# 1 The scp package

The scp package is used to process and analyse mass spectrometry (MS)-based single cell proteomics (SCP) data. The functions rely on a specific data structure that wraps QFeatures objects (Gatto (2020)) around SingleCellExperiment objects (Amezquita et al. (2019)). This data structure could be seen as Matryoshka dolls were the SingleCellExperiment objects are small dolls contained in the bigger QFeatures doll.

The SingleCellExperiment class provides a dedicated framework for single-cell data. The SingleCellExperiment serves as an interface to many cutting-edge methods for processing, visualizing and analysis single-cell data. More information about the SingleCellExperiment class and associated methods can be found in the OSCA book.

The QFeatures class is a data framework dedicated to manipulate and process MS-based quantitative data. It preserves the relationship between the different levels of information: peptide to spectrum match (PSM) data, peptide data and protein data. The QFeatures package also provides an interface to many utility functions to streamline the processing MS data. More information about MS data analysis tools can be found in the RforMassSpectrometry project.

Before running the vignette we need to load the scp package.

library("scp")

We also load ggplot2, magrittr and dplyr for convenient data manipulation and plotting.

library("ggplot2")
library("magrittr")
library("dplyr")

# 2 Before you start

This vignette will guide you through some common steps of mass spectrometry-based single-cell proteomics (SCP) data analysis. SCP is an emerging field and further research is required to develop a principled analysis workflow. Therefore, we do not guarantee that the steps presented here are the best steps for this type of data analysis. This vignette performs the steps that were described in the SCoPE2 landmark paper (Specht et al. (2021)) and that were reproduced in another work using the scp package (Vanderaa and Gatto (2021)). The replication on the full SCoPE2 dataset using scp is available in this vignette. We hope to convince the reader that, although the workflow is probably not optimal, scp has the full potential to perform standardized and principled data analysis. All functions presented here are comprehensively documented, highly modular, can easily be extended with new algorithms. Suggestions, feature requests or bug reports are warmly welcome. Feel free to open an issue in the GitHub repository.

This workflow can be applied to any MS-based SCP data. The minimal requirement to follow this workflow is that the data should contain the following information:

• Raw.file: field in both the feature data and the sample data that gives the names of MS acquisition runs or files.
• Channel: field in the sample data that links to columns in the quantification data and that allows to link samples to MS channels (more details in another vignette).
• SampleType: field in the sample data that provides the type of sample that is acquired (carrier, reference, single-cell,…). Only needed for multiplexing experiments.
• Potential.contaminant: field in the feature data that marks contaminant peptides.
• Reverse: field in the feature data that marks reverse peptides.
• PIF: field in the feature data that provides spectral purity.
• PEP or dart_PEP: field in the feature data that provides peptide posterior error probabilities.
• Modified.sequence: field in the feature data that provides the peptide identifiers.
• Leading.razor.protein: field in the feature data that provides the protein identifiers.
• At least one field in the feature data that contains quantification values. In this case, there are 16 quantification columns named as Reporter.intensity. followed by an index (1 to 16).

Each required field will be described more in detail in the corresponding sections. Names can be adapted by the user to more meaningful ones or adapted to other output tables.

# 3 Read in SCP data

The first step is to read in the PSM quantification table generated by, for example, MaxQuant (Tyanova, Temu, and Cox (2016)). We created a small example data by subsetting the MaxQuant evidence.txt table provided in the SCoPE2 landmark paper (Specht et al. (2021)). The mqScpData table is a typical example of what you would get after reading in a CSV file using read.csv or read.table. See ?mqScpData for more information about the table content.

data("mqScpData")

We also provide an example of a sample annotation table that provides useful information about the samples that are present in the example data. See ?sampleAnnotation for more information about the table content.

data("sampleAnnotation")

As a note, the example sample data contains 5 different types of samples (SampleType) that can be found in a TMT-based SCP data set:

table(sampleAnnotation\$SampleType)
#>
#>      Blank    Carrier Macrophage   Monocyte  Reference     Unused
#>         19          3         20          5          3         14
• The carrier channels (Carrier) contain 200 cell equivalents and are meant to boost the peptide identification rate.
• The normalization channels (Reference) contain 5 cell equivalents and are used to partially correct for between-run variation.
• The unused channels (Unused) are channels that are left empty due to isotopic cross-contamination by the carrier channel.
• The negative control channels (Blank) contain samples that do not contain any cell but are processed as single-cell samples.
• The single-cell sample channels contain the single-cell samples of interest, that are macrophage (Macrophage) or monocyte (Monocyte).

Using readSCP, we combine both tables in a QFeatures object formatted as described above.

scp <- readSCP(featureData = mqScpData,
colData = sampleAnnotation,
channelCol = "Channel",
batchCol = "Raw.file",
removeEmptyCols = TRUE)
#> Splitting data based on 'Raw.file'
#> Formatting data as a 'QFeatures' object
scp
#> An instance of class QFeatures containing 4 assays:
#>  [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 395 rows and 11 columns
#>  [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 487 rows and 11 columns
#>  [3] 190321S_LCA10_X_FP97_blank_01: SingleCellExperiment with 109 rows and 11 columns
#>  [4] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 370 rows and 16 columns

See here that the 3 first assays contain 11 columns that correspond to the TMT-11 labels and the last assay contains 16 columns that correspond to the TMT-16 labels.

