Quantitative features for mass spectrometry data (original) (raw)
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 andMultiAssayExperiment classes. Assays in a QFeatures object have a hierarchical relation: proteins are composed of peptides, themselves produced by spectra, as depicted in figure1. 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")Creating QFeatures object
While QFeatures objects can be created manually (see ?QFeaturesfor details), most users will probably possess quantitative data in a spreadsheet or a dataframe. In such cases, the easiest is to use thereadQFeatures function to extract the quantitative data and metadata columns. Below, we load the hlpsms dataframe that contains data for 3010 PSMs from the TMT-10plex hyperLOPIT spatial proteomics experiment from (Christoforou et al. 2016). The quantColsargument specifies that columns 1 to 10 contain quantitation data, and that the assay should be named psms in the returned QFeaturesobject, to reflect the nature of the data.
data(hlpsms)
hl <- readQFeatures(hlpsms, quantCols = 1:10, name = "psms")## Checking arguments.## Loading data as a 'SummarizedExperiment' object.## Formatting sample annotations (colData).## Formatting data as a 'QFeatures' object.hl## An instance of class QFeatures (type: bulk) with 1 set:
##
## [1] psms: SummarizedExperiment with 3010 rows and 10 columnsBelow, we see that we can extract an assay using its index or its name. The individual assays are stored as _SummarizedExperiment_object and further access its quantitative data and metadata using theassay 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 0.29732174head(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_SummarizedExperiment_ (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 theaggregateFeatures 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.##
Aggregated: 1/1hl## An instance of class QFeatures (type: bulk) with 2 sets:
##
## [1] psms: SummarizedExperiment with 3010 rows and 10 columns
## [2] peptides: SummarizedExperiment with 2923 rows and 10 columnshl[["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)##
Aggregated: 1/1hl## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [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 columnshl[["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 thecolData 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 columnshl$tag <- c("126", "127N", "127C", "128N", "128C", "129N", "129C",
"130N", "130C", "131")
colData(hl)## DataFrame with 10 rows and 1 column
## tag
## <character>
## X126 126
## X127C 127N
## X127N 127C
## X128C 128N
## X128N 128C
## X129C 129N
## X129N 129C
## X130C 130N
## X130N 130C
## X131 131Subsetting
One particularity of the QFeatures infrastructure is that the features of the constitutive assays are linked through an aggregative relation. This relation is recorded when creating new assays withaggregateFeatures and is exploited when subsetting QFeature by their feature names.
In the example below, we are interested in the Stat3B isoform of the Signal transducer and activator of transcription 3 (STAT3) with accession number P42227-2. This accession number corresponds to a feature name in the proteins assay. But this protein row was computed from 8 peptide rows in the peptides assay, themselves resulting from the aggregation of 8 rows in the psms assay.
stat3 <- hl["P42227-2", , ]
stat3## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [1] psms: SummarizedExperiment with 9 rows and 10 columns
## [2] peptides: SummarizedExperiment with 8 rows and 10 columns
## [3] proteins: SummarizedExperiment with 1 rows and 10 columnsWe can easily visualise this new QFeatures object using ggplot2once converted into a data.frame. See the visualization vignette for more details about data exploration from a QFeatures object.
stat3_df <- data.frame(longForm(stat3))
stat3_df$assay <- factor(stat3_df$assay,
levels = c("psms", "peptides", "proteins"))
library("ggplot2")
ggplot(data = stat3_df,
aes(x = colname,
y = value,
group = rowname)) +
geom_line() + geom_point() +
facet_grid(~ assay)Below we repeat the same operation for the Signal transducer and activator of transcription 1 (STAT1) and 3 (STAT3) accession numbers, namely P42227-2 and P42225. We obtain a new QFeatures instance containing 2 proteins, 9 peptides and 10 PSMS. From this, we can readily conclude that STAT1 was identified by a single PSM/peptide.
stat <- hl[c("P42227-2", "P42225"), , ]
stat## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [1] psms: SummarizedExperiment with 10 rows and 10 columns
## [2] peptides: SummarizedExperiment with 9 rows and 10 columns
## [3] proteins: SummarizedExperiment with 2 rows and 10 columnsBelow, we visualise the expression profiles for the two proteins.
stat_df <- data.frame(longForm(stat))
stat_df$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 thesubsetByFeature function, for use with the pipe operator.
hl |>
subsetByFeature("P42227-2")## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [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 columnshl |>
subsetByFeature(c("P42227-2", "P42225"))## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [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 columnsand possibly
hl |>
subsetByFeature("P42227-2") |>
longForm() |>
as.data.frame() |>
ggplot(aes(x = colname,
y = value,
group = rowname)) +
geom_line() +
facet_grid(~ assay)to reproduce the line plot.
