1 Introduction

The QFeatures package provides infrastructure (that is classes to store data and the methods to process and manipulate them) to manage and analyse quantitative features from mass spectrometry experiments. It is based on the SummarizedExperiment and MultiAssayExperiment classes. Assays in a QFeatures object have a hierarchical relation: proteins are composed of peptides, themselves produced by spectra, as depicted in figure 1. Throughout the aggregation and processing of these data, the relations between assays are tracked and recorded, thus allowing users to easily navigate across spectra, peptide and protein quantitative data.

Figure 1: Conceptual representation of a QFeatures object and the aggregative relation between different assays

In the following sections, we are going to demonstrate how to create a single-assay QFeatures objects starting from a spreadsheet, how to compute the next assays (peptides and proteins), and how these can be manipulated and explored.

library("QFeatures")

2 Creating `QFeatures` object

While QFeatures objects can be created manually (see ?QFeatures for details), most users will probably possess quantitative data in a spreadsheet or a dataframe. In such cases, the easiest is to use the readQFeatures function to extract the quantitative data and metadata columns. Below, we load the hlpsms dataframe that contains data for 28 PSMs from the TMT-10plex hyperLOPIT spatial proteomics experiment from (Christoforou et al. 2016). The ecol argument specifies that columns 1 to 10 contain quantitation data, and that the assay should be named psms in the returned QFeatures object, to reflect the nature of the data.

data(hlpsms)
hl <- readQFeatures(hlpsms, ecol = 1:10, name = "psms")
hl

## An instance of class QFeatures containing 1 assays:
##  [1] psms: SummarizedExperiment with 3010 rows and 10 columns

Below, we see that we can extract an assay using its index or its name. The individual assays are stored as SummerizedExperiment object and further access its quantitative data and metadata using the assay and rowData functions

hl[[1]]

## class: SummarizedExperiment 
## dim: 3010 10 
## metadata(0):
## assays(1): ''
## rownames(3010): 1 2 ... 3009 3010
## rowData names(18): Sequence ProteinDescriptions ... RTmin markers
## colnames(10): X126 X127C ... X130N X131
## colData names(0):

hl[["psms"]]

## class: SummarizedExperiment 
## dim: 3010 10 
## metadata(0):
## assays(1): ''
## rownames(3010): 1 2 ... 3009 3010
## rowData names(18): Sequence ProteinDescriptions ... RTmin markers
## colnames(10): X126 X127C ... X130N X131
## colData names(0):

head(assay(hl[["psms"]]))

##         X126      X127C       X127N      X128C       X128N      X129C
## 1 0.12283431 0.08045915 0.070804055 0.09386901 0.051815695 0.13034383
## 2 0.35268185 0.14162381 0.167523880 0.07843497 0.071087436 0.03214548
## 3 0.01546089 0.16142297 0.086938133 0.23120844 0.114664348 0.09610188
## 4 0.04702854 0.09288723 0.102012167 0.11125409 0.067969116 0.14155358
## 5 0.01044693 0.15866147 0.167315736 0.21017494 0.147946673 0.07088253
## 6 0.04955362 0.01215244 0.002477681 0.01297833 0.002988949 0.06253195
##        X129N       X130C      X130N       X131
## 1 0.17540095 0.040068658 0.11478839 0.11961594
## 2 0.06686260 0.031961793 0.02810434 0.02957384
## 3 0.15977819 0.010127118 0.08059400 0.04370403
## 4 0.18015910 0.035329902 0.12166589 0.10014038
## 5 0.17555789 0.007088253 0.02884754 0.02307803
## 6 0.01726511 0.172651119 0.37007905 0.29732174

head(rowData(hl[["psms"]]))