Important: More details about the usage of readSCP and how to read your own data set are provided in the Load data using readSCP vignette.

Another way to get an overview of the scp object is to plot the QFeatures object. This will create a graph where each node is an assay and links between assays are denoted as edges.

plot(scp)

# 4 Clean missing data

All single-cell data contain many zeros. The zeros can be biological zeros or technical zeros and differentiating between the two types is not a trivial task. To avoid artefacts in downstream steps, we replace the zeros by the missing value NA. The zeroIsNA function takes the QFeatures object and the name(s) or index/indices of the assay(s) to clean and automatically replaces any zero in the selected quantitative data by NA.

scp <- zeroIsNA(scp, i = 1:4)

# 5 Filter PSMs

A common steps in SCP is to filter out low-confidence PSMs. Each PSM assay contains feature meta-information that are stored in the rowData of the assays. The QFeatures package allows to quickly filter the rows of an assay by using these information. The available variables in the rowData are listed below for each assay.

rowDataNames(scp)
#> CharacterList of length 4
#> [["190222S_LCA9_X_FP94BM"]] uid Sequence Length ... residual participated
#> [["190321S_LCA10_X_FP97AG"]] uid Sequence Length ... residual participated
#> [["190321S_LCA10_X_FP97_blank_01"]] uid Sequence ... residual participated
#> [["190914S_LCB3_X_16plex_Set_21"]] uid Sequence ... residual participated

## 5.1 Filter features based on feature annotations

Below are some examples of criteria that are used to identify low-confidence. The information is readily available since this was computed by MaxQuant:

• Remove PSMs that are matched to contaminants
• Remove PSMs that are matched to the decoy database
• Keep PSMs that exhibit a high PIF (parental ion fraction), indicative of the purity of a spectrum

We can perform this filtering using the filterFeatures function from QFeatures. filterFeatures automatically accesses the feature annotations and selects the rows that meet the provided condition(s). For instance, Reverse != "+" keeps the rows for which the Reverse variable in the rowData is not "+" (i.e. the PSM is not matched to the decoy database).

scp <- filterFeatures(scp,
~ Reverse != "+" &
Potential.contaminant != "+" &
!is.na(PIF) & PIF > 0.8)
#> 'Reverse' found in 4 out of 4 assay(s)
#> 'Potential.contaminant' found in 4 out of 4 assay(s)
#> 'PIF' found in 4 out of 4 assay(s)

## 5.2 Filter assays based on detected features

To avoid proceeding with failed runs, another interesting filter is to remove assays with too few features. If a batch contains less than, for example, 150 features we can then suspect something wrong happened in that batch and it should be removed. Using dims, we can query the dimensions (hence the number of features and the number of samples) of all assays contained in the dataset.

dims(scp)
#>      190222S_LCA9_X_FP94BM 190321S_LCA10_X_FP97AG 190321S_LCA10_X_FP97_blank_01
#> [1,]                   283                    318                            60
#> [2,]                    11                     11                            11
#>      190914S_LCB3_X_16plex_Set_21
#> [1,]                          200
#> [2,]                           16

Actually, a QFeatures object can be seen as a three-order array: $$features \times samples \times assay$$. Hence, QFeatures supports three-order subsetting x[rows, columns, assays]. We first select the assays that have sufficient PSMs (the number of rows is greater than 150), and then subset the scp object for the assays that meet the criterion.

keepAssay <- dims(scp)[1, ] > 150
scp <- scp[, , keepAssay]
#> Warning: 'experiments' dropped; see 'metadata'
#> harmonizing input:
#>   removing 11 sampleMap rows not in names(experiments)
#>   removing 11 colData rownames not in sampleMap 'primary'
scp
#> An instance of class QFeatures containing 3 assays:
#>  [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 283 rows and 11 columns
#>  [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 318 rows and 11 columns
#>  [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 200 rows and 16 columns

Notice the 190321S_LCA10_X_FP97_blank_01 sample was removed because it did not contain sufficient features, as expected from a blank run. This could also have been the case for failed runs.

## 5.3 Filter features based on SCP metrics

Another type of filtering is specific to SCP. In the SCoPE2 analysis, the authors suggest a filters based on the sample to carrier ratio (SCR), that is the reporter ion intensity of a single-cell sample divided by the reporter ion intensity of the carrier channel (200 cells) from the same batch. It is expected that the carrier intensities are much higher than the single-cell intensities.

The SCR can be computed using the computeSCR function from scp. The function must be told which channels are the samples that must be divided and which channel contains the carrier. This information is provided in the sample annotations and is accessed using the colData, under the SampleType field.

table(colData(scp)[, "SampleType"])
#>
#>      Blank    Carrier Macrophage   Monocyte  Reference     Unused
#>          3          3         20          5          3          4

In this dataset, SampleType gives the type of sample that is present in each TMT channel. The SCoPE2 protocole includes 5 types of samples:

• The carrier channels (Carrier) contain 200 cell equivalents and are meant to boost the peptide identification rate.
• The normalization channels (Reference) contain 5 cell equivalents and are used to partially correct for between-run variation.
• The unused channels (Unused) are channels that are left empty due to isotopic cross-contamination by the carrier channel.
• The negative control channels (Blank) contain samples that do not contain any cell but are processed as single-cell samples.
• The single-cell sample channels contain the single-cell samples of interest, that are macrophage (Macrophage) or monocyte (Monocyte).

