--- title: "MSstats: End to End Workflow" date: September 5th, 2024 --- ```{r style, echo = FALSE, results = 'asis'} BiocStyle::markdown() ``` ```{r global_options, include=FALSE} knitr::opts_chunk$set(fig.width=10, fig.height=7, warning=FALSE, message=FALSE) options(width=110) ``` ```{=html} ``` # __MSstats: Protein/Peptide significance analysis__ Package: MSstats Author: Anshuman Raina & Devon Kohler Date: 5th Semptember 2024 ## __Introduction__ `MSstats`, an R package in Bioconductor, supports protein differential analysis for statistical relative quantification of proteins and peptides in global, targeted and data-independent proteomics. It handles shotgun, label-free and label-based (universal synthetic peptide-based) SRM (selected reaction monitoring), and DIA (data independent acquisition) experiments. It can be used for experiments with complex designs (e.g. comparing more than two experimental conditions, or a repeated measure design, such as a time course). This vignette summarizes the introduction and various options of all functionalities in `MSstats`. More details are available in `User Manual`. For more information about the MSstats workflow, including a detailed description of the available processing options and their impact on the resulting differential analysis, please see the following publication: Kohler et al, Nature Protocols 19, 2915–2938 (2024). ## __Installation__ To install this package, start R (version “4.0”) and enter: ``` {r code Installation} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("MSstats") library(MSstats) library(ggplot2) ``` ## __1. Workflow__ ### __1.1 Raw Data__ To begin with, we will load sample datasets, including both annotated and plain data. The dataset you need can be found [here](https://github.com/Vitek-Lab/MSstatsConvert/blob/devel/inst/tinytest/raw_data/PD/pd_input.csv). We will also load the Annotation Dataset using MSstatsConvert. You can access this dataset [here](https://github.com/Vitek-Lab/MSstatsConvert/blob/devel/inst/tinytest/raw_data/PD/annot_pd.csv). ``` {r code Load Dataset} library(MSstats) # Load data pd_raw = system.file("tinytest/raw_data/PD/pd_input.csv", package = "MSstatsConvert") annotation_raw = system.file("tinytest/raw_data/PD/annot_pd.csv", package = "MSstatsConvert") pd = data.table::fread(pd_raw) annotation = data.table::fread(annotation_raw) head(pd, 5) head(annotation, 5) ``` ### __1.2 Loading PD Data to MSstats__ The imported data from Step 1.1. now must be converted through `MSstatsConvert` package's `PDtoMSstatsFormat` converter. This function converts the Proteome Discoverer output into the required input format for `MSstats`. Actual data modification can be seen below: ```{r code PDtoMSstatsFormat} library(MSstatsConvert) pd_imported = MSstatsConvert::PDtoMSstatsFormat(pd, annotation, use_log_file = FALSE) head(pd_imported) ``` ### __1.3 Converters__ We have the following converters, which allow you to convert various types of output reports which include the feature level data to the required input format of `MSstats`. Further information about the converters can be found in the `MSstatsConvert` package. 1. `DIANNtoMSstatsFormat` 2. `DIAUmpiretoMSstatsFormat` 3. `FragPipetoMSstatsFormat` 4. `MaxQtoMSstatsFormat` 5. `OpenMStoMSstatsFormat` 6. `OpenSWATHtoMSstatsFormat` 7. `PDtoMSstatsFormat` 8. `ProgenesistoMSstatsFormat` 9. `SkylinetoMSstatsFormat` 10. `SpectronauttoMSstatsFormat` 11. `MetamorpheusToMSstatsFormat` We show an example of how to use the above said Converters. For more information about using the individual converters please see the coresponding documentation. ```{r Converter Files} skyline_raw = system.file("tinytest/raw_data/Skyline/skyline_input.csv", package = "MSstatsConvert") skyline = data.table::fread(skyline_raw) head(skyline, 5) ``` ```{r SkylinetoMSstatsFormat, results='hide', message=FALSE, warning=FALSE} msstats_format = MSstatsConvert::SkylinetoMSstatsFormat(skyline_raw, qvalue_cutoff = 0.01, useUniquePeptide = TRUE, removeFewMeasurements = TRUE, removeOxidationMpeptides = TRUE, removeProtein_with1Feature = TRUE) ``` ```{r SkylinetoMSstatsFormat head} head(msstats_format) ``` ### __1.4 Data Process__ Once we import the dataset correctly with Converter, we need to pre-process the data which is done by the `dataProcess` function. This step involves data processing and quality control of the measured feature intensities. This function includes 5 main processing steps (with other additional small steps): * __Log transformation__ - Transform the feature intensities from their original scale to the log scale. This step helps make the data closer to being normally distributed, requiring less replicates for the central limit theorem to kick in. * __Normalization__ - There are three different normalization options supported. 'equalizeMedians' (default) represents constant normalization (equalizing the medians) based on reference signals is performed. 'quantile' represents quantile normalization based on reference signals is performed. 