QFeatures in a nutshell

This vignette briefly recaps the main concepts of QFeatures on which scp relies. More in depth information is to be found in the QFeatures vignettes.

The QFeatures class

The QFeatures class (Gatto and Vanderaa (2023)) is based on the MultiAssayExperiment class that holds a collection of SummarizedExperiment (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_FP97AG_RI1 190321S_LCA10_X_FP97AG_RI2
#> PSM3773                       57895                     603.73
#> PSM9078                       64889                    1481.30
#> PSM9858                       58993                     489.85
#> PSM11744                      75711                     539.02
#> PSM21752                          0                       0.00
#>          190321S_LCA10_X_FP97AG_RI3 190321S_LCA10_X_FP97AG_RI4
#> 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_FP97AG_RI5 190321S_LCA10_X_FP97AG_RI6
#> 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_FP97AG_RI7 190321S_LCA10_X_FP97AG_RI8
#> 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_FP97AG_RI9 190321S_LCA10_X_FP97AG_RI10
#> 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_FP97AG_RI11
#> PSM3773                       742.53
#> PSM9078                      1062.80
#> PSM9858                       768.61
#> PSM11744                      481.38
#> PSM21752                        0.00

Note that you can retrieve the list of available assays in a QFeatures 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"

Feature metadata

For each individual assay, there is feature metadata available. We extract the list of metadata tables by using rowData() on the QFeatures object.

rowData(scp1)
#> DataFrameList of length 5
#> names(5): 190321S_LCA10_X_FP97AG 190222S_LCA9_X_FP94BM 190914S_LCB3_X_16plex_Set_21 peptides proteins
rowData(scp1)[["proteins"]]
#> DataFrame with 292 rows and 9 columns
#>                                            protein Match.time.difference
#>                                        <character>             <logical>
#> A1A519                                      A1A519                    NA
#> A5D8V6                                      A5D8V6                    NA
#> A5PLK6                                      A5PLK6                    NA
#> A5PLL1                                      A5PLL1                    NA
#> A6NC97                                      A6NC97                    NA
#> ...                                            ...                   ...
#> REV__CON__ENSEMBL:ENSBTAP00000038253 REV__CON__...                    NA
#> REV__CON__P06868                     REV__CON__...                    NA
#> REV__CON__Q05443                     REV__CON__...                    NA
#> REV__CON__Q32PI4                     REV__CON__...                    NA
#> REV__CON__Q3MHN5                     REV__CON__...                    NA
#>                                      Match.m.z.difference Match.q.value
#>                                                 <logical>     <logical>
#> A1A519                                                 NA            NA
#> A5D8V6                                                 NA            NA
#> A5PLK6                                                 NA            NA
#> A5PLL1                                                 NA            NA
#> A6NC97                                                 NA            NA
#> ...                                                   ...           ...
#> REV__CON__ENSEMBL:ENSBTAP00000038253                   NA            NA
#> REV__CON__P06868                                       NA            NA
#> REV__CON__Q05443                                       NA            NA
#> REV__CON__Q32PI4                                       NA            NA
#> REV__CON__Q3MHN5                                       NA            NA
#>                                      Match.score Reporter.PIF Reporter.fraction
#>                                        <logical>    <logical>         <logical>
#> A1A519                                        NA           NA                NA
#> A5D8V6                                        NA           NA                NA
#> A5PLK6                                        NA           NA                NA
#> A5PLL1                                        NA           NA                NA
#> A6NC97                                        NA           NA                NA
#> ...                                          ...          ...               ...
#> REV__CON__ENSEMBL:ENSBTAP00000038253          NA           NA                NA
#> REV__CON__P06868                              NA           NA                NA
#> REV__CON__Q05443                              NA           NA                NA
#> REV__CON__Q32PI4                              NA           NA                NA
#> REV__CON__Q3MHN5                              NA           NA                NA
#>                                      Potential.contaminant        .n
#>                                                <character> <integer>
#> A1A519                                                             1
#> A5D8V6                                                             1
#> A5PLK6                                                             1
#> A5PLL1                                                             1
#> A6NC97                                                             1
#> ...                                                    ...       ...
#> REV__CON__ENSEMBL:ENSBTAP00000038253                     +         1
#> REV__CON__P06868                                         +         1
#> REV__CON__Q05443                                         +         1
#> REV__CON__Q32PI4                                         +         1
#> REV__CON__Q3MHN5                                         +         1

You can also retrieve the names of each rowData column for all assays with rowDataNames.

rowDataNames(scp1)
#> CharacterList of length 5
#> [["190321S_LCA10_X_FP97AG"]] uid Sequence ... peptide Leading.razor.protein
#> [["190222S_LCA9_X_FP94BM"]] uid Sequence ... peptide Leading.razor.protein
#> [["190914S_LCB3_X_16plex_Set_21"]] uid Sequence ... Leading.razor.protein
#> [["peptides"]] Sequence Length Modifications ... .n Leading.razor.protein
#> [["proteins"]] protein Match.time.difference ... Potential.contaminant .n