## DataFrame with 6 rows and 18 columns
##      Sequence ProteinDescriptions NbProteins ProteinGroupAccessions
##   <character>         <character>  <integer>            <character>
## 1     SQGEIDk       Tetratrico...          1                 Q8BYY4
## 2     YEAQGDk       Vacuolar p...          1                 P46467
## 3     TTScDTk       C-type man...          1                 Q64449
## 4     aEELESR       Liprin-alp...          1                 P60469
## 5     aQEEAIk       Isoform 2 ...          2               P13597-2
## 6    dGAVDGcR       Structural...          1                 Q6P5D8
##   Modifications    qValue       PEP  IonScore NbMissedCleavages
##     <character> <numeric> <numeric> <integer>         <integer>
## 1 K7(TMT6ple...     0.008   0.11800        27                 0
## 2 K7(TMT6ple...     0.001   0.01070        27                 0
## 3 C4(Carbami...     0.008   0.11800        11                 0
## 4 N-Term(TMT...     0.002   0.04450        24                 0
## 5 N-Term(Car...     0.001   0.00850        36                 0
## 6 N-Term(TMT...     0.000   0.00322        26                 0
##   IsolationInterference IonInjectTimems Intensity    Charge      mzDa      MHDa
##               <integer>       <integer> <numeric> <integer> <numeric> <numeric>
## 1                     0              70    335000         2   503.274   1005.54
## 2                     0              70    926000         2   520.267   1039.53
## 3                     0              70    159000         2   521.258   1041.51
## 4                     0              70    232000         2   531.785   1062.56
## 5                     0              70    212000         2   537.804   1074.60
## 6                     0              70    865000         2   539.761   1078.51
##   DeltaMassPPM     RTmin       markers
##      <numeric> <numeric>   <character>
## 1        -0.38     24.02       unknown
## 2         0.61     18.85       unknown
## 3         1.11     10.17       unknown
## 4         0.35     29.18       unknown
## 5         1.70     25.56 Plasma mem...
## 6        -0.67     21.27 Nucleus - ...

For further details on how to manipulate such objects, refer to the MultiAssayExperiment (Ramos et al. 2017) and SummerizedExperiment (Morgan et al. 2019) packages.

As illustrated in figure 1, an central characteristic of QFeatures objects is the aggregative relation between their assays. This can be obtained with the aggregateFeatures function that will aggregate quantitative features from one assay into a new one. In the next code chunk, we aggregate PSM-level data into peptide by grouping all PSMs that were matched the same peptide sequence. Below, the aggregation function is set, as an example, to the mean. The new assay is named peptides.

hl <- aggregateFeatures(hl, "psms", "Sequence", name = "peptides", fun = colMeans)

## Your row data contain missing values. Please read the relevant
## section(s) in the aggregateFeatures manual page regarding the effects
## of missing values on data aggregation.

hl

## An instance of class QFeatures containing 2 assays:
##  [1] psms: SummarizedExperiment with 3010 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 2923 rows and 10 columns

hl[["peptides"]]

## class: SummarizedExperiment 
## dim: 2923 10 
## metadata(0):
## assays(2): assay aggcounts
## rownames(2923): AAAVSTEGk AAIDYQk ... ykVEEASDLSISk ykVPQTEEPTAk
## rowData names(7): Sequence ProteinDescriptions ... markers .n
## colnames(10): X126 X127C ... X130N X131
## colData names(0):

Below, we repeat the aggregation operation by grouping peptides into proteins as defined by the ProteinGroupAccessions variable.

hl <- aggregateFeatures(hl, "peptides", "ProteinGroupAccessions", name = "proteins", fun = colMeans)
hl

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 3010 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 2923 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 1596 rows and 10 columns

hl[["proteins"]]

## class: SummarizedExperiment 
## dim: 1596 10 
## metadata(0):
## assays(2): assay aggcounts
## rownames(1596): A2A432 A2A6Q5-3 ... Q9Z2Z9 Q9Z315
## rowData names(3): ProteinGroupAccessions markers .n
## colnames(10): X126 X127C ... X130N X131
## colData names(0):

The sample assayed in a QFeatures object can be documented in the colData slot. The hl data doens’t currently possess any sample metadata. These can be addedd as a new DataFrame with matching names (i.e. the DataFrame rownames must be identical assay’s colnames) or can be added one variable at at time, as shown below.

colData(hl)

## DataFrame with 10 rows and 0 columns

hl$tag <- c("126", "127N", "127C", "128N", "128C", "129N", "129C",
            "130N", "130C", "131")
colData(hl)

## DataFrame with 10 rows and 1 column
##               tag
##       <character>
## X126          126
## X127C        127N
## X127N        127C
## X128C        128N
## X128N        128C
## X129C        129N
## X129N        129C
## X130C        130N
## X130N        130C
## X131          131

3 Subsetting

One particularity of the QFeatures infrastructure is that the features of the constitutive assays are linked through an aggregative relation. This relation is recorded when creating new assays with aggregateFeatures and is exploited when subsetting QFeature by their feature names.