'globalStandards' represents normalization with global standards proteins. FALSE represents no normalization is performed. * __Feature selection__ - This also has three options i.e. Select All features, Top-N features (by mean intensity) or “Best” features. * __Missing value imputation__ - We impute plausible values in case of missing data points. The RunLevelData can be queried to show Number of imputed intensities (censored intensities) in a RUN and Protein. * __Summarization__ - After data processing the individual features are summarized up to the protein-level using Tukey's Median Polish. Linear summarization is also available as an option. ``` {r code dataProcess} summarized = dataProcess( pd_imported, logTrans = 2, normalization = "equalizeMedians", featureSubset = "all", n_top_feature = 3, summaryMethod = "TMP", equalFeatureVar = TRUE, censoredInt = "NA", MBimpute = TRUE ) head(summarized$FeatureLevelData) head(summarized$ProteinLevelData) head(summarized$SummaryMethod) ``` ### __1.4.1 Data Processing Options__ Reference: [Kohler et al. 2024](https://www.nature.com/articles/s41596-024-01000-3#Sec20) #### Normalization Four options for normalization are included in MSstats: median, quantile, global standards and no normalization. There is no single best normalization for all experiments. Researchers must consider the assumptions underlying each normalization option and the appropriateness of the assumptions for their study. Below, we summarize the normalization options, their assumptions and the effect on downstream statistical analysis. ```{r echo=FALSE, message=FALSE} library(kableExtra) table_data <- data.frame( Name = c("Median", "", "Quantile", "", "Global standards", "", "", "None", ""), Description = c( "Equalize medians of all log feature intensities in each run", "", "Equalize the distributions of all log feature intensities in each run", "", "Equalize median log-intensities of endogenous or spiked-in reference peptides or proteins. Apply adjustment to the remainder of log feature intensities", "", "", "Do not apply any normalization", "" ), Assumption = c( "All steps of data collection and acquisition were randomized", "Most of the proteins in the experiment are the same and have the same concentration for all of the runs. The experimental artifacts affect every peptide in a run by the same constant amount", "All steps of data collection and acquisition were randomized", "Most of the proteins in the experiment are the same and have the same concentration for all of the runs. The experimental artifacts affect every peptide non-linearly, as a function of its log intensity", "All steps of data collection and acquisition were randomized", "The reference peptides or proteins are present in each run and have the same concentration for all of the runs. All experimental artifacts occur only after standards were added.", "The experimental artifacts affect every protein in a run by the same constant amount", "All steps of data collection and acquisition were randomized", "The experiment has no systematic artifacts or has been normalized in another custom manner" ), Effect = c( "The normalization estimates the artifact deviations in each run with a single quantity, reducing overfitting", "The normalization reduces bias and variance of the estimated log fold change", "The normalization estimates the artifact deviations in each run with a complex non-linear function, potentially leading to overfitting", "The normalization reduces bias and variance of the estimated log fold change but may over-correct", "The normalization estimates the artifact deviations in each run with a single quantity, which reduces overfitting", "The normalization estimates the artifact deviations from a small number of peptides, which may increase overfitting. The normalization does not eliminate artifacts that occurred before adding spiked references", "The normalization reduces bias and variance of the estimated log fold change", "All patterns of variation of interest and of nuisance variation are preserved", "" ) ) tryCatch({ kable(table_data, "html", escape = FALSE, col.names = c("Name", "Description", "Assumption", "Effect")) %>% kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover")) }, error = function(e) { stop(paste0("Error in rendering the normalization options table: ", e$message)) }) ``` If the assumptions of the normalization are not verified, the normalization may, in fact, increase bias or variance of the estimated log fold change. For example, if the experiment is not randomized and the experimental artifacts are confounded with the conditions, the median and quantile normalizations will introduce bias. #### Feature Selection Feature selection is used to determine which protein features should be used to infer the overall protein abundance in a sample. The options here are: - Using all features - Using the top ‘N’ features - Removing uninformative features and outliers Using all features will simply leverage all available information to infer the underlying protein abundance. Top ‘N’ features selects a pre-specified number of features with the highest average intensity across all runs for protein-level inference. This option is useful if