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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Handling quantitative proteomics features?</title>
<meta charset="utf-8" />
<meta name="author" content="Laurent Gatto and Christophe Vanderaa" />
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class: center, middle, inverse, title-slide
# Handling quantitative proteomics features?
### Laurent Gatto and Christophe Vanderaa
### 2021/08/10
---
class: middle
name: cc-by
### Get the slides at http://bit.ly/202108SCP
These slides are available under a **creative common
[CC-BY license](http://creativecommons.org/licenses/by/4.0/)**. You are
free to share (copy and redistribute the material in any medium or
format) and adapt (remix, transform, and build upon the material) for
any purpose, even commercially
<img height="20px" alt="CC-BY" src="https://raw.githubusercontent.com/UCLouvain-CBIO/scp-teaching/main/img/cc1.jpg" />.
---
class: middle, center, inverse
# What is QFeatures?
---
class: middle
### What is QFeatures?
- An R/[Bioconductor](http://www.bioconductor.org/packages/QFeatures)
*package* to process and manage quantitative proteomics data.
- A *class*, i.e. the container that will hold the quantitative
(single cell) proteomics data and that will **track** and **record**
the processing steps.
---
class: middle, center, inverse
# Feature aggregation in proteomics
---
class: middle
.pull-left[
### Feature aggregation in proteomics
- One or more PSMs are aggregated into peptides.
- One or more peptides are aggregated into protein groups.
**QFeatures** does this in a consistent and reproducible way and
records these steps, so that one can easily **track** features across
levels.
]
.pull-right[
![](https://rformassspectrometry.github.io/QFeatures/articles/QFeatures_files/figure-html/featuresplot-1.png)<!-- -->
]
---
class: middle, center, inverse
# What happens under the hood?
---
class:
.panelset[
.panel[.panel-name[QFeatures]
A **QFeatures** object containing three **assays**, named PSM1, PSM2
and PSM3. Each assay contains quantitative data for a set of features
(rows) and samples (columns). The **QFeatures** object also contains
sample annotations matching columns across all assays.
<img src="./figs/qfeatures_intro_1.svg" width="70%" style="display: block; margin: auto;" />
Such a **QFeatures** object can be created with the [`readSCP()`]()
function.
]
.panel[.panel-name[assay]
<div id="htmlwidget-e05e34fd5962cf6a2c69" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-e05e34fd5962cf6a2c69">{"x":{"filter":"none","data":[["PSM1","PSM2","PSM3","PSM4","PSM5","PSM6","PSM7","PSM8","PSM9","PSM10"],[1,2,3,4,5,6,7,8,9,10],[11,12,13,14,15,16,17,18,19,20],["","","","","","","","","",""],["SYGFNAAR","SYGFNAAR","SYGFNAAR","ELGNDAYK","ELGNDAYK","ELGNDAYK","IAEESNFPFIK","IAEESNFPFIK","IAEESNFPFIK","IAEESNFPFIK"],["ProtA","ProtA","ProtA","ProtA","ProtA","ProtA","ProtB","ProtB","ProtB","ProtB"],[1,2,3,4,5,6,7,8,9,10],["Mitochondrion","Mitochondrion","Mitochondrion","Mitochondrion","Mitochondrion","Mitochondrion","unknown","unknown","unknown","unknown"],[0.084,0.077,0.063,0.073,0.012,0.011,0.075,0.038,0.028,0.097]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>S1<\/th>\n <th>S2<\/th>\n <th>...<\/th>\n <th>Sequence<\/th>\n <th>Protein<\/th>\n <th>Var<\/th>\n <th>location<\/th>\n <th>pval<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,2,6,8]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
]
.panel[.panel-name[colData]
<div id="htmlwidget-6b6e5dc69ade1cc055c3" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-6b6e5dc69ade1cc055c3">{"x":{"filter":"none","data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36"],["S1","S2","S3","S4","S5","S6","S7","S8","S9","S10","S11","S12","S13","S14","S15","S16","S17","S18","S19","S20","S21","S22","S23","S24","S25","S26","S27","S28","S29","S30","S31","S32","S33","S34","S35","S36"],["LC1","LC1","LC1","LC1","LC1","LC1","LC1","LC1","LC1","LC1","LC1","LC1","LC2","LC2","LC2","LC2","LC2","LC2","LC2","LC2","LC2","LC2","LC2","LC2","LC3","LC3","LC3","LC3","LC3","LC3","LC3","LC3","LC3","LC3","LC3","LC3"],["carrier","empty","cellA","cellB","cellC","blank","cellA","cellB","cellC","cellA","cellB","cellC","carrier","empty","cellA","cellB","cellC","blank","cellA","cellB","cellC","cellA","cellB","cellC","carrier","empty","cellA","cellB","cellC","blank","cellA","cellB","cellC","cellA","cellB","cellC"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>sample<\/th>\n <th>LC_batch<\/th>\n <th>type<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script>
]
]
---
class:
#### Aggregating PSMs to peptides
<img src="./figs/qfeatures_intro_2.svg" style="display: block; margin: auto;" />
```r
qf <- aggregateFeaturesOverAssays(qf,
i = c("PSM1", "PSM2", "PSM3"), ## assays to be aggregated
name = c("Pep1", "Pep2", "Pep3"), ## new assay name
fcol = "peptide", ## what to aggregate
fun = colMedians) ## how to aggregate
```
---
class:
#### Joining peptide assays
<img src="./figs/qfeatures_intro_3.svg" style="display: block; margin: auto;" />
```r
qf <- joinAssays(qf,
c("Pep1", "Pep2", "Pep3"), ## assays to be joined
name = "Peptides") ## new assay name
```
---
class:
#### Aggregate peptides into proteins
<img src="./figs/qfeatures_intro_4.svg" style="display: block; margin: auto;" />
```r
qf <- aggregateFeatures(qf,
"Peptides", ## assay to be aggregated
"Protein", ## new assay name
name = "proteins", ## what to aggregate
fun = colMedians) ## how to aggregate
```
---
class: middle, center, inverse
# Why tracking assays
---
class: middle
<img src="https://rformassspectrometry.github.io/QFeatures/articles/QFeatures_files/figure-html/plotstat-1.png" width="90%" style="display: block; margin: auto;" />
---
class: middle, center, inverse
# More with QFeatures and scp
---
class: middle
### More with QFeatures and scp
- quality control
- feature and sample filtering, for example `filterFeatures()`
- transformation and normalisation, for example `normalize()`, `logTransform()`
- imputation with `impute()`
- batch correction
- visualisaton
- the full R/Bioconductor statistical learning toolkit
---
class: middle, center, inverse
# Exercise
---
class: middle
#### Match the code to the `QFeatures` object structure.
A.
<img src="./figs/qf_plot_qst1.png" width="30%" />
B.
<img src="./figs/qf_plot_qst2.png" width="30%" />
C.
<img src="./figs/qf_plot_qst3.png" width="30%" />
```r
1. joinAssays(); aggregateFeatures(); logTranform()
2. aggregateFeaturesOverAssays(); logTransform(); joinAssays()
3. logTransfrom(); joinAssays(); aggregateFeatures()
```
Connect to **http://www.wooclap.com/YYRQRM**
---
class: middle
### Further information
- The **QFeatures** webpage: http://RforMassSpectrometry.github.io/QFeatures
- The **scp** webpage: http://UCLouvain-CBIO.github.io/scp
- Online **tutotial**: https://lgatto.github.io/QFeaturesScpWorkshop2021/
### Funding
Fonds de la Recherche Scientifique (FNRS), Belgium
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