You can also get the rowData from different assays in a single table using the rbindRowData function. It will keep the common rowData variables to all selected assays (provided through i).

rbindRowData(scp1, i = 1:5)
#> DataFrame with 1388 rows and 10 columns
#>              assay       rowname       protein Match.time.difference
#>        <character>   <character>   <character>             <logical>
#> 1    190321S_LC...       PSM3773        P61981                    NA
#> 2    190321S_LC...       PSM9078        Q8WVN8                    NA
#> 3    190321S_LC...       PSM9858        P55084                    NA
#> 4    190321S_LC...      PSM11744        P19099                    NA
#> 5    190321S_LC...      PSM21752        P52952                    NA
#> ...            ...           ...           ...                   ...
#> 1384      proteins REV__CON__... REV__CON__...                    NA
#> 1385      proteins REV__CON__... REV__CON__...                    NA
#> 1386      proteins REV__CON__... REV__CON__...                    NA
#> 1387      proteins REV__CON__... REV__CON__...                    NA
#> 1388      proteins REV__CON__... REV__CON__...                    NA
#>      Match.m.z.difference Match.q.value Match.score Reporter.PIF
#>                 <logical>     <logical>   <logical>    <logical>
#> 1                      NA            NA          NA           NA
#> 2                      NA            NA          NA           NA
#> 3                      NA            NA          NA           NA
#> 4                      NA            NA          NA           NA
#> 5                      NA            NA          NA           NA
#> ...                   ...           ...         ...          ...
#> 1384                   NA            NA          NA           NA
#> 1385                   NA            NA          NA           NA
#> 1386                   NA            NA          NA           NA
#> 1387                   NA            NA          NA           NA
#> 1388                   NA            NA          NA           NA
#>      Reporter.fraction Potential.contaminant
#>              <logical>           <character>
#> 1                   NA                      
#> 2                   NA                      
#> 3                   NA                      
#> 4                   NA                      
#> 5                   NA                      
#> ...                ...                   ...
#> 1384                NA                     +
#> 1385                NA                     +
#> 1386                NA                     +
#> 1387                NA                     +
#> 1388                NA                     +

Sample metadata

The sample metadata is retrieved using colData on the QFeatures object.

colData(scp1)
#> DataFrame with 38 rows and 7 columns
#>                                             Set     Channel SampleAnnotation
#>                                     <character> <character>      <character>
#> 190222S_LCA9_X_FP94BM_RI1         190222S_LC...         RI1    carrier_mi...
#> 190222S_LCA9_X_FP94BM_RI2         190222S_LC...         RI2             norm
#> 190222S_LCA9_X_FP94BM_RI3         190222S_LC...         RI3           unused
#> 190222S_LCA9_X_FP94BM_RI4         190222S_LC...         RI4             sc_u
#> 190222S_LCA9_X_FP94BM_RI5         190222S_LC...         RI5             sc_0
#> ...                                         ...         ...              ...
#> 190914S_LCB3_X_16plex_Set_21_RI12 190914S_LC...        RI12            sc_m0
#> 190914S_LCB3_X_16plex_Set_21_RI13 190914S_LC...        RI13            sc_m0
#> 190914S_LCB3_X_16plex_Set_21_RI14 190914S_LC...        RI14            sc_m0
#> 190914S_LCB3_X_16plex_Set_21_RI15 190914S_LC...        RI15            sc_m0
#> 190914S_LCB3_X_16plex_Set_21_RI16 190914S_LC...        RI16            sc_m0
#>                                    SampleType     lcbatch     sortday
#>                                   <character> <character> <character>
#> 190222S_LCA9_X_FP94BM_RI1             Carrier        LCA9          s8
#> 190222S_LCA9_X_FP94BM_RI2           Reference        LCA9          s8
#> 190222S_LCA9_X_FP94BM_RI3              Unused        LCA9          s8
#> 190222S_LCA9_X_FP94BM_RI4            Monocyte        LCA9          s8
#> 190222S_LCA9_X_FP94BM_RI5               Blank        LCA9          s8
#> ...                                       ...         ...         ...
#> 190914S_LCB3_X_16plex_Set_21_RI12  Macrophage        LCB3          s9
#> 190914S_LCB3_X_16plex_Set_21_RI13  Macrophage        LCB3          s9
#> 190914S_LCB3_X_16plex_Set_21_RI14  Macrophage        LCB3          s9
#> 190914S_LCB3_X_16plex_Set_21_RI15  Macrophage        LCB3          s9
#> 190914S_LCB3_X_16plex_Set_21_RI16  Macrophage        LCB3          s9
#>                                        digest
#>                                   <character>
#> 190222S_LCA9_X_FP94BM_RI1                   N
#> 190222S_LCA9_X_FP94BM_RI2                   N
#> 190222S_LCA9_X_FP94BM_RI3                   N
#> 190222S_LCA9_X_FP94BM_RI4                   N
#> 190222S_LCA9_X_FP94BM_RI5                   N
#> ...                                       ...
#> 190914S_LCB3_X_16plex_Set_21_RI12           R
#> 190914S_LCB3_X_16plex_Set_21_RI13           R
#> 190914S_LCB3_X_16plex_Set_21_RI14           R
#> 190914S_LCB3_X_16plex_Set_21_RI15           R
#> 190914S_LCB3_X_16plex_Set_21_RI16           R