you believe that the features with lower average intensity are less reliable, or in cases in which some of the proteins have a very large number of features (such as in DIA experiments). For any individual protein, it is usually possible to determine changes in abundance by looking at the peaks with highest intensity; in these cases, using all features results in redundancy while greatly increasing the computational processing time. Finally, removing uninformative features and outliers attempts to select the ‘best’ features by removing features that have too many missing values, that are too noisy or have outliers. #### Missing Value Imputation Missing value imputation attempts to infer feature intensities in runs in which they were not measured. MSstats imputes these values by using an accelerated failure time model ```{r echo=FALSE, message=FALSE} imputation_table <- data.frame( Name = c("Imputation", "No imputation"), Description = c( "Infer missing feature intensities by using an accelerated failure time model. It will not impute for runs in which all features are missing", "Do not apply imputation" ), Assumption = c( "Features are missing for reasons of low abundance (e.g., features are missing not at random)", "Assume no information about reasons for missingness or that features are missing at random" ), Effect = c( "If the assumption is true, imputation will remove bias toward high intensities in the summarization step. Otherwise, bias will be introduced via inaccurate imputation", "If the assumption is true, no new bias will be introduced. Otherwise, if features are missing for reasons of low abundance, summarized values will be biased toward high intensities" ) ) tryCatch({ kable(imputation_table, "html", escape = FALSE, col.names = c("Name", "Description", "Assumption", "Effect")) %>% kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed")) %>% column_spec(2:4, width = "30em") }, error = function(e) { stop(paste0("Error in rendering the imputation options table: ", e$message)) }) ``` ### __1.4.2 Data Process Plots__ After processing the input data, `MSstats` provides multiple plots to analyze the results. Here we show the various types of plots we can use. By default, a pdf file will be downloaded with corresponding feature level data and the Plot generated. Alternatively, the `address` parameter can be set to `FALSE` which will output the plots directly. ```{r dataProcessPlots, results='hide', message=FALSE, warning=FALSE} # Profile plot dataProcessPlots(data=summarized, type="ProfilePlot", address = FALSE, which.Protein = "P0ABU9") # Quality control plot dataProcessPlots(data=summarized, type="QCPlot", address = FALSE, which.Protein = "P0ABU9") # Quantification plot for conditions dataProcessPlots(data=summarized, type="ConditionPlot", address = FALSE, which.Protein = "P0ABU9") ``` ### __1.5 Modeling __ In this step we test for differential changes in protein abundance across conditions using a linear mixed-effects model. The model will be automatically adjusted based on your experimental design. A contrast matrix must be provided to the model. Alternatively, all pairwise comparisons can be made by passing `pairwise` to the function. For more information on creating contrast matrices, please see the citation linked at the beginning of this document. ``` {r code groupComparison} model = groupComparison("pairwise", summarized) ``` Model Details ``` {r Model } head(model$ModelQC) head(model$ComparisonResult) ``` ### __1.5.1 groupComparisonPlot__ Visualization for model-based analysis and summarizing differentially abundant proteins. To summarize the results of log-fold changes and adjusted p-values for differentially abundant proteins, `groupComparisonPlots` takes testing results from function `groupComparison` as input and automatically generate three types of figures in pdf files as output : * __Volcano plot__ : For each comparison separately. It illustrates actual log-fold changes and adjusted p-values for each comparison separately with all proteins. The x-axis is the log fold change. The base of logarithm transformation is the same as specified in “logTrans” from `dataProcess`. The y-axis is the negative log2 or log10 adjusted p-values. The horizontal dashed line represents the FDR cutoff. The points below the FDR cutoff line are non-significantly abundant proteins (colored in black). The points above the FDR cutoff line are significantly abundant proteins (colored in red/blue for up-/down-regulated). If fold change cutoff is specified (FCcutoff = specific value), the points above the FDR cutoff line but within the FC cutoff line are non-significantly abundant proteins (colored in black). * __Heatmap__ : For multiple comparisons. It illustrates up-/down-regulated proteins for multiple comparisons with all proteins. Each column represents each comparison of interest. Each row represents each protein. Color red/blue represents proteins in that specific comparison are significantly up-regulated/down-regulated proteins with FDR cutoff and/or FC cutoff. The color scheme shows the evidences of significance. The darker color it is, the