Introduction

In this tutorial, we’ll walk you through the process of modelling single-cell proteomics (SCP) data using the scplainer approach (Vanderaa and Gatto (2024)). By the end of this vignette, you will be able to:

Define and estimate a model suitable for SCP data
Filter peptides based on the patterns of missing values
Exploring the model output through analysis of variance
Exploring the model output through differential abundance analysis
Exploring the model output through component analysis
Perform batch correction to remove unwanted technical artefacts

The last point will allow you to generate SCP data that is suitable for downstream analysis, such as clustering or trajectory inference. The figure below provides a roadmap of the workflow:

scplainer modelling workflow

The vignette will start with the processed data extracted as a SingleCellExperiment object from a processed QFeatures object. We will not cover data processing as it is covered in another vignette.

library("scp")
library("SingleCellExperiment")
library("patchwork")
library("ggplot2")

Example data set

The example data set is a subset of the leduc2022_pSCoPE data set (see ?scpdata::leduc2022_pSCoPE for more info). The data is acquired using TMT-18 multiplexing and data-dependent acquisition (DDA). The data has been processed using a minimal workflow:

Any zero value has been replaced by NA.
Any peptide was removed from the data set if it matched to a decoy or contaminant peptide, had low spectral purity, had low identification confidence, or had a high sample to carrier ratio.
Any cell was removed from the data set if it had a high coefficient of variation, had an abberrant median intensity, or had few identified peptides.
PSM data were combined into peptide data. When multiple PSM match to the same peptide, the median intensity was taken.
Intensities were log2 transformed
To limit intensive computation, we have limited the data set to 200 peptides in 73 cells.

We suggest using this minimal processing workflow, although the approach presented here is agnostic of previous processing and allows for other custom workflows. The data processing was conducted with QFeatures and scp.

data("leduc_minimal")
leduc_minimal
#> class: SingleCellExperiment 
#> dim: 200 73 
#> metadata(1): model
#> assays(1): ''
#> rownames(200): SAVEDEGLK APNVVVTR ... FLLAVSRDR EASMVITESPAALQLR
#> rowData names(6): Sequence Reverse ... Leading.razor.protein.symbol
#>   gene
#> colnames(73): eAL00219RI5 eAL00219RI6 ... wAL00286RI17 wAL00286RI18
#> colData names(12): Set Channel ... MedianCV passQC
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):

The data set is formatted as a SingleCellExperiment object. The data set consists of 200 peptides and 73 cells. Peptide annotations can be retrieved from the rowData and cell annotations can be retrieved from the colData. The cell annotation will be used during modelling.

A full reanalysis of Leduc’s nPOP dataset is also available here.

Data modelling

The core of the approach relies on statistical modelling of the data using linear regression. Under the hood, the model fetches as input the intensity matrix stored in assay(leduc_minimal). The cell annotations are retrieved using colData(leduc_minimal). They describe known technical and biological variables that may influence the acquired peptide intensities. The annotations are used to build a regression model with p parameters. Then, the model estimates the coefficients. Coefficients provide the contributions of each parameter to the expression of each of peptide as well as the uncertainty of the estimation. These will be explored in the following section.

We’ll start by defining the variables to include in the model. Recall that the example data set contains TMT-labeled cells. This means that each MS acquisition run contains multiple cells. Each run is subject to technical fluctuations that can lead to undesired variation, this is known as a batch effect.

table(leduc_minimal$Set)
#> 
#> eAL00219 eAL00220 eAL00221 wAL00284 wAL00285 wAL00286 
#>       13       12       12       12       12       12

The labelling reagent (Channel) can also lead to undesired systematic effects and will also be considered as a source for batch effects.

table(leduc_minimal$Channel)
#> 
#>  TMT126 TMT127N TMT127C TMT128N TMT128C TMT129N TMT129C TMT130N TMT130C TMT131N 
#>       0       0       0       0       6       5       6       6       5       4 
#> TMT131C TMT132N TMT132C TMT133N TMT133C TMT134N TMT134C TMT135N 
#>       5       5       5       6       5       5       5       5

Finally, each cell is processed individually and the amount of peptide material recovered from each cell may lead to undesired variation as well. This issue is usually solved through normalization, such as removing the median intensity from each cell. Normalization was internationally omitted in the minimal data processing so that we can account for it during modelling. The median intensity were already computed (MedianIntensity).

hist(leduc_minimal$MedianIntensity, breaks = 10)