Note that you can easily access a colData column using the $ operator. See here how we extract the sample types from the colData.

scp1$SampleType
#>  [1] "Carrier"    "Reference"  "Unused"     "Monocyte"   "Blank"     
#>  [6] "Monocyte"   "Macrophage" "Macrophage" "Macrophage" "Macrophage"
#> [11] "Macrophage" "Carrier"    "Reference"  "Unused"     "Macrophage"
#> [16] "Monocyte"   "Macrophage" "Macrophage" "Macrophage" "Macrophage"
#> [21] "Macrophage" "Macrophage" "Carrier"    "Reference"  "Unused"    
#> [26] "Unused"     "Macrophage" "Macrophage" "Blank"      "Monocyte"  
#> [31] "Macrophage" "Monocyte"   "Blank"      "Macrophage" "Macrophage"
#> [36] "Macrophage" "Macrophage" "Macrophage"

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 containing 1 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 11 columns

An alternative that results in exactly the same output is using the subsetByAssay 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 containing 1 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 11 columns

Subset 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 containing 5 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 7 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 176 rows and 5 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 215 rows and 8 columns 
#>  [4] peptides: SingleCellExperiment with 539 rows and 20 columns 
#>  [5] proteins: SingleCellExperiment with 292 rows and 20 columns

An alternative that results in exactly the same output is using the subsetByColData method.

subsetByColData(scp1, scp1$SampleType == "Macrophage")
#> An instance of class QFeatures containing 5 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 7 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 176 rows and 5 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 215 rows and 8 columns 
#>  [4] peptides: SingleCellExperiment with 539 rows and 20 columns 
#>  [5] proteins: SingleCellExperiment with 292 rows and 20 columns

Subset 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 the Q02878 protein.

scp1["Q02878", , ]
#> An instance of class QFeatures containing 5 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 9 rows and 11 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 10 rows and 11 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 0 rows and 16 columns 
#>  [4] peptides: SingleCellExperiment with 11 rows and 38 columns 
#>  [5] proteins: SingleCellExperiment with 1 rows and 38 columns

You 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 the subsetByColData method.

subsetByFeature(scp1, "Q02878")
#> An instance of class QFeatures containing 5 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 9 rows and 11 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 10 rows and 11 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 0 rows and 16 columns 
#>  [4] peptides: SingleCellExperiment with 11 rows and 38 columns 
#>  [5] proteins: SingleCellExperiment with 1 rows and 38 columns

You 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 containing 5 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 126 rows and 11 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 132 rows and 11 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 176 rows and 16 columns 
#>  [4] peptides: SingleCellExperiment with 422 rows and 38 columns 
#>  [5] proteins: SingleCellExperiment with 0 rows and 38 columns

Note 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 using filterNA. 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 containing 5 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 11 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 176 rows and 11 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 215 rows and 16 columns 
#>  [4] peptides: SingleCellExperiment with 539 rows and 38 columns 
#>  [5] proteins: SingleCellExperiment with 105 rows and 38 columns

Common 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 the names() of the QFeautres object. A detailed cheatsheet about regex in R can be found here.

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 use zeroIsNa to automatically convert 0 values to NA in assays of interest. For instance, we here replace missing data in the peptides assay.

table(assay(scp1, "peptides") == 0)
#> 
#> FALSE  TRUE 
#>  5611  1509
scp1 <-zeroIsNA(scp1, "peptides")
table(assay(scp1, "peptides") == 0)
#> 
#> FALSE 
#>  5611

Feature 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
#> An instance of class QFeatures containing 6 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 11 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 176 rows and 11 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 215 rows and 16 columns 
#>  [4] peptides: SingleCellExperiment with 539 rows and 38 columns 
#>  [5] proteins: SingleCellExperiment with 292 rows and 38 columns 
#>  [6] 190321S_LCA10_X_FP97AG_aggr: SingleCellExperiment with 100 rows and 11 columns

