QFeatures in a nutshell (original) (raw)
This vignette briefly recaps the main concepts of QFeatures on whichscp relies. More in depth information is to be found in theQFeatures vignettes.
The QFeatures class
The QFeatures class (Gatto and Vanderaa (2023)) is based on theMultiAssayExperiment class that holds a collection ofSummarizedExperiment (or other classes that inherits from it) objects termed assays. The assays in a QFeatures object have a hierarchical relation: proteins are composed of peptides, themselves produced by spectra, as depicted in figure below.
A more technical representation is shown below, highlighting that each assay is a SummarizedExperiment (containing the quantitative data, row and column annotations for each individual assay), as well as a global sample annotation table, that annotates cells across all assays.
Those links are stored as part as the QFeatures object and connect the assays together. We load an example dataset from the scp package that is formatted as an QFeatures object and plot those connection.
library(scp)
data("scp1")
plot(scp1)Accessing the data
The QFeatures class contains all the available and metadata. We here show how to retrieve those different pieces of information.
Quantitative data
The quantitative data, stored as matrix-like objects, can be accessed using the assay function. For example, we here extract the quantitative data for the first MS batch (and show a subset of it):
assay(scp1, "190321S_LCA10_X_FP97AG")[1:5, ]
#> 190321S_LCA10_X_FP97AGRI1 190321S_LCA10_X_FP97AGRI2
#> PSM3773 57895 603.73
#> PSM9078 64889 1481.30
#> PSM9858 58993 489.85
#> PSM11744 75711 539.02
#> PSM21752 0 0.00
#> 190321S_LCA10_X_FP97AGRI3 190321S_LCA10_X_FP97AGRI4
#> PSM3773 2787.9 757.17
#> PSM9078 4891.6 597.53
#> PSM9858 2899.4 882.37
#> PSM11744 7292.7 357.90
#> PSM21752 0.0 0.00
#> 190321S_LCA10_X_FP97AGRI5 190321S_LCA10_X_FP97AGRI6
#> PSM3773 862.08 1118.80
#> PSM9078 1140.30 1300.10
#> PSM9858 296.60 977.15
#> PSM11744 1091.30 736.87
#> PSM21752 0.00 0.00
#> 190321S_LCA10_X_FP97AGRI7 190321S_LCA10_X_FP97AGRI8
#> PSM3773 640.10 1446.10
#> PSM9078 1092.50 1309.40
#> PSM9858 498.60 1437.90
#> PSM11744 712.74 590.75
#> PSM21752 0.00 0.00
#> 190321S_LCA10_X_FP97AGRI9 190321S_LCA10_X_FP97AGRI10
#> PSM3773 968.49 648.56
#> PSM9078 1538.40 1014.50
#> PSM9858 857.40 888.01
#> PSM11744 15623.00 298.60
#> PSM21752 0.00 0.00
#> 190321S_LCA10_X_FP97AGRI11
#> PSM3773 742.53
#> PSM9078 1062.80
#> PSM9858 768.61
#> PSM11744 481.38
#> PSM21752 0.00Note that you can retrieve the list of available assays in aQFeatures object using the names() function.
names(scp1)
#> [1] "190321S_LCA10_X_FP97AG" "190222S_LCA9_X_FP94BM"
#> [3] "190914S_LCB3_X_16plex_Set_21" "peptides"
#> [5] "proteins"Subsetting the data
There are three dimensions we want to subset for:
- Assays
- Samples
- Features
Therefore, QFeatures support a three-index subsetting. This is performed through the simple bracket method [feature, sample, assay].
Subset assays
Suppose that we want to focus only on the first MS batch (190321S_LCA10_X_FP97AG) for separate processing of the data. Subsetting the QFeatures object for that assay is simply:
scp1[, , "190321S_LCA10_X_FP97AG"]
#> harmonizing input:
#> removing 103 sampleMap rows not in names(experiments)
#> removing 27 colData rownames not in sampleMap 'primary'
#> An instance of class QFeatures (type: bulk) with 1 set:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 11 columnsAn alternative that results in exactly the same output is using thesubsetByAssay method.
