter, to use the standard t-test statistic. Change the parameter to use the modified test statistic approach described by Tusher et al. [15].
?. [3.8 Differential Expression Analysis]
Select the multiple hypothesis testing correction method to be used by specifying the Use for truncation parameter (see Note below and Fig.4a).
The method with largest powerPermutation-based FDR is recommended and at least 250 repetitions are suggested. In case of technical replicates, these have to be specified as a separate grouping (see Section 3.7,step 1) and selected with the “Preserve grouping in randomizations” option. Failure to specify technical replicates will result in wrong FDR calculation. The more conservative Benjamini-Hochberg correction can also be used when a lower number of Type I errors at the cost of lower sensitivity are desired.
?. [3.8 Differential Expression Analysis]
Specify if a suffix should be added to the output columns produced by Perseus. This option is relevant when multiple tests are conducted, e.g., with different parameter settings, as it helps to distinguish between them in the output table.
?. [3.8 Differential Expression Analysis]
Inspect the output table. It contains three new columns:ANOVA significant,−Log ANOVA p-value, and ANOVA q-value.
The “Significant” column contains a “+” if a protein met the selected significance threshold (usuallyq-value). Additionally,p-values (probability of type I error) and the correspondingq-values (correctedp-value) are provided in the output table.
?. [3.8 Differential Expression Analysis]
Go to “Processing ➔ Filter rows ➔ Filter rows based on categorical column.” Set the Column parameter to ANOVA Significant and the Mode parameter to Keep matching rows to retain all differentially expressed proteins.
?. [3.8 Differential Expression Analysis]
Go to “Processing ➔ Tests ➔ Post-hoc tests.” Set theGroupingparameter to the same grouping that was used for the ANOVA test (see Section 3.6, step 1) and the FDR to the desired threshold. Tukey’s honestly significant difference (THSD) is computed for all proteins and all pairwise comparisons and the significant hits within the corresponding pairs are marked (see Note below and Fig.4b).
Tukey’s honestly significant difference (THSD) is a post-hoc test that when performed on ANOVA significant hits determines in exactly which pairwise group comparisons a given protein is differentially expressed.
?. [3.9 Clustering and Profile Plots]
Go to “Analysis ➔ Clustering/PCA ➔ Hierarchical clustering.” Keep the default parameters and click ok.
?. [3.9 Clustering and Profile Plots]
Inspect the resulting heatmap and the relationship between the groups and the proteins.
?. [3.9 Clustering and Profile Plots]
Click on the Change color gradient button in the button ribbon above the heatmap to examine the color scale usage (red means high and green low expression) and to modify them.
?. [3.9 Clustering and Profile Plots]
Click on several node junctions in the protein tree that represent potentially interesting clusters of proteins (i.e., upregulation in a certain experimental condition). The selected clusters are highlighted and appear in the “Row clusters” table displayed to the right of the heatmap.
Clusters can be defined by clicking on the respective nodes in the protein tree or based on the precise distance measure used to compute the tree. To use the latter option, click on the “Define row clusters” button and specify the desired number of clusters, which will then be automatically defined.
?. [3.9 Clustering and Profile Plots]
Inspect the different profile plots as you navigate through the different clusters in the table. Change the color by modifying the Color scale and export the profile plots by clicking on the Export image button (see Fig.5).
?. [3.9 Clustering and Profile Plots]
From the ribbon menu in the heat map view, click on the Export row clustering button to add the cluster information to a new data matrix.
?. [3.10 Functional Analysis]
Go to “Multi-proc. ➔ Matching rows by name.” Both Base and Other matrices point to the last matrix.
?. [3.10 Functional Analysis]
Click on Base matrix and then in the workflow window select the data matrix that was generated before filtering for ANOVA significant hits (Section 3.9, step 6).
?. [3.10 Functional Analysis]
In the pop-up window set Matching column in matrix 1 and 2 to a common identifier (e.g., Protein IDs).
?. [3.10 Functional Analysis]
In the categorical columns section, transfer the category Cluster to the right hand-side. Click ok.
The matching step is necessary in order to define the correct background against which enrichment will be computed. Too small (only significant hits) or too large (the complete proteome, even if not detected with MS analysis) introduces bias in the enrichment results.
?. [3.10 Functional Analysis]
Go to “Processing ➔ Annot. columns ➔ Fisher exact test.” Change the Column parameter to Cluster and click ok. The resulting table contains information about all annotation categories that were found to be significantly enriched or depleted using a Fisher’s exact test and multiple hypotheses correction.
The enrichment output table contains information about the values used to compute the contingency table for the Fisher’s exact test (e.g., category and intersection size), the enrichment factor, and the statistical significance of the hit indicated by ap-value and the associated false discovery rate.
In summary, this chapter provides a complete protocol for fundamental analysis of proteomic data, starting from data upload and transformation and ending with identification of proteins, characteristic of the specific disease progression stage, and the underlying processes in which they are involved. The described analytical methods and visualization tools are integrated in Perseus, a freely available platform for analysis of omics data, which provides a comprehensive portfolio of analysis tools with a user-friendly interface [19]. A special emphasis is placed on employing statistically sound methods in the analysis of large data, avoiding wrong interpretation and extracting maximum information. More advanced computational techniques such as supervised learning are also supported and are often instrumental for the analysis of complex data where genetic and intra-tumor variability may pose challenges. Moreover, Perseus is being continuously developed to integrate analysis of various data types, including posttranslation modifications, sequence information, as well as to allow deeper functional interpretation through network and pathways analysis.