The computeSCR function expects the following input:

• The QFeatures dataset
• The assay name(s) or index/indices for which the SCR should be computed
• colvar: the variable in the sample annotations (colData) that hold the information used to discriminate sample channels from carrier channels.
• carrierPattern: a string pattern (following regular expression syntax) that identifies the carrier channel in each batch.
• samplePattern: a string pattern (following regular expression syntax) that identifies the samples to divide.

Optionally, you can also provide the following arguments:

• rowDataName: the name of the column in the rowData where to store the computed SCR for each feature.
• sampleFUN: when multiple samples are present in an assay, there are as many SCR as there are samples that need to be summarized to a single value per feature. sampleFUN tells which function to use for summarizing the sample values before computing the SCR; the default is the mean.
• carrierFUN: some designs might include several carriers per run (not the case in this example). Similarly to sampleFUN, carrierFUN tells which function to use for summarizing the carrier values before computing the SCR; the default is the same function as sampleFUN.

The function creates a new field in the rowData of the assays. We compute the average SCR for each PSM and store it in the corresponding rowData, under the MeanSCR column.

scp <- computeSCR(scp,
i = 1:3,
colvar = "SampleType",
carrierPattern = "Carrier",
samplePattern = "Macrophage|Monocyte",
sampleFUN = "mean",
rowDataName = "MeanSCR")

Before applying the filter, we plot the distribution of the average SCR. We collect the rowData from several assays in a single table DataFrame using the rbindRowData function from QFeatures.

rbindRowData(scp, i = 1:3) %>%
data.frame %>%
ggplot(aes(x = MeanSCR)) +
geom_histogram() +
geom_vline(xintercept = c(1/200, 0.1),
lty = c(2, 1)) +
scale_x_log10()

The expected ratio between single cells and the carrier is 1/200 (dashed line). We can see that the distribution mode is slightly shifted towards higher ratios with a mode around 0.01. However, there are a few PSMs that stand out of the distribution and have a much higher signal than expected, indicating something wrong happened during the quantification of those PSMs. We therefore filter out PSMs with an average SCR higher than 0.1 (solide line). This is again easily performed using the filterFeatures functions.

scp <- filterFeatures(scp,
~ !is.na(MeanSCR) &
MeanSCR < 0.1)
#> 'MeanSCR' found in 3 out of 3 assay(s)

## 5.4 Filter features to control for FDR

Finally, we might also want to control for false discovery rate (FDR). MaxQuant already computes posterior error probabilities (PEP), but filtering on PEPs is too conservative (Käll et al. (2008)) so we provide the pep2qvalue function to convert PEPs to q-values that are directly related to FDR. We here compute the q-values from the PEP (dart_PEP) across all 3 assays. dart_PEP contains the PEP values that have been updated using the DART-ID algorithm (Chen, Franks, and Slavov (2019)). The function will store the results in the rowData, we here asked to name the new column qvalue_PSMs.

scp <- pep2qvalue(scp,
i = 1:3,
PEP = "dart_PEP",
rowDataName = "qvalue_PSMs")

We also allow to compute q-values at peptide or protein level rather than PSM. In this case, you need to supply the groupBy argument. Suppose we want to compute the q-values at protein level, we can fetch the protein information stored under Leading.razor.protein in the rowData. This time, we store the q-values in a new field called qvalue_proteins.

scp <- pep2qvalue(scp,
i = 1:3,
PEP = "dart_PEP",
rowDataName = "qvalue_proteins")

We can now filter the PSM to control, let’s say, the protein FDR at 1%. This can be performed using filterFeatures because the q-values were stored in the rowData.

scp <- filterFeatures(scp,
~ qvalue_proteins < 0.01)
#> 'qvalue_proteins' found in 3 out of 3 assay(s)

# 6 Process the PSM data

## 6.1 Relative reporter ion intensity

In order to partialy correct for between-run variation, SCoPE2 suggests computing relative reporter ion intensities. This means that intensities measured for single-cells are divided by the reference channel containing 5-cell equivalents. We use the divideByReference function that divides channels of interest by the reference channel. Similarly to computeSCR, we can point to the samples and the reference columns in each assay using the annotation contained in the colData.

We here divide all columns (using the regular expression wildcard .) by the reference channel (Reference).

scp <- divideByReference(scp,
i = 1:3,
colvar = "SampleType",
samplePattern = ".",
refPattern = "Reference")

# 7 Aggregate PSM data to peptide data

Now that the PSM assays are processed, we can aggregate them to peptides. This is performed using the aggregateFeaturesOverAssays function. For each assay, the function aggregates several PSMs into a unique peptide. This is best illustrated by the figure below.