subsetByAssay(scp1, "190321S_LCA10_X_FP97AG")
#> harmonizing input:
#> removing 103 sampleMap rows not in names(experiments)
#> removing 27 colData rownames not in sampleMap 'primary'
#> An instance of class QFeatures (type: bulk) with 1 set:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 11 columnsSubset samples
Subsetting samples is often performed after sample QC where we want to keep only quality samples and sample of interest. In our example, the different samples are either technical controls or single-cells (macrophages and monocytes). Suppose we are only interested in macrophages, we can subset the data as follows:
scp1[, scp1$SampleType == "Macrophage", ]
#> An instance of class QFeatures (type: bulk) with 5 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 7 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 176 rows and 5 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 215 rows and 8 columns
#> [4] peptides: SummarizedExperiment with 539 rows and 20 columns
#> [5] proteins: SummarizedExperiment with 292 rows and 20 columnsAn alternative that results in exactly the same output is using thesubsetByColData method.
subsetByColData(scp1, scp1$SampleType == "Macrophage")
#> An instance of class QFeatures (type: bulk) with 5 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 7 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 176 rows and 5 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 215 rows and 8 columns
#> [4] peptides: SummarizedExperiment with 539 rows and 20 columns
#> [5] proteins: SummarizedExperiment with 292 rows and 20 columnsSubset features
Subsetting for features does more than simply subsetting for the features of interest, it will also take the features that are linked to that feature. Here is an example, suppose we are interested in theQ02878 protein.
scp1["Q02878", , ]
#> An instance of class QFeatures (type: bulk) with 5 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 9 rows and 11 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 10 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 0 rows and 16 columns
#> [4] peptides: SummarizedExperiment with 11 rows and 38 columns
#> [5] proteins: SummarizedExperiment with 1 rows and 38 columnsYou can see it indeed retrieved that protein from the proteins assay, but it also retrieved 11 associated peptides in the peptides assay and 19 associated PSMs in 2 different MS runs.
An alternative that results in exactly the same output is using thesubsetByColData method.
subsetByFeature(scp1, "Q02878")
#> An instance of class QFeatures (type: bulk) with 5 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 9 rows and 11 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 10 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 0 rows and 16 columns
#> [4] peptides: SummarizedExperiment with 11 rows and 38 columns
#> [5] proteins: SummarizedExperiment with 1 rows and 38 columnsYou can also subset features based on the rowData. This is performed by filterFeatures. For example, we want to remove features that are associated to reverse sequence hits.
filterFeatures(scp1, ~ Reverse != "+")
#> 'Reverse' found in 4 out of 5 assay(s).
#> No filter applied to the following assay(s) because one or more
#> filtering variables are missing in the rowData: 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 5 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 126 rows and 11 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 132 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 176 rows and 16 columns
#> [4] peptides: SummarizedExperiment with 422 rows and 38 columns
#> [5] proteins: SummarizedExperiment with 0 rows and 38 columnsNote however that if an assay is missing the variable that is used to filter the data (in this case the proteins assay), then all features for that assay are removed.
You can also subset the data based on the feature missingness usingfilterNA. In this example, we filter out proteins with more than 70 % missing data.
filterNA(scp1, i = "proteins", pNA = 0.7)
#> An instance of class QFeatures (type: bulk) with 5 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 11 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 176 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 215 rows and 16 columns
#> [4] peptides: SummarizedExperiment with 539 rows and 38 columns
#> [5] proteins: SummarizedExperiment with 105 rows and 38 columnsCommon processing steps
We here provide a list of common processing steps that are encountered in single-cell proteomics data processing and that are already available in the QFeatures package.
All functions below require the user to select one or more assays from the QFeatures object. This is passed through the i argument. Note that some datasets may contain hundreds of assays and providing the assay selection manually can become cumbersome. We therefore suggest the user to use regular expression (aka regex) to chose from thenames() of the QFeautres object. A detailed cheatsheet about regex in R can be foundhere.
