1 Introduction

The hdxmsqc package is a quality control assessment package from hydrogen-deuterium exchange mass-spectrometry (HDX-MS) data. The functions look for outliers in retention time and ion mobility. They also examine missing values, mass errors, intensity based outliers, deviations of the data from monotonicity, the correlation of charge states, whether uptake values are coherent based on overlapping peptides and finally the similarity of the observed to the theoretical spectra observed. This package is designed to help those performing iterative quality control through manual inspection but also a set of metric and visualizations by which practitioners can use to demonstrate they have high quality data.

2 packages

The packages required are the following.

suppressMessages(require(hdxmsqc))
require(S4Vectors)
suppressMessages(require(dplyr))
require(tidyr)
## Loading required package: tidyr
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:S4Vectors':
## 
##     expand
require(QFeatures)
require(RColorBrewer)
## Loading required package: RColorBrewer
require(ggplot2)
## Loading required package: ggplot2
require(MASS)
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
require(pheatmap)
## Loading required package: pheatmap
require(Spectra)
require(patchwork)
## Loading required package: patchwork
## 
## Attaching package: 'patchwork'
## The following object is masked from 'package:MASS':
## 
##     area

3 Data

We first load the data, as exported from HDExaminer.

BRD4uncurated <- data.frame(read.csv(system.file("extdata", "ELN55049_AllResultsTables_Uncurated.csv", package = "hdxmsqc", mustWork = TRUE)))

The following code chunk tidies dataset, which improves the formatting and converts to wide format. It will also note the number of states, timepoints and peptides.

BRD4uncurated_wide <- processHDE(HDExaminerFile = BRD4uncurated,
                                 proteinStates = c("wt", "iBET"))
## Number of peptide sequence: 167
## Number of timepoints: 7
## Number of Protein States: 2
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion

The next code chunk extracts the columns with the quantitative data.

i <- grep(pattern = "X..Deut",
          x = names(BRD4uncurated_wide))

We now parse the object into an object of class Qfeatures. This standardises the formatting of the data.

BRD4df <- readQFeatures(assayData = BRD4uncurated_wide,
                        ecol = i,
                        names = "Deuteration",
                        fnames = "fnames")
## Checking arguments.
## Warning in .checkWarnEcol(quantCols, ecol): 'ecol' is deprecated, use
## 'quantCols' instead.
## Loading data as a 'SummarizedExperiment' object.
## Formatting sample annotations (colData).
## Formatting data as a 'QFeatures' object.

4 Visualisation

A simple heatmap of our data can give us a sense of it.

pheatmap(assay(BRD4df), cluster_rows = FALSE, scale = "row")