stronger evidence of significance it has. Color gold represents proteins are not significantly different in abundance. * __Comparison plot__ : For multiple comparisons per protein. It illustrates log-fold change and its variation of multiple comparisons for single protein. X-axis is comparison of interest. Y-axis is the log fold change. The red points are the estimated log fold change from the model. The error bars are the confidence interval with 0.95 significant level for log fold change. This interval is only based on the standard error, which is estimated from the model. ``` {r GroupComparisonPlots} groupComparisonPlots( model$ComparisonResult, type="Heatmap", sig = 0.05, FCcutoff = FALSE, logBase.pvalue = 10, ylimUp = FALSE, ylimDown = FALSE, xlimUp = FALSE, x.axis.size = 10, y.axis.size = 10, dot.size = 3, text.size = 4, text.angle = 0, legend.size = 13, ProteinName = TRUE, colorkey = TRUE, numProtein = 100, clustering = "both", width = 800, height = 600, which.Comparison = "all", which.Protein = "all", address = FALSE, isPlotly = FALSE ) groupComparisonPlots( model$ComparisonResult, type="VolcanoPlot", sig = 0.05, FCcutoff = FALSE, logBase.pvalue = 10, ylimUp = FALSE, ylimDown = FALSE, xlimUp = FALSE, x.axis.size = 10, y.axis.size = 10, dot.size = 3, text.size = 4, text.angle = 0, legend.size = 13, ProteinName = TRUE, colorkey = TRUE, numProtein = 100, clustering = "both", width = 800, height = 600, which.Comparison = "Condition2 vs Condition4", which.Protein = "all", address = FALSE, isPlotly = FALSE ) ``` ### __1.6 GroupComparisonQCPlots__ To check and verify that the resultant data of `groupComparison` offers a linear model for whole plot inference, `groupComparisonQC` plots take the fitted data and provide two ways of plotting: 1. Normal Q-Q plot : Quantile-Quantile plots represents normal quantile-quantile plot for each protein after fitting models 2. Residual plot : represents a plot of residuals versus fitted values for each protein in the dataset. Results based on statistical models for whole plot level inference are accurate as long as the assumptions of the model are met. The model assumes that the measurement errors are normally distributed with mean 0 and constant variance. The assumption of a constant variance can be checked by examining the residuals from the model. ``` {r GroupComparisonQCplots, results='hide', message=FALSE, warning=FALSE} source("..//R//groupComparisonQCPlots.R") groupComparisonQCPlots(data=model, type="QQPlots", address=FALSE, which.Protein = "P0ABU9") groupComparisonQCPlots(data=model, type="ResidualPlots", address=FALSE, which.Protein = "P0ABU9") ``` ### __1.7 Sample Size Calculation__ Calculate sample size for future experiments of a Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment based on intensity-based linear model. The function fits the model and uses variance components to calculate sample size. The underlying model fitting with intensity-based linear model with technical MS run replication. Estimated sample size is rounded to 0 decimal. Two options of the calculation: * number of biological replicates per condition * power ```{r Sample Size} sample_size_calc = designSampleSize(model$FittedModel, desiredFC=c(1.75,2.5), power = TRUE, numSample=5) ``` ### __1.7.1 Sample Size Calculation Plot__ To illustrate the relationship of desired fold change and the calculated minimal number sample size which are The input is the result from function `designSampleSize`. ```{r Sample Size plot} designSampleSizePlots(sample_size_calc, isPlotly=FALSE) ``` ### __1.8 Quantification from groupComparison Data__ Model-based quantification for each condition or for each biological samples per protein in a targeted Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment. Quantification takes the processed data set by `dataProcess` as input and automatically generate the quantification results (data.frame) with long or matrix format. The quantification for endogenous samples is based on run summarization from subplot model, with TMP robust estimation. * Sample quantification : individual biological sample quantification for each protein. The label of each biological sample is a combination of the corresponding group and the sample ID. If there are no technical replicates or experimental replicates per sample, sample quantification is the same as run summarization from `dataProcess` (`RunlevelData` from `dataProcess`). If there are technical replicates or experimental replicates, sample quantification is median among run quantification corresponding MS runs. * Group quantification : quantification for individual group or individual condition per protein. It is median among sample quantification. ```{r Quantification} sample_quant_long = quantification(summarized, type = "Sample", format = "long") sample_quant_long sample_quant_wide = quantification(summarized, type = "Sample", format = "matrix") sample_quant_wide group_quant_long = quantification(summarized, type = "Group", format = "long") group_quant_long group_quant_wide = quantification(summarized, type = "Group", format = "matrix") group_quant_wide ```