Introduction to quantitative proteomics data
Overview
Teaching: 15 min
Exercises: 0 minQuestions
What data do we have?
What analyses can we apply?
How can we check the data throughout the analysis?
Objectives
Understand proteomics data set.
Understand what data cleaning we need to do.
Understand what data analysis we can do.
Data
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The data we are going to use are part of a study of the Clinical Proteomic Technology Assessment for Cancer CPTAC led by Amanda Paulovich.
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In the 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 S. cerevisiae strain BY4741. Two different spike-in concentrations were used: 6A (0.25 fmol UPS1 proteins/μL) and 6B (0.74 fmol UPS1 proteins/μL).
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The data were searched with MaxQuant version 1.5.2.8, and detailed search settings were described by Goeminne et al.
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Three replicates are available for each concentration. The study is a spike-in study for which we know the ground truth.
Read data
To read and instepct the peptides.txt file we will use the package readr, which is bundled within the tidyverse package. Please load the tidyverse if not already done. :
library("tidyverse")
f <- readr::read_delim("https://raw.githubusercontent.com/lgatto/bioc-ms-prot/master/data/cptac_peptides.txt",delim="\t")
## explore
head(f)
Quantitative information
Quantitative information is contained in the columns that have “Intensity” tag. We can see which columns have quantitative information.
(i <- grep("Intensity.", names(f))) # 56 57 58 59 60 61
Your turn
Exercise
Subtract peptide names and intensity values from
f.Solution
f[,c(1,i)]
Key Points
It’s important to understand your proteomics data.