1 Reading data as QFeatures

We are going to use a subset of the CPTAC study 6 containing conditions A and B (Paulovich et al. 2010). The peptide-level data, as processed by MaxQuant (Cox and Mann 2008) is available in the msdata package:

basename(f <- msdata::quant(pattern = "cptac", full.names = TRUE))
## [1] "cptac_a_b_peptides.txt"

From the names of the columns, we see that the quantitative columns, starting with "Intensity." (note the dot!) are at positions 56 to 61.

names(read.delim(f))
##  [1] "Sequence"                 "N.term.cleavage.window"  
##  [3] "C.term.cleavage.window"   "Amino.acid.before"       
##  [5] "First.amino.acid"         "Second.amino.acid"       
##  [7] "Second.last.amino.acid"   "Last.amino.acid"         
##  [9] "Amino.acid.after"         "A.Count"                 
## [11] "R.Count"                  "N.Count"                 
## [13] "D.Count"                  "C.Count"                 
## [15] "Q.Count"                  "E.Count"                 
## [17] "G.Count"                  "H.Count"                 
## [19] "I.Count"                  "L.Count"                 
## [21] "K.Count"                  "M.Count"                 
## [23] "F.Count"                  "P.Count"                 
## [25] "S.Count"                  "T.Count"                 
## [27] "W.Count"                  "Y.Count"                 
## [29] "V.Count"                  "U.Count"                 
## [31] "Length"                   "Missed.cleavages"        
## [33] "Mass"                     "Proteins"                
## [35] "Leading.razor.protein"    "Start.position"          
## [37] "End.position"             "Unique..Groups."         
## [39] "Unique..Proteins."        "Charges"                 
## [41] "PEP"                      "Score"                   
## [43] "Identification.type.6A_7" "Identification.type.6A_8"
## [45] "Identification.type.6A_9" "Identification.type.6B_7"
## [47] "Identification.type.6B_8" "Identification.type.6B_9"
## [49] "Experiment.6A_7"          "Experiment.6A_8"         
## [51] "Experiment.6A_9"          "Experiment.6B_7"         
## [53] "Experiment.6B_8"          "Experiment.6B_9"         
## [55] "Intensity"                "Intensity.6A_7"          
## [57] "Intensity.6A_8"           "Intensity.6A_9"          
## [59] "Intensity.6B_7"           "Intensity.6B_8"          
## [61] "Intensity.6B_9"           "Reverse"                 
## [63] "Potential.contaminant"    "id"                      
## [65] "Protein.group.IDs"        "Mod..peptide.IDs"        
## [67] "Evidence.IDs"             "MS.MS.IDs"               
## [69] "Best.MS.MS"               "Oxidation..M..site.IDs"  
## [71] "MS.MS.Count"
(i <- grep("Intensity\\.", names(read.delim(f))))
## [1] 56 57 58 59 60 61

We now read these data using the readQFeatures function. The peptide level expression data will be imported into R as an instance of class QFeatures named cptac with an assay named peptides. We also use the fnames argument to set the row-names of the peptides assay to the peptide sequences.

library("QFeatures")
cptac <- readQFeatures(f, ecol = i, sep = "\t", name = "peptides", fnames = "Sequence")

2 Encoding the experimental design

Below we update the sample (column) annotations to encode the two groups, 6A and 6B, and the original sample numbers.

cptac$group <- rep(c("6A", "6B"), each = 3)
cptac$sample <- rep(7:9, 2)
colData(cptac)
## DataFrame with 6 rows and 2 columns
##                      group    sample
##                <character> <integer>
## Intensity.6A_7          6A         7
## Intensity.6A_8          6A         8
## Intensity.6A_9          6A         9
## Intensity.6B_7          6B         7
## Intensity.6B_8          6B         8
## Intensity.6B_9          6B         9

