PDA/docs/pda_tutorialDesign.html at master · statOmics/PDA · GitHub

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<h1 class="title toc-ignore">3. Statistical analysis with msqrob2 for
experiments with more complex designs</h1>
<h4 class="author">Lieven Clement</h4>
<h4 class="date"><a href="https://statomics.github.io">statOmics</a>,
Ghent University</h4>

</div>


<p><a rel="license" href="https://creativecommons.org/licenses/by-nc-sa/4.0"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a></p>
<div id="analysis-of-more-complex-designs-with-msqrob"
class="section level3">
<h3>3. Analysis of more complex designs with MSqRob</h3>
<p>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 extend our generic
workflow for differential analysis of quantitative datasets with more
complex 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].</p>
</div>
<div id="basic-statistical-concepts" class="section level3">
<h3>3.1 Basic Statistical Concepts</h3>
<p>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 (<a
href="http://www.nature.com/collections/qghhqm/pointsofsignificance"
class="uri">http://www.nature.com/collections/qghhqm/pointsofsignificance</a>).</p>
<p>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 three for a wild-type sample). The
measurements, however, are gathered on the observational units. In a
shotgun proteomics experiment, these are the individual peptide
intensities.</p>
<p>For many proteins, there are thus multiple observations/peptide
intensities for each experimental unit, which can be considered as
technical replicates or pseudo-replicates.<br />
There are two methods to deal with pseudo-replication at the
peptide-level.</p>
<ol style="list-style-type: decimal">
<li><p>Summarize the peptide intensities into a protein expression value
for each protein in the preprocessing and specify the statistical model
at the protein level. Indeed, we no-longer have pseudo-replication at
the protein level.</p></li>
<li><p>Model the peptide intensities using a mixed model with a random
effect for sample, which will model the correlation between peptides for
the same protein in the same sample.</p></li>
</ol>
<p>Both approaches are possible with msqrob2. But, in the tutorial we
focus on the summariztion method which is computationally less demanding
and the statistical model is less complex to teach.</p>
<p>In a typical proteomics data set one can make very precise estimates
on the technical variability of the intensity measurements; i.e. how
strongly intensity measurements fluctuate for the peptides of a
particular protein. 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.</p>
<p>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).</p>
<p>A very powerful concept in experimental design is that of blocking.
In randomized complete block design one randomizes the different
treatments to experimental units that are arranged within groups/blocks
(e.g. batches, time periods) that are similar to each other. Due to
practical constraints, it is often impossible to perform all experiments
on the same day, or even on the same HPLC column or mass spectrometer,
leading to unwanted sources of technical variation. In other
experiments, researchers might test the treatment in multiple cultures
or in big experiments that involve multiple labs. A good experimental
design aims to mitigate unwanted sources of variability by including all
or as many treatments as possible within each block. That way,
variability between blocks can be factored out from the analysis when
assessing treatment effects (Figure 1). It is of prime importance that
all treatments are present within each block, otherwise confounding can
occur between the treatment and block e.g. (Figure 1).</p>
<div class="float">
<img src="figures/blocking.png" alt="Figure 1. Blocking" />
<div class="figcaption">Figure 1. Blocking</div>
</div>
<p>Figure 1. is an example of a good (A) and a bad (B) design. In design
A, both the green and orange treatments are divided equally within each
block. That way, the treatment effect can be estimated within a block.
In design B, each block contains only one treatment, so the treatment
effect is entirely confounded with the blocking effect and it is thus
impossible to draw meaningful conclusions on the treatment (unless one
would be willing to assume that the blocking effect is negligible, which
is a very strong assumption that cannot be verified based on the
