LC Observations - Retention Time Stability

Tobias Kockmann1, Christian Trachsel1, Christian Panse1,2* and Bernd Roschitzki1**

1Functional Genomics Center Zurich - Swiss Federal Institute of Technology in Zurich
2Swiss Institute of Bioinformatics

*cp@fgcz.ethz.ch
**bernd.roschitzki@fgcz.uzh.ch

Abstract

This document extends the work concerning the HPLC systems presented in (2016). We refer to the original manuscript for a description of the theory behind applications.

Package

msqc1 1.28.0

1 Introduction

This vignette file is thought to demonstrate the retention time stability of the msqc1_8rep and msqc1_dil data contained in the msqc1 R package.

1.1 Problem:

Retention Time (RT) form different LC-MS platforms are often not comparable (duration, offset).

1.2 Solution: Normalization

The iRT concept [proposed in pmid22577012 for the RT normalization is used. Instead of using the iRT-peptides MSQC1-peptides are used as reference peptides between the different chromatographic settings and systems.

The normalization is done by applying the following steps:

• define a reference platform (QTRAP)
• for each platform a linear model is build (in the following we use the stats::lm function) which maps the RT space of the corresponding platform into reference platform RT space (by using the stats::predict.lm method)
• scale the normalized RT values into [0, 1]
• visualize the normalized RT values

The code below shows the used R functions for the applied RT normalization.

msqc1:::.normalize_rt

## function (S, S.training, reference_instrument = "Retention.Time.QTRQP") 
## {
##     S.normalized <- S
##     for (instrument in unique(S.normalized$instrument)) {
##         i <- paste("Retention.Time", instrument, sep = ".")
##         rt.out <- S.training[, reference_instrument]
##         rt.in <- S.training[, i]
##         S.fit <- lm(rt.out ~ rt.in)
##         S.normalized[S.normalized$instrument == instrument, "Retention.Time"] <- predict(S.fit, 
##             data.frame(rt.in = S.normalized[S.normalized$instrument == 
##                 instrument, "Retention.Time"]))
##     }
##     S.normalized.min <- min(S.normalized$Retention.Time, na.rm = TRUE)
##     S.normalized.delta <- max(S.normalized$Retention.Time, na.rm = TRUE) - 
##         S.normalized.min
##     S.normalized$Retention.Time <- (S.normalized$Retention.Time - 
##         S.normalized.min)/S.normalized.delta
##     return(S.normalized)
## }
## <bytecode: 0x55e306b28510>
## <environment: namespace:msqc1>

The reshape_rt method is used for reshaping the data from long to wide format which ease the the model building in the msqc1:::.normalize_rt method above.

msqc1:::.reshape_rt

## function (S, peptides = peptides, plot = TRUE, ...) 
## {
##     S <- S[grep("[by]", S$Fragment.Ion), ]
##     S <- S[S$Peptide.Sequence %in% peptides$Peptide.Sequence, 
##         ]
##     S <- aggregate(Retention.Time ~ Peptide.Sequence * instrument, 
##         FUN = mean, data = S)
##     S <- droplevels(S)
##     S.training <- reshape(S, direction = "wide", v.names = "Retention.Time", 
##         timevar = c("instrument"), idvar = "Peptide.Sequence")
##     if (plot == TRUE) {
##         pairs(S.training[, 2:6], pch = as.integer(S.training$Peptide.Sequence), 
##             col = as.integer(S.training$Peptide.Sequence), lower.panel = NULL, 
##             ...)
##     }
##     return(S.training)
## }
## <bytecode: 0x55e3065e9018>
## <environment: namespace:msqc1>

The following R code listings displays some helper functions designed to ease the visualization of the msqc1_8rep and msqc1_dil RT values.

msqc1:::.plot_rt_8rep

## function (S, peptides, ...) 
## {
##     .figure_setup()
##     S <- S[grep("[by]", S$Fragment.Ion), ]
##     S <- S[S$Peptide.Sequence %in% peptides$Peptide.Sequence, 
##         ]
##     S <- aggregate(Retention.Time ~ Peptide.Sequence * File.Name.Id * 
##         instrument * relative.amount * Isotope.Label.Type, FUN = mean, 
##         data = S)
##     S <- droplevels(S)
##     xyplot(Retention.Time ~ File.Name.Id | Isotope.Label.Type * 
##         instrument, data = S, layout = c(10, 1), group = S$Peptide.Sequence, 
##         auto.key = list(space = "right", points = TRUE, lines = FALSE, 
##             cex = 1), ...)
## }
## <bytecode: 0x55e3067de320>
## <environment: namespace:msqc1>

msqc1:::.plot_rt_dil

## function (S, peptides, ...) 
## {
##     .figure_setup()
##     S <- S[grep("[by]", S$Fragment.Ion), ]
##     S <- S[S$Peptide.Sequence %in% peptides$Peptide.Sequence, 
##         ]
##     S <- aggregate(Retention.Time ~ Peptide.Sequence * File.Name * 
##         instrument * relative.amount * Isotope.Label.Type, FUN = mean, 
##         data = S)
##     S <- droplevels(S)
##     xyplot(Retention.Time ~ relative.amount | Isotope.Label.Type * 
##         instrument, data = S, layout = c(10, 1), group = S$Peptide.Sequence, 
##         scales = list(x = list(rot = 45, log = TRUE, at = sort(unique(S$relative.amount)))), 
##         auto.key = list(space = "right", points = TRUE, lines = FALSE, 
##             cex = 1), ...)
## }
## <bytecode: 0x55e306852238>
## <environment: namespace:msqc1>

2 RT plot

2.1 8 Technical Replicates RT plot

Prepare the training data for the linear model.

S.training.8rep <- msqc1:::.reshape_rt(msqc1_8rep, peptides=peptides, cex=2)