In the example below, we are interested in the Stat3B isoform of the Signal transducer and activator of transcription 3 (STAT3) with accession number P42227-2. This accession number corresponds to a feature name in the proteins assay. But this protein row was computed from 8 peptide rows in the peptides assay, themselves resulting from the aggregation of 8 rows in the psms assay.

stat3 <- hl["P42227-2", , ]
stat3

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 9 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 8 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 1 rows and 10 columns

We can easily visualise this new QFeatures object using ggplot2 once converted into a data.frame.

stat3_df <- data.frame(longFormat(stat3))
stat3_df$assay <- factor(stat3_df$assay,
                        levels = c("psms", "peptides", "proteins"))

library("ggplot2")
ggplot(data = stat3_df,
       aes(x = colname,
           y = value,
           group = rowname)) +
    geom_line() + geom_point() +
    facet_grid(~ assay)

Below we repeat the same operation for the Signal transducer and activator of transcription 1 (STAT1) and 3 (STAT3) accession numbers, namely P42227-2 and P42225. We obtain a new QFeatures instance containing 2 proteins, 9 peptides and 10 PSMS. From this, we can readily conclude that STAT1 was identified by a single PSM/peptide.

stat <- hl[c("P42227-2", "P42225"), , ]
stat

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 10 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 9 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 2 rows and 10 columns

Below, we visualise the expression profiles for the two proteins.

stat_df <- data.frame(longFormat(stat))
stat_df$stat3 <- ifelse(stat_df$rowname %in% stat3_df$rowname,
                        "STAT3", "STAT1")
stat_df$assay <- factor(stat_df$assay,
                        levels = c("psms", "peptides", "proteins"))

ggplot(data = stat_df,
       aes(x = colname,
           y = value,
           group = rowname)) +
    geom_line() + geom_point() +
    facet_grid(stat3 ~ assay)

The subsetting by feature names is also available as a call to the subsetByFeature function, for use with the pipe operator.

library(magrittr)
hl %>%
    subsetByFeature("P42227-2")

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 9 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 8 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 1 rows and 10 columns

hl %>%
    subsetByFeature(c("P42227-2", "P42225"))

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 10 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 9 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 2 rows and 10 columns

and possibly

hl %>%
    subsetByFeature("P42227-2") %>%
    longFormat() %>%
    as.data.frame %>%
    ggplot(aes(x = colname,
               y = value,
               group = rowname)) +
    geom_line() +
    facet_grid(~ assay)

to reproduce the line plot.

Finally, a simply shiny app allows to explore and visualise the respective assays of a QFeatures object.

display(stat)

Figure 2: QFeatures interactive interface: heatmap of the peptide assay data

Figure 3: QFeatures interactive interface: quantitative peptide assay data

Figure 4: QFeatures interactive interface: peptide assay row data

A dropdown menu in the side bar allows the user to select an assay of interest, which can then be visualised as a heatmap (figure 2), as a quantitative table (figure 3) or a row data table (figure 4).

4 Filtering

QFeatures is assays can also be filtered based on variables in their respective row data slots using the filterFeatures function. The filters can be defined using the formula interface or using AnnotationFilter objects from the AnnotationFilter package (Morgan and Rainer 2019). In addition to the pre-defined filters (such as SymbolFilter, ProteinIdFilter, … that filter on gene symbol, protein identifier, …), this package allows users to define arbitrary character or numeric filters using the VariableFilter.

mito_filter <- VariableFilter(field = "markers",
                              value = "Mitochondrion",
                              condition = "==")
mito_filter

## class: CharacterVariableFilter 
## condition: == 
## value: Mitochondrion

qval_filter <- VariableFilter(field = "qValue",
                              value = 0.001,
                              condition = "<=")
qval_filter

## class: NumericVariableFilter 
## condition: <= 
## value: 0.001

These filter can then readily be applied to all assays’ row data slots. The mito_filter will return all PSMs, peptides and proteins that were annotated as localising to the mitochondrion.

filterFeatures(hl, mito_filter)

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 167 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 162 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 113 rows and 10 columns

The qval_filter, on the other hand, will only return a subset of PSMs, because the qValue variable is only present in the psms assays. The q-values are only relevant to PSMs and that variable was dropped from the other assays.

filterFeatures(hl, qval_filter)

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 2466 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 0 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 0 rows and 10 columns

The same filters can be created using the forumla interface:

filterFeatures(hl, ~ markers == "Mitochondrion")

