Quantitative features for mass spectrometry data
27 October 2020
Abstract
This vignette describes the functionality implemented in the QFeatures package. QFeatures provides infrastructure to manage and process quantitative features for high-throughput mass spectrometry assays, including proteomics and metabolomics.Package
QFeatures 1.0.0
1 Introduction
The QFeatures package provides infrastructure (that is classes and
the methods to process and manipulate them) to manage and analyse
quantitative features from mass spectrometry experiments. It is based
on the MultiAssayExperiment class from the MultiAssayExperiment
(Ramos et al. 2017). that stores a set of assays. Assays in a QFeatures
object have a specific relation, that is depicted in figure
1: assays in a QFeatures object are the result
of the aggregation of quantitative features of other assays. In the
case of a quantitative proteomics experiment, these different assays
would be PSMs, that are aggregated into peptides, that are themselves
aggregated into proteins.
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 columnsBelow, 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 0.29732174head(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 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 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)
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 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 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 columnshl$tag <- c("126", "127N", "127C", "128N", "128C", "129N", "129C",
"130N", "130C", "131")
colData(hl)## DataFrame with 10 rows and 1 column
## tag
## <character>
## X126 126
## X127C 127N
## X127N 127C
## X128C 128N
## X128N 128C
## X129C 129N
## X129N 129C
## X130C 130N
## X130N 130C
## X131 1313 Subsetting
One particularity of the QFeatures infrastructure is that the
features of the constitutive assays are linked through an aggregative
relation. This relation is recorded when creating new assays with
aggregateFeatures and is exploited when subsetting QFeature by their
feature names.
In the example below, we are interested in the Stat3B isoform of the Signal transducer and activator of transcription 3 (STAT3) with accession number P42227-2. This accession number corresponds to a feature name in the proteins assay. But this protein row was computed from 8 peptide rows in the peptides assay, themselves resulting from the aggregation of 8 rows in the psms assay.
stat3 <- hl["P42227-2", , ]
stat3## An instance of class QFeatures containing 3 assays:
## [1] psms: SummarizedExperiment with 9 rows and 10 columns
## [2] peptides: SummarizedExperiment with 8 rows and 10 columns
## [3] proteins: SummarizedExperiment with 1 rows and 10 columnsWe can easily visualise this new QFeatures object using ggplot2
once converted into a data.frame.
stat3_df <- data.frame(longFormat(stat3))
stat3_df$assay <- factor(stat3_df$assay,
levels = c("psms", "peptides", "proteins"))
library("ggplot2")
ggplot(data = stat3_df,
aes(x = colname,
y = value,
group = rowname)) +
geom_line() + geom_point() +
facet_grid(~ assay)
Below we repeat the same operation for the Signal transducer and
activator of transcription 1 (STAT1) and 3 (STAT3) accession numbers,
namely P42227-2 and P42225. We obtain a new QFeatures instance
containing 2 proteins, 9 peptides and 10 PSMS. From this, we can
readily conclude that STAT1 was identified by a single PSM/peptide.
stat <- hl[c("P42227-2", "P42225"), , ]
stat## An instance of class QFeatures containing 3 assays:
## [1] psms: SummarizedExperiment with 10 rows and 10 columns
## [2] peptides: SummarizedExperiment with 9 rows and 10 columns
## [3] proteins: SummarizedExperiment with 2 rows and 10 columnsBelow, we visualise the expression profiles for the two proteins.
stat_df <- data.frame(longFormat(stat))
stat_df$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 columnshl %>%
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 columnsand 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: 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)## 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 columnsThe 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)## harmonizing input:
## removing 20 sampleMap rows not in names(experiments)## An instance of class QFeatures containing 1 assays:
## [1] psms: SummarizedExperiment with 2466 rows and 10 columnsThe 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 columnsfilterFeatures(hl, ~ qValue <= 0.001)## harmonizing input:
## removing 20 sampleMap rows not in names(experiments)## An instance of class QFeatures containing 1 assays:
## [1] psms: SummarizedExperiment with 2466 rows and 10 columnsSession information
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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_3.1.0 magrittr_1.5
## [3] dplyr_1.0.2 ggplot2_3.3.2
## [5] QFeatures_1.0.0 MultiAssayExperiment_1.16.0
## [7] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
## [11] IRanges_2.24.0 S4Vectors_0.28.0
## [13] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
## [15] matrixStats_0.57.0 BiocStyle_2.18.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 msdata_0.29.0 lattice_0.20-41
## [4] gtools_3.8.2 digest_0.6.27 R6_2.4.1
## [7] evaluate_0.14 highr_0.8 pillar_1.4.6
## [10] zlibbioc_1.36.0 rlang_0.4.8 lazyeval_0.2.2
## [13] magick_2.5.0 Matrix_1.2-18 preprocessCore_1.52.0
## [16] rmarkdown_2.5 labeling_0.4.2 stringr_1.4.0
## [19] ProtGenerics_1.22.0 RCurl_1.98-1.2 munsell_0.5.0
## [22] DelayedArray_0.16.0 compiler_4.0.3 xfun_0.18
## [25] pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0
## [28] tibble_3.0.4 GenomeInfoDbData_1.2.4 bookdown_0.21
## [31] crayon_1.3.4 withr_2.3.0 MASS_7.3-53
## [34] bitops_1.0-6 grid_4.0.3 gtable_0.3.0
## [37] lifecycle_0.2.0 AnnotationFilter_1.14.0 MsCoreUtils_1.2.0
## [40] scales_1.1.1 KernSmooth_2.23-17 stringi_1.5.3
## [43] farver_2.0.3 XVector_0.30.0 limma_3.46.0
## [46] ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.4
## [49] tools_4.0.3 glue_1.4.2 purrr_0.3.4
## [52] yaml_2.2.1 colorspace_1.4-1 BiocManager_1.30.10
## [55] caTools_1.18.0 knitr_1.30References
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.