2. Statistical analysis with MSqRob for simple designs

The result of a quantitative analysis is a list of peptide and/or protein abundances for every protein in different samples, or abundance ratios between the samples. In this chapter we will describe a generic workflow for differential analysis of quantitative datasets with simple experimental designs. In order to extract relevant information from these massive datasets, we will use the MSqRob Shiny GUI, a graphical interface that allows for straightforward data inspection and robust relative protein quantification [1]. The material in this tutorial is partially based on our paper Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob [2].

2.1 Basic Statistical Concepts

The actual design of an experiment strongly impacts the data analysis and its power to discover differentially abundant proteins. Therefore, we first cover some basic concepts on experimental design. Next, we provide a general step-by-step overview of a typical quantitative proteomics data analysis workflow. The monthly column “Points of significance” in Nature Methods is a useful primer on statistical design for researchers in life sciences to which we extensively refer in this section (http://www.nature.com/collections/qghhqm/pointsofsignificance). For proteomics experiments it is important to differentiate between experimental units and observational units. Experimental units are the subjects/objects on which one applies a given treatment, often also denoted as biological repeats. In a proteomics experiment, the number of experimental units is typically rather limited (e.g. three biological repeats of a knockout and a wild-type sample). The measurements, however, are applied on the observational units. In a shotgun proteomics experiment, these are the individual peptide intensities. For many proteins, there are thus multiple observations/peptide intensities for each experimental unit, which can be considered as technical replicates or pseudo-replicates [3]. Hence, one can make very precise estimates on the technical variability of the intensity measurements; i.e. how strongly intensity measurements fluctuate for a particular protein in a particular sample. However, the power to generalize the effects observed in the sample to the whole population remains limited as most biological experiments typically only have a limited number of biological repeats [4]. We thus strongly advise researchers to think upfront about their experimental design and to maximize the number of biological repeats (we suggest at least three biological repeats, and preferably more).

2.2 The CPTAC A vs B dataset

Our first case-study is a subset of the data of the 6th study of the Clinical Proteomic Technology Assessment for Cancer (CPTAC). In this experiment, the authors spiked the Sigma Universal Protein Standard mixture 1 (UPS1) containing 48 different human proteins in a protein background of 60 ng/μL Saccharomyces cerevisiae strain BY4741 (MATa, leu2Δ0, met15Δ0, ura3Δ0, his3Δ1). Two different spike-in concentrations were used: 6A (0.25 fmol UPS1 proteins/μL) and 6B (0.74 fmol UPS1 proteins/μL) [5]. The raw data files can be downloaded from https://cptac-data-portal.georgetown.edu/cptac/public?scope=Phase+I (Study 6), the processed data can be downloaded by zipping the github repository https://github.com/statOmics/SGA2019/tree/data, in the folder data/quantification/cptacAvsB_lab3. We limited ourselves to the data of LTQ-Orbitrap W at site 56. The data were searched with MaxQuant version 1.5.2.8, and detailed search settings were described in Goeminne et al. (2016) [1]. Three replicates are available for each concentration.

[2.3.a] cptac.html is a script with the entire workflow when median summarisation is adopted. Study the script and try to understand each of the analysis steps. We know the real FC for the spike in proteins and the yeast proteins (see description of the data). What do you observe?

[2.3.b] Repeat the analysis for the robust summarization method. What do you observe, how does that compare to the median summarisation and try to explain this?

[2.3.c] Repeat the analysis using MaqLFQ summarization. You can use the proteinGroups.txt file for this purpose. Note, that the summarization has already be conducted by the MaxQuant software. So only log2 transformation and normalisation has to be conducted. What do you observe, how does that compare to the robust summarisation and try to explain this?

2.3 The Francisella dataset

A study on the facultative pathogen Francisella tularensis was conceived by Ramond et al. (2015) [12]. F. tularensis enters the cells of its host by phagocytosis. The authors showed that F. tularensis is arginine deficient and imports arginine from the host cell via an arginine transporter, ArgP, in order to efficiently escape from the phagosome and reach the cytosolic compartment, where it can actively multiply. In their study, they compared the proteome of wild type F. tularensis (WT) to ArgP-gene deleted F. tularensis (knock-out, D8). For this exercise, we use a subset of the F. tularensis dataset where bacterial cultures were grown in biological triplicate and each sample was run on a nanoRSLC-Q Exactive PLUS instrument. The data were searched with MaxQuant version 1.4.1.2. The data can be found on https://github.com/statOmics/SGA2019/tree/data.

Which contrast do we want to test now? [2.4.a]

Give the interpretation of the contrast for your top hit? [2.4b]

2.4 Breast cancer example

Eighteen Estrogen Receptor Positive Breast cancer tissues from from patients treated with tamoxifen upon recurrence have been assessed in a proteomics study. Nine patients had a good outcome (or) and the other nine had a poor outcome (pd). The proteomes have been assessed using an LTQ-Orbitrap and the thermo output .RAW files were searched with MaxQuant (version 1.4.1.2) against the human proteome database (FASTA version 2012-09, human canonical proteome).

Three peptides txt files are available:

1. For a 3 vs 3 comparison
2. For a 6 vs 6 comparison
3. For a 9 vs 9 comparison

The data can be found at https://github.com/statOmics/SGA2019/tree/data. in the folder data/quantification/cancer

Perform an MSqRob analysis for each peptide file. What are the differences and try to explain why.