3 Filtering out contaminants and reverse hits

filterFeatures(cptac, ~ Reverse == "")
## An instance of class QFeatures containing 1 assays:
##  [1] peptides: SummarizedExperiment with 11436 rows and 6 columns
filterFeatures(cptac, ~ Potential.contaminant == "")
## An instance of class QFeatures containing 1 assays:
##  [1] peptides: SummarizedExperiment with 11385 rows and 6 columns
library("magrittr")
cptac <- cptac %>%
    filterFeatures(~ Reverse == "") %>%
    filterFeatures(~ Potential.contaminant == "")

4 Removing up unneeded feature variables

The spreadsheet that was read above contained numerous variables that are returned by MaxQuant, but not necessarily necessary in the frame of a downstream statistical analysis.

rowDataNames(cptac)
## CharacterList of length 1
## [["peptides"]] Sequence N.term.cleavage.window ... MS.MS.Count

The only ones that we will be needing below are the peptides sequences and the protein identifiers. Below, we store these variables of interest and filter them using the selectRowData function.

rowvars <- c("Sequence", "Proteins", "Leading.razor.protein")
cptac <- selectRowData(cptac, rowvars)
rowDataNames(cptac)
## CharacterList of length 1
## [["peptides"]] Sequence Proteins Leading.razor.protein

5 Managing missing values

Missing values can be very numerous in certain proteomics experiments and need to be dealt with carefully. The first step is to assess their presence across samples and features. But before being able to do so, we need to replace 0 by NA, given that MaxQuant encodes missing data with a 0 using the zeroIsNA function.

cptac <- zeroIsNA(cptac, i = seq_along(cptac))
nNA(cptac, i = seq_along(cptac))
## $nNA
## DataFrame with 1 row and 3 columns
##         assay       nNA       pNA
##   <character> <integer> <numeric>
## 1    peptides     30609   44.9194
## 
## $nNArows
## DataFrame with 11357 rows and 4 columns
##             assay          name       nNA       pNA
##       <character>   <character> <integer> <numeric>
## 1        peptides AAAAGAGGAG...         4   66.6667
## 2        peptides     AAAALAGGK         0    0.0000
## 3        peptides    AAAALAGGKK         0    0.0000
## 4        peptides AAADALSDLE...         0    0.0000
## 5        peptides AAADALSDLE...         0    0.0000
## ...           ...           ...       ...       ...
## 11353    peptides YYSIYDLGNN...         6  100.0000
## 11354    peptides YYTFNGPNYN...         3   50.0000
## 11355    peptides    YYTITEVATR         4   66.6667
## 11356    peptides YYTVFDRDNN...         6  100.0000
## 11357    peptides YYTVFDRDNN...         6  100.0000
## 
## $nNAcols
## DataFrame with 6 rows and 4 columns
##         assay          name       nNA       pNA
##   <character>   <character> <integer> <numeric>
## 1    peptides Intensity....      4669   41.1112
## 2    peptides Intensity....      5388   47.4421
## 3    peptides Intensity....      5224   45.9981
## 4    peptides Intensity....      4651   40.9527
## 5    peptides Intensity....      5470   48.1641
## 6    peptides Intensity....      5207   45.8484

The output of the nNA function tells us that

  • there are currently close to 50% is missing values in the data;
  • there are 4051 peptides with 0 missing values, 989 with a single missing values, … and 3014 peptides composed of only missing values;
  • the range of missing values in the 6 samples is comparable and ranges between 4651 and 5470.

In this dataset, we have such a high number of peptides without any data because the 6 samples are a subset of a larger dataset, and these peptides happened to be absent in groups A and B. Below, we use filterNA to remove all the peptides that contain one or more missing values by using pNA = 0 (which also is the default value).

cptac <- filterNA(cptac, i = seq_along(cptac), pNA = 0)
cptac
## An instance of class QFeatures containing 1 assays:
##  [1] peptides: SummarizedExperiment with 4051 rows and 6 columns

I we wanted to keep peptides that have up to 90% of missing values, corresponsing in this case to those that have only one value (i.e 5/6 percent of missing values), we could have set pNA to 0.9.

6 Imputation

The impute method can be used to perform missing value imputation using a variety of imputation methods. The method takes an instance of class QFeatures (or a SummarizedExperiment) as input, an a character naming the desired method (see ?impute for the complete list with details) and returns a new instance of class QFeatures (or SummarizedExperiment) with imputed data.