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 usingAnnotationFilter objects from the r BiocStyle::Biocpkg("AnnotationFilter") package(Morgan and Rainer 2019). In addition to the pre-defined filters (such asSymbolFilter, 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: Mitochondrionqval_filter <- VariableFilter(field = "qValue",
value = 0.001,
condition = "<=")
qval_filter## class: NumericVariableFilter
## condition: <=
## value: 0.001These 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)## 'markers' found in 3 out of 3 assay(s).## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [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 columnsThe qval_filter, on the other hand, will only return a subset of PSMs, because the qValue variable is only present in the psmsassays. The q-values are only relevant to PSMs and that variable was dropped from the other assays.
filterFeatures(hl, qval_filter)## 'qValue' found in 1 out of 3 assay(s).## No filter applied to the following assay(s) because one or more
## filtering variables are missing in the rowData: peptides, proteins. You
## can control whether to remove or keep the features using the 'keep'
## argument (see '?filterFeature').## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [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 columnsThe same filters can be created using the forumla interface:
filterFeatures(hl, ~ markers == "Mitochondrion")## 'markers' found in 3 out of 3 assay(s).## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [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 columnsfilterFeatures(hl, ~ qValue <= 0.001)## 'qValue' found in 1 out of 3 assay(s).## No filter applied to the following assay(s) because one or more
## filtering variables are missing in the rowData: peptides, proteins. You
## can control whether to remove or keep the features using the 'keep'
## argument (see '?filterFeature').## An instance of class QFeatures (type: bulk) with 3 sets:
##
## [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 columnsSession information
## R version 4.5.0 (2025-04-11)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] gplots_3.2.0 dplyr_1.1.4
## [3] ggplot2_3.5.2 QFeatures_1.19.2
## [5] MultiAssayExperiment_1.35.3 SummarizedExperiment_1.39.0
## [7] Biobase_2.69.0 GenomicRanges_1.61.0
## [9] GenomeInfoDb_1.45.3 IRanges_2.43.0
## [11] S4Vectors_0.47.0 BiocGenerics_0.55.0
## [13] generics_0.1.4 MatrixGenerics_1.21.0
## [15] matrixStats_1.5.0 BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.52 bslib_0.9.0
## [4] caTools_1.18.3 lattice_0.22-7 bitops_1.0-9
## [7] vctrs_0.6.5 tools_4.5.0 tibble_3.2.1
## [10] cluster_2.1.8.1 pkgconfig_2.0.3 BiocBaseUtils_1.11.0
## [13] KernSmooth_2.23-26 Matrix_1.7-3 RColorBrewer_1.1-3
## [16] lifecycle_1.0.4 farver_2.1.2 compiler_4.5.0
## [19] stringr_1.5.1 statmod_1.5.0 tinytex_0.57
## [22] clue_0.3-66 htmltools_0.5.8.1 sass_0.4.10
## [25] yaml_2.3.10 lazyeval_0.2.2 pillar_1.10.2
## [28] crayon_1.5.3 jquerylib_0.1.4 tidyr_1.3.1
## [31] MASS_7.3-65 limma_3.65.1 DelayedArray_0.35.1
## [34] cachem_1.1.0 magick_2.8.6 abind_1.4-8
## [37] gtools_3.9.5 tidyselect_1.2.1 digest_0.6.37
## [40] stringi_1.8.7 reshape2_1.4.4 purrr_1.0.4
## [43] bookdown_0.43 labeling_0.4.3 fastmap_1.2.0
## [46] grid_4.5.0 cli_3.6.5 SparseArray_1.9.0
## [49] magrittr_2.0.3 S4Arrays_1.9.0 dichromat_2.0-0.1
## [52] withr_3.0.2 UCSC.utils_1.5.0 scales_1.4.0
## [55] rmarkdown_2.29 XVector_0.49.0 httr_1.4.7
## [58] igraph_2.1.4 evaluate_1.0.3 knitr_1.50
## [61] rlang_1.1.6 Rcpp_1.0.14 glue_1.8.0
## [64] BiocManager_1.30.25 msdata_0.49.0 jsonlite_2.0.0
## [67] AnnotationFilter_1.33.0 R6_2.6.1 plyr_1.8.9
## [70] ProtGenerics_1.41.0 MsCoreUtils_1.21.0References
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.
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.