design).</p>
</div>
<div id="blocking-mouse-t-cell-example" class="section level3">
<h3>3.2 Blocking: Mouse T-cell example</h3>
<p>Duguet et al. 2017 compared the proteomes of mouse regulatory T cells
(Treg) and conventional T cells (Tconv) in order to discover
differentially regulated proteins between these two cell populations.
For each biological repeat the proteomes were extracted for both Treg
and Tconv cell pools, which were purified by flow cytometry. The data in
data/quantification/mouseTcell on the <a
href="https://github.com/statOmics/PDA25EBI/archive/refs/heads/data.zip">PDA-data
repository</a> are a subset of the data <a
href="https://www.ebi.ac.uk/pride/archive/projects/PXD004436">PXD004436</a>
on PRIDE.</p>
<div class="float">
<img src="figures/mouseTcell_RCB_design.png"
alt="Figure 2. Design Mouse Study" />
<div class="figcaption">Figure 2. Design Mouse Study</div>
</div>
<p>Three subsets of the data are avialable:</p>
<ul>
<li>peptidesCRD.txt: contains data of Tconv cells for 4 bio-repeats and
Treg cells for 4 bio-repeats</li>
<li>peptidesRCB.txt: contains data for 4 bio-repeats only, but for each
bio-repeat the Treg and Tconv proteome is profiled.<br />
</li>
<li>peptides.txt: contains data of Treg and Tconv cells for 7
bio-repeats</li>
</ul>
<p>Adjust the script <a href="./cptac_robust.html">cptac.html</a> for
the analysis or perform the analysis with the <a
href="./cptac_robust_gui.html">GUI</a> .</p>
<div id="how-would-you-analyse-the-crd-data" class="section level4">
<h4>3.2.1. How would you analyse the CRD data?</h4>
</div>
<div id="how-would-you-analyse-the-rcb-data" class="section level4">
<h4>3.2.2. How would you analyse the RCB data?</h4>
</div>
<div
id="try-to-explain-the-difference-in-the-number-of-proteins-that-can-be-discovered-with-both-designs"
class="section level4">
<h4>3.2.3. Try to explain the difference in the number of proteins that
can be discovered with both designs?</h4>
<p><br/><br/></p>
</div>
</div>
<div id="heart-dataset" class="section level3">
<h3>3.3 Heart dataset</h3>
<div class="float">
<img src="figures/heart.png" alt="Figure 3. Heart" />
<div class="figcaption">Figure 3. Heart</div>
</div>
<p>Researchers have assessed the proteome in different regions of the
heart for 3 patients (identifiers 3, 4, and 8). For each patient they
sampled the left atrium (LA), right atrium (RA), left ventricle (LV) and
the right ventricle (RV). The data are a small subset of the public
dataset <a
href="https://www.ebi.ac.uk/pride/archive/projects/PXD006675">PXD006675</a>
on PRIDE.</p>
<p>Suppose that researchers are mainly interested in comparing the
ventricular to the atrial proteome. Particularly, they would like to
compare the left atrium to the left ventricle, the right atrium to the
right ventricle, the average ventricular vs atrial proteome and if
ventricular vs atrial proteome shifts differ between left and right
heart region.</p>
<p>Adjust the script <a href="./cptac_robust.html">cptac.html</a> for
the analysis or perform the analysis using the <a
href="./cptac_robust_gui.html">GUI</a> .</p>
<div id="which-factors-will-you-use-in-the-mean-model"
class="section level4">
<h4>3.3.1. Which factors will you use in the mean model?</h4>
</div>
<div id="spell-out-the-contrast-for-each-research-question"
class="section level4">
<h4>3.3.2. Spell out the contrast for each research question?</h4>
</div>
<div id="interpret-the-estimate-for-the-top-hit-of-each-contrast"
class="section level4">
<h4>3.3.3. Interpret the estimate for the top hit of each contrast?</h4>
</div>
<div
id="try-to-explain-why-there-is-such-a-large-difference-in-the-number-of-significant-proteins-that-are-found-between-the-contrasts"
class="section level4">
<h4>3.3.4. Try to explain why there is such a large difference in the
number of significant proteins that are found between the
contrasts?</h4>
</div>
</div>
<div id="full-francisella-dataset-of-ramon-et-al."
class="section level3">
<h3>3.3 Full francisella dataset of Ramon et al. </h3>
<ul>
<li>The Francisella tularensis data that we used in the first part of
the course notes were a subset of the data of Ramond et al. </li>
<li>The authors have run the sample for each bio-rep in technical
triplicate on the mass-spectrometer.</li>
<li>Try to analyse the data for the full experiment</li>
</ul>
</div>


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