Missing data assignment
It often occurs that in MS experiements, 0 values are not true zeros but rather signal that is too weak to be detected. Therefore, it is advised to consider 0 values as missing data (NA). You can usezeroIsNa to automatically convert 0 values to NA in assays of interest. For instance, we here replace missing data in the peptidesassay.
table(assay(scp1, "peptides") == 0)
#>
#> FALSE TRUE
#> 5611 1509
scp1 <-zeroIsNA(scp1, "peptides")
table(assay(scp1, "peptides") == 0)
#>
#> FALSE
#> 5611Feature aggregation
Shotgun proteomics analyses, bulk as well as single-cell, acquire and quantify peptides. However, biological inference is often performed at protein level. Protein quantitations can be estimated through feature aggregation. This is performed by aggregateFeatures, a function that takes an assay from the Qfeatures object and that aggregates its features with respect to a grouping variable in the rowData (fcol) and an aggregation function.
aggregateFeatures(scp1, i = "190321S_LCA10_X_FP97AG", fcol = "protein",
name = "190321S_LCA10_X_FP97AG_aggr",
fun = MsCoreUtils::robustSummary)
#> 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.
#> Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20
#> steps
#>
Aggregated: 1/1
#> An instance of class QFeatures (type: bulk) with 6 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 11 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 176 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 215 rows and 16 columns
#> [4] peptides: SummarizedExperiment with 539 rows and 38 columns
#> [5] proteins: SummarizedExperiment with 292 rows and 38 columns
#> [6] 190321S_LCA10_X_FP97AG_aggr: SummarizedExperiment with 100 rows and 11 columnsYou can see that the aggregated function is added as a new assay to the QFeatures object. Note also that, under the hood,aggregateFeatures keeps track of the relationship between the features of the newly aggregated assay and its parent.
Normalization
An ubiquituous step that is performed in biological data analysis is normalization that is meant to remove undesired variability and to make different samples comparable. The normalize function offers an interface to a wide variety of normalization methods. See?MsCoreUtils::normalize_matrix for more details about the available normalization methods. Below, we normalize the samples so that they are mean centered.
normalize(scp1, "proteins", method = "center.mean",
name = "proteins_mcenter")
#> An instance of class QFeatures (type: bulk) with 6 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 11 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 176 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 215 rows and 16 columns
#> [4] peptides: SummarizedExperiment with 539 rows and 38 columns
#> [5] proteins: SummarizedExperiment with 292 rows and 38 columns
#> [6] proteins_mcenter: SummarizedExperiment with 292 rows and 38 columnsOther custom normalization can be applied using the sweep method, where normalization factors have to be supplied manually. As an example, we here normalize the samples using a scaled size factor.
sf <- colSums(assay(scp1, "proteins"), na.rm = TRUE) / 1E4
sweep(scp1, i = "proteins",
MARGIN = 2, ## 1 = by feature; 2 = by sample
STATS = sf, FUN = "/",
name = "proteins_sf")
#> An instance of class QFeatures (type: bulk) with 6 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 11 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 176 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 215 rows and 16 columns
#> [4] peptides: SummarizedExperiment with 539 rows and 38 columns
#> [5] proteins: SummarizedExperiment with 292 rows and 38 columns
#> [6] proteins_sf: SummarizedExperiment with 292 rows and 38 columnsLog transformation
The QFeatures package also provide the logTransform function to facilitate the transformation of the quantitative data. We here show its usage by transforming the protein data using a base 2 logarithm with a pseudo-count of one.
logTransform(scp1, i = "proteins", base = 2, pc = 1,
name = "proteins_log")
#> An instance of class QFeatures (type: bulk) with 6 sets:
#>
#> [1] 190321S_LCA10_X_FP97AG: SummarizedExperiment with 166 rows and 11 columns
#> [2] 190222S_LCA9_X_FP94BM: SummarizedExperiment with 176 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SummarizedExperiment with 215 rows and 16 columns
#> [4] peptides: SummarizedExperiment with 539 rows and 38 columns
#> [5] proteins: SummarizedExperiment with 292 rows and 38 columns
#> [6] proteins_log: SummarizedExperiment with 292 rows and 38 columnsImputation
Finally, QFeatures offers an interface to a wide variety of imputation methods to replace missing data by estimated values. The list of available methods is given by ?MsCoreUtils::impute_matrix. We demonstrate the use of this function by replacing missing data using KNN imputation.
anyNA(assay(scp1, "proteins"))
#> [1] TRUE
scp1 <- impute(scp1, i = "proteins", method ="knn", k = 3)
#> Loading required namespace: impute
#> Imputing along margin 1 (features/rows).
#> Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 284 rows with more than 50 % entries missing;
#> mean imputation used for these rows
anyNA(assay(scp1, "proteins"))
#> [1] TRUEData visualization
Visualization of the feature and sample metadata is rather straightforward since those are stored as tables (see section_Accessing the data_). From those tables, any visualization tool can be applied. Note however that using ggplot2 require data.frames ortibbles but rowData and colData are stored as DFrames objects. You can easily convert one data format to another. For example, we plot the parental ion fraction (measure of spectral purity) for each of the three MS batches.
rd <- rbindRowData(scp1, i = 1:3)
library("ggplot2")
ggplot(data.frame(rd)) +
aes(y = PIF,
x = assay) +
geom_boxplot()
#> Warning: Removed 64 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).Combining the metadata and the quantitative data is more challenging since the risk of data mismatch is increased. The QFeatures package therefore provides th longForm function to transform a QFeaturesobject in a long DFrame table. For instance, we plot the quantitative data distribution for the first assay according to the acquisition channel index and colour with respect to the sample type. Both pieces of information are taken from the colData, so we provide them as colvars.
lf <- longForm(scp1[, , 1], colvars = c("SampleType", "Channel"))
#> harmonizing input:
#> removing 141 sampleMap rows not in names(experiments)
#> removing 27 colData rownames not in sampleMap 'primary'
ggplot(data.frame(lf)) +
aes(x = Channel,
y = value,
colour = SampleType) +
geom_boxplot()A more in-depth tutorial about data visualization from a QFeaturesobject is provided in the QFeautres visualizationvignette.
Session 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] ggplot2_3.5.2 scp_1.19.2
[3] QFeatures_1.19.2 MultiAssayExperiment_1.35.3
[5] SummarizedExperiment_1.39.0 Biobase_2.69.0
[7] GenomicRanges_1.61.0 GenomeInfoDb_1.45.4
[9] IRanges_2.43.0 S4Vectors_0.47.0
[11] BiocGenerics_0.55.0 generics_0.1.4
[13] MatrixGenerics_1.21.0 matrixStats_1.5.0
[15] BiocStyle_2.37.0
loaded via a namespace (and not attached):
[1] impute_1.83.0 gtable_0.3.6
[3] xfun_0.52 bslib_0.9.0
[5] ggrepel_0.9.6 lattice_0.22-7
[7] vctrs_0.6.5 tools_4.5.0
[9] tibble_3.2.1 cluster_2.1.8.1
[11] nipals_1.0 pkgconfig_2.0.3
[13] BiocBaseUtils_1.11.0 Matrix_1.7-3
[15] RColorBrewer_1.1-3 IHW_1.37.0
[17] lpsymphony_1.37.0 lifecycle_1.0.4
[19] farver_2.1.2 compiler_4.5.0
[21] stringr_1.5.1 clue_0.3-66
[23] htmltools_0.5.8.1 sass_0.4.10
[25] fdrtool_1.2.18 yaml_2.3.10
[27] lazyeval_0.2.2 pillar_1.10.2
[29] crayon_1.5.3 jquerylib_0.1.4
[31] tidyr_1.3.1 MASS_7.3-65
[33] SingleCellExperiment_1.31.0 DelayedArray_0.35.1
[35] cachem_1.1.0 abind_1.4-8
[37] metapod_1.17.0 tidyselect_1.2.1
[39] digest_0.6.37 slam_0.1-55
[41] stringi_1.8.7 dplyr_1.1.4
[43] reshape2_1.4.4 purrr_1.0.4
[45] bookdown_0.43 labeling_0.4.3
[47] fastmap_1.2.0 grid_4.5.0
[49] cli_3.6.5 SparseArray_1.9.0
[51] magrittr_2.0.3 S4Arrays_1.9.1
[53] dichromat_2.0-0.1 withr_3.0.2
[55] scales_1.4.0 UCSC.utils_1.5.0
[57] rmarkdown_2.29 XVector_0.49.0
[59] httr_1.4.7 igraph_2.1.4
[61] evaluate_1.0.3 knitr_1.50
[63] rlang_1.1.6 Rcpp_1.0.14
[65] glue_1.8.0 BiocManager_1.30.25
[67] jsonlite_2.0.0 AnnotationFilter_1.33.0
[69] R6_2.6.1 plyr_1.8.9
[71] ProtGenerics_1.41.0 MsCoreUtils_1.21.0