Applied Bioinformatics 2025�Week 3 Session 1�Introduction to MSstats
Natalie Turner, PhD
Postdoctoral Fellow – Yates Lab
Department of Molecular Medicine
naturner@scripps.edu
MSstats and MSstatsConvert
MSstats family packages works with label-free, Selected Reaction Monitoring (SRM) and Tandem Mass Tag (TMT) datasets
Takes MS raw quant data as input (compatible with multiple upstream processing software tools)
MSstatsConvert enables reformatting of virtually any MS quantification result into a format required by MSstats
e.g., ‘DIANNtoMSstatsFormat’ takes the results file from DIANN and formats/cleans it for further processing in MSstats
Features
Peptide Spectral Match (PSM)- and protein-level filtering,
managing shared peptides,
removing decoy, iRT, and other irrelevant sequences,
removing features or proteins with a low number of measurements,
aggregating duplicated measurements,
handling fractionation by removing overlapped features,
creating balanced statistical design in the presence of missing data.
DIANNtoMSstatsFormat
input | name of MSstats input report from Diann, which includes feature-level data. |
annotation | name of 'annotation.txt' data which includes Condition, BioReplicate, Run. |
global_qvalue_cutoff | The global qvalue cutoff |
qvalue_cutoff | local qvalue cutoff for library |
pg_qvalue_cutoff | local qvalue cutoff for protein groups. Run should be the same as filename. |
useUniquePeptide | should unique peptides be removed |
removeFewMeasurements | should proteins with few measurements be removed |
removeOxidationMpeptides | should peptides with oxidation be removed |
removeProtein_with1Feature | should proteins with a single feature be removed |
use_log_file | logical. If TRUE, information about data processing will be saved to a file. |
append | logical. If TRUE, information about data processing will be added to an existing log file. |
verbose | logical. If TRUE, information about data processing wil be printed to the console. |
log_file_path | character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file. |
MBR | True if analysis was done with match between runs |
quantificationColumn | Use 'FragmentQuantCorrected'(default) column for quantified intensities. 'FragmentQuantRaw' can be used instead. |
dataProcess: Clean, normalize and summarize before differential analysis�
raw | name of the raw (input) data set. |
logTrans | base of logarithm transformation: 2 (default) or 10. |
normalization | normalization to remove systematic bias between MS runs. There are three different normalizations supported: 'equalizeMedians' (default) represents constant normalization (equalizing the medians) based on reference signals is performed. 'quantile' represents quantile normalization based on reference signals 'globalStandards' represents normalization with global standards proteins. If FALSE, no normalization is performed. |
featureSubset | "all" (default) uses all features that the data set has. "top3" uses top 3 features which have highest average of log-intensity across runs. "topN" uses top N features which has highest average of log-intensity across runs. It needs the input for n_top_feature option. "highQuality" flags uninformative feature and outliers. |
remove_uninformative_feature_outlier | optional. Only required if featureSubset = "highQuality". TRUE allows to remove 1) noisy features (flagged in the column feature_quality with "Uninformative"), 2) outliers (flagged in the column, is_outlier with TRUE, before run-level summarization. FALSE (default) uses all features and intensities for run-level summarization. |
min_feature_count | optional. Only required if featureSubset = "highQuality". Defines a minimum number of informative features a protein needs to be considered in the feature selection algorithm. |
n_top_feature | optional. Only required if featureSubset = 'topN'. It that case, it specifies number of top features that will be used. Default is 3, which means to use top 3 features. |
dataProcess: Clean, normalize and summarize before differential analysis�
summaryMethod | "TMP" (default) means Tukey's median polish, which is robust estimation method. "linear" uses linear mixed model. |
equalFeatureVar | only for summaryMethod = "linear". default is TRUE. Logical variable for whether the model should account for heterogeneous variation among intensities from different features. Default is TRUE, which assume equal variance among intensities from features. FALSE means that we cannot assume equal variance among intensities from features, then we will account for heterogeneous variation from different features. |
censoredInt | Missing values are censored or at random. 'NA' (default) assumes that all 'NA's in 'Intensity' column are censored. '0' uses zero intensities as censored intensity. In this case, NA intensities are missing at random. The output from Skyline should use '0'. Null assumes that all NA intensites are randomly missing. |
MBimpute | only for summaryMethod = "TMP" and censoredInt = 'NA' or '0'. TRUE (default) imputes 'NA' or '0' (depending on censoredInt option) by Accelated failure model. FALSE uses the values assigned by cutoffCensored. |
remove50missing | only for summaryMethod = "TMP". TRUE removes the proteins where every run has at least 50% missing values for each peptide. FALSE is default. |
fix_missing | Optional, same as the 'fix_missing' parameter in MSstatsConvert::MSstatsBalancedDesign function |
maxQuantileforCensored | Maximum quantile for deciding censored missing values, default is 0.999 |
numberOfCores | Number of cores for parallel processing. When > 1, a logfile named 'MSstats_dataProcess_log_progress.log' is created to track progress. Only works for Linux & Mac OS. Default is 1. |
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