You 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 containing 6 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 11 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 176 rows and 11 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 215 rows and 16 columns 
#>  [4] peptides: SingleCellExperiment with 539 rows and 38 columns 
#>  [5] proteins: SingleCellExperiment with 292 rows and 38 columns 
#>  [6] proteins_mcenter: SingleCellExperiment with 292 rows and 38 columns

Other 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 containing 6 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 11 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 176 rows and 11 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 215 rows and 16 columns 
#>  [4] peptides: SingleCellExperiment with 539 rows and 38 columns 
#>  [5] proteins: SingleCellExperiment with 292 rows and 38 columns 
#>  [6] proteins_sf: SingleCellExperiment with 292 rows and 38 columns

Log 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 containing 6 assays:
#>  [1] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 166 rows and 11 columns 
#>  [2] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 176 rows and 11 columns 
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 215 rows and 16 columns 
#>  [4] peptides: SingleCellExperiment with 539 rows and 38 columns 
#>  [5] proteins: SingleCellExperiment with 292 rows and 38 columns 
#>  [6] proteins_log: SingleCellExperiment with 292 rows and 38 columns

Imputation

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] TRUE

Data 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 or tibbles 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 longFormat function to transform a QFeatures object 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 <- longFormat(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 QFeatures object is provided in the QFeautres visualization vignette.

Session information

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

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       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggplot2_3.5.1               scp_1.17.0                 
 [3] QFeatures_1.16.0            MultiAssayExperiment_1.33.0
 [5] SummarizedExperiment_1.36.0 Biobase_2.67.0             
 [7] GenomicRanges_1.59.0        GenomeInfoDb_1.43.0        
 [9] IRanges_2.41.0              S4Vectors_0.44.0           
[11] BiocGenerics_0.53.0         MatrixGenerics_1.19.0      
[13] matrixStats_1.4.1           BiocStyle_2.35.0           

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1            farver_2.1.2               
 [3] dplyr_1.1.4                 fastmap_1.2.0              
 [5] SingleCellExperiment_1.28.0 lazyeval_0.2.2             
 [7] nipals_0.8                  digest_0.6.37              
 [9] lifecycle_1.0.4             cluster_2.1.6              
[11] ProtGenerics_1.38.0         magrittr_2.0.3             
[13] compiler_4.4.1              rlang_1.1.4                
[15] sass_0.4.9                  tools_4.4.1                
[17] igraph_2.1.1                utf8_1.2.4                 
[19] yaml_2.3.10                 knitr_1.48                 
[21] labeling_0.4.3              S4Arrays_1.6.0             
[23] DelayedArray_0.33.1         plyr_1.8.9                 
[25] RColorBrewer_1.1-3          abind_1.4-8                
[27] withr_3.0.2                 purrr_1.0.2                
[29] sys_3.4.3                   grid_4.4.1                 
[31] fansi_1.0.6                 colorspace_2.1-1           
[33] scales_1.3.0                MASS_7.3-61                
[35] cli_3.6.3                   rmarkdown_2.28             
[37] crayon_1.5.3                generics_0.1.3             
[39] metapod_1.14.0              httr_1.4.7                 
[41] reshape2_1.4.4              BiocBaseUtils_1.9.0        
[43] cachem_1.1.0                stringr_1.5.1              
[45] zlibbioc_1.52.0             impute_1.81.0              
[47] AnnotationFilter_1.31.0     BiocManager_1.30.25        
[49] XVector_0.46.0              vctrs_0.6.5                
[51] Matrix_1.7-1                jsonlite_1.8.9             
[53] slam_0.1-54                 IHW_1.35.0                 
[55] ggrepel_0.9.6               clue_0.3-65                
[57] maketools_1.3.1             tidyr_1.3.1                
[59] jquerylib_0.1.4             glue_1.8.0                 
[61] stringi_1.8.4               gtable_0.3.6               
[63] UCSC.utils_1.2.0            munsell_0.5.1              
[65] lpsymphony_1.35.0           tibble_3.2.1               
[67] pillar_1.9.0                htmltools_0.5.8.1          
[69] GenomeInfoDbData_1.2.13     R6_2.5.1                   
[71] evaluate_1.0.1              lattice_0.22-6             
[73] highr_0.11                  bslib_0.8.0                
[75] Rcpp_1.0.13                 fdrtool_1.2.18             
[77] SparseArray_1.6.0           xfun_0.48                  
[79] MsCoreUtils_1.19.0          buildtools_1.0.0           
[81] pkgconfig_2.0.3

License

This vignette is distributed under a CC BY-SA license license.

Reference

Gatto, Laurent, and Christophe Vanderaa. 2023. “QFeatures: Quantitative Features for Mass Spectrometry Data.” https://doi.org/10.18129/B9.bioc.QFeatures.