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 167 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 162 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 113 rows and 10 columns

filterFeatures(hl, ~ qValue <= 0.001)

## An instance of class QFeatures containing 3 assays:
##  [1] psms: SummarizedExperiment with 2466 rows and 10 columns 
##  [2] peptides: SummarizedExperiment with 0 rows and 10 columns 
##  [3] proteins: SummarizedExperiment with 0 rows and 10 columns

Session information

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] gplots_3.1.1                magrittr_2.0.1             
##  [3] dplyr_1.0.6                 ggplot2_3.3.3              
##  [5] QFeatures_1.2.0             MultiAssayExperiment_1.18.0
##  [7] SummarizedExperiment_1.22.0 Biobase_2.52.0             
##  [9] GenomicRanges_1.44.0        GenomeInfoDb_1.28.0        
## [11] IRanges_2.26.0              S4Vectors_0.30.0           
## [13] BiocGenerics_0.38.0         MatrixGenerics_1.4.0       
## [15] matrixStats_0.58.0          BiocStyle_2.20.0           
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.0              jsonlite_1.7.2          gtools_3.8.2           
##  [4] bslib_0.2.5.1           assertthat_0.2.1        BiocManager_1.30.15    
##  [7] highr_0.9               msdata_0.31.1           GenomeInfoDbData_1.2.6 
## [10] yaml_2.2.1              pillar_1.6.1            lattice_0.20-44        
## [13] glue_1.4.2              limma_3.48.0            digest_0.6.27          
## [16] XVector_0.32.0          colorspace_2.0-1        htmltools_0.5.1.1      
## [19] Matrix_1.3-3            pkgconfig_2.0.3         magick_2.7.2           
## [22] bookdown_0.22           zlibbioc_1.38.0         purrr_0.3.4            
## [25] scales_1.1.1            tibble_3.1.2            farver_2.1.0           
## [28] generics_0.1.0          AnnotationFilter_1.16.0 ellipsis_0.3.2         
## [31] withr_2.4.2             lazyeval_0.2.2          crayon_1.4.1           
## [34] evaluate_0.14           fansi_0.4.2             MASS_7.3-54            
## [37] tools_4.1.0             lifecycle_1.0.0         stringr_1.4.0          
## [40] munsell_0.5.0           cluster_2.1.2           DelayedArray_0.18.0    
## [43] compiler_4.1.0          jquerylib_0.1.4         caTools_1.18.2         
## [46] rlang_0.4.11            grid_4.1.0              RCurl_1.98-1.3         
## [49] MsCoreUtils_1.4.0       bitops_1.0-7            labeling_0.4.2         
## [52] rmarkdown_2.8           gtable_0.3.0            DBI_1.1.1              
## [55] R6_2.5.0                knitr_1.33              utf8_1.2.1             
## [58] clue_0.3-59             ProtGenerics_1.24.0     KernSmooth_2.23-20     
## [61] stringi_1.6.2           Rcpp_1.0.6              vctrs_0.3.8            
## [64] tidyselect_1.1.1        xfun_0.23

References

Christoforou, Andy, Claire M Mulvey, Lisa M Breckels, Aikaterini Geladaki, Tracey Hurrell, Penelope C Hayward, Thomas Naake, et al. 2016. “A Draft Map of the Mouse Pluripotent Stem Cell Spatial Proteome.” Nat Commun 7: 8992. https://doi.org/10.1038/ncomms9992.

Morgan, Martin, Valerie Obenchain, Jim Hester, and Hervé Pagès. 2019. SummarizedExperiment: SummarizedExperiment Container.

Morgan, Martin, and Johannes Rainer. 2019. AnnotationFilter: Facilities for Filtering Bioconductor Annotation Resources. https://github.com/Bioconductor/AnnotationFilter.

Ramos, Marcel, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez Cabrera, Tiffany Chan, et al. 2017. “Software for the Integration of Multi-Omics Experiments in Bioconductor.” Cancer Research 77(21); e39-42.

Quantitative features for mass spectrometry data

19 May 2021

Abstract

Package

1 Introduction

2 Creating `QFeatures` object

3 Subsetting

4 Filtering

Session information

References

Quantitative features for mass spectrometry data

19 May 2021

Abstract

Package

1 Introduction

2 Creating QFeatures object

3 Subsetting

4 Filtering

Session information

References

2 Creating `QFeatures` object