As described in more details in (Lazar et al. 2016), there are two types of mechanisms resulting in missing values in LC/MSMS experiments.

  • Missing values resulting from absence of detection of a feature, despite ions being present at detectable concentrations. For example in the case of ion suppression or as a result from the stochastic, data-dependent nature of the MS acquisition method. These missing value are expected to be randomly distributed in the data and are defined as missing at random (MAR) or missing completely at random (MCAR).

  • Biologically relevant missing values, resulting from the absence of the low abundance of ions (below the limit of detection of the instrument). These missing values are not expected to be randomly distributed in the data and are defined as missing not at random (MNAR).

MAR and MCAR values can be reasonably well tackled by many imputation methods. MNAR data, however, requires some knowledge about the underlying mechanism that generates the missing data, to be able to attempt data imputation. MNAR features should ideally be imputed with a left-censor (for example using a deterministic or probabilistic minimum value) method. Conversely, it is recommended to use hot deck methods (for example nearest neighbour, maximum likelihood, etc) when data are missing at random.

Mixed imputation method. Black cells represent presence of quantitation values and light grey corresponds to missing data. The two groups of interest are depicted in green and blue along the heatmap columns. Two classes of proteins are annotated on the left: yellow are proteins with randomly occurring missing values (if any) while proteins in brown are candidates for non-random missing value imputation.

Figure 1: Mixed imputation method
Black cells represent presence of quantitation values and light grey corresponds to missing data. The two groups of interest are depicted in green and blue along the heatmap columns. Two classes of proteins are annotated on the left: yellow are proteins with randomly occurring missing values (if any) while proteins in brown are candidates for non-random missing value imputation.

It is anticipated that the identification of both classes of missing values will depend on various factors, such as feature intensities and experimental design. Below, we use perform mixed imputation, applying nearest neighbour imputation on the 654 features that are assumed to contain randomly distributed missing values (if any) (yellow on figure 1) and a deterministic minimum value imputation on the 35 proteins that display a non-random pattern of missing values (brown on figure 1).

7 Data transformation

When analysing continuous data using parametric methods (such as t-test or linear models), it is often necessary to log-transform the data. The figure below (left) show that how our data is mainly composed of small values with a long tail of larger ones, which is a typical pattern of quantitative omics data.

Below, we use the logTransform function to log2-transform our data. This time, instead of overwriting the peptides assay, we are going to create a new one to contain the log2-transformed data.

cptac <- addAssay(cptac,
                  logTransform(cptac[[1]]),
                  name = "peptides_log")
cptac
## An instance of class QFeatures containing 2 assays:
##  [1] peptides: SummarizedExperiment with 4051 rows and 6 columns 
##  [2] peptides_log: SummarizedExperiment with 4051 rows and 6 columns
par(mfrow = c(1, 2))
limma::plotDensities(assay(cptac[[1]]))
limma::plotDensities(assay(cptac[[2]]))
Quantitative data in its original scale (left) and log2-transformed (right).

Figure 2: Quantitative data in its original scale (left) and log2-transformed (right)

8 Normalisation

Assays in QFeatures objects can be normalised with the normalize function. The type of normalisation is defined by the method argument; below, we use quantiles normalisation, store the normalised data into a new experiment, and visualise the resulting data.

cptac <- addAssay(cptac,
                  normalize(cptac[["peptides_log"]], method = "quantiles"),
                  name = "peptides_norm")
## Loading required namespace: preprocessCore
cptac
## An instance of class QFeatures containing 3 assays:
##  [1] peptides: SummarizedExperiment with 4051 rows and 6 columns 
##  [2] peptides_log: SummarizedExperiment with 4051 rows and 6 columns 
##  [3] peptides_norm: SummarizedExperiment with 4051 rows and 6 columns
par(mfrow = c(1, 2))
limma::plotDensities(assay(cptac[["peptides_log"]]))
limma::plotDensities(assay(cptac[["peptides_norm"]]))