Finally, the biological variable of interest in the example data set is the cell type that is known because cells come from 2 cell lines (SampleType).

table(leduc_minimal$SampleType)
#> 
#> Melanoma Monocyte 
#>       37       36

We create a formula object that will define which variable must be modelled in our analysis.

f <- ~ 1 + ## intercept
    Channel + Set + ## batch variables
    MedianIntensity + ## normalization
    SampleType ## biological variable

Note that the formula can be adapted to the data set. For instance, no labelling reagents is used for LFQ experiments, so it can be dropped. Similarly, each cell in an LFQ experiment is acquired in a single run so MS run cannot be used as a batch effect variable. The day of acquisition could be used instead.

Once a model is defined, we fit it with scpModelWorkflow().

leduc_minimal <- scpModelWorkflow(leduc_minimal, formula = f)
#> Warning in scpModelWorkflow(leduc_minimal, formula = f): An element called
#> 'model' is already present in the metadata. The associated content will be
#> overwritten.

You can always retrieve the formula that was used to fit model using

scpModelFormula(leduc_minimal)
#> ~1 + Channel + Set + MedianIntensity + SampleType

The data that is modelled by each variable are contained in the so-called effect matrices.

scpModelEffects(leduc_minimal)
#> List of length 4
#> names(4): Channel Set MedianIntensity SampleType

Similarly, the data that could not be captured by the model are contained in the residual matrix.

scpModelResiduals(leduc_minimal)[1:5, 1:5]
#>                eAL00219RI5 eAL00219RI6 eAL00219RI7 eAL00219RI8 eAL00219RI9
#> SAVEDEGLK               NA          NA          NA          NA          NA
#> APNVVVTR        -0.2112685   0.2569749 -0.08076257 -0.03317054  0.06679681
#> IVVVTAGVR               NA          NA          NA          NA          NA
#> GFQEVVTPNIFNSR   0.6375780  -0.3356473  0.34648412 -0.80982152  0.96378484
#> ENAYDLEANLAVLK          NA          NA          NA          NA          NA

Finally, the input data used to model the can also be retrieved.

scpModelInput(leduc_minimal)[1:5, 1:5]
#>                eAL00219RI5 eAL00219RI6 eAL00219RI7 eAL00219RI8 eAL00219RI9
#> SAVEDEGLK               NA          NA          NA          NA          NA
#> APNVVVTR          13.56997    13.85389   13.362218   13.110304    14.03041
#> IVVVTAGVR               NA          NA          NA          NA          NA
#> GFQEVVTPNIFNSR    10.78733     9.36700    9.815223    7.878296    11.01890
#> ENAYDLEANLAVLK          NA          NA          NA          NA          NA

Note that the number of peptides changed. This is the consequence of peptide filtering.

dim(scpModelInput(leduc_minimal))
#> [1] 140  73

Peptide filtering

The proportion of missing values for each features is high in single-cell proteomics data.

table(missing = is.na(assay(leduc_minimal)))
#> missing
#> FALSE  TRUE 
#>  5954  8646

Many features can typically contain more coefficients to estimate than observed values. These features cannot be estimated and will be ignored during further steps. These features are identified by computing the ratio between the number of observed values and the number of coefficients to estimate. We call it the n/p ratio. You can extract the n/p ratio for each feature:

head(scpModelFilterNPRatio(leduc_minimal))
#>      SAVEDEGLK       APNVVVTR      IVVVTAGVR GFQEVVTPNIFNSR ENAYDLEANLAVLK 
#>       1.277778       3.476190       1.411765       2.333333       1.235294 
#>      IGPLGLSPK 
#>       1.888889

Once the model is estimated, use scpModelFilterPlot() to explore the distribution of n/p ratios across the features.

scpModelFilterPlot(leduc_minimal)
#> To change the threshold, use:
#> scpModelFilterThreshold(object, name) <- threshold

By default, any feature that has an n/p ration greater than 1 is included in the analysis. However, feature with an n/p ratio close to 1 may lead to unreliable outcome because there are not enough observed data. You could consider the n/p ratio as the average number of replicate per coefficient to estimate. Therefore, you may want to increase the n/p threshold.

scpModelFilterThreshold(leduc_minimal) ## default is 1
#> [1] 1
scpModelFilterThreshold(leduc_minimal) <- 1.5
scpModelFilterThreshold(leduc_minimal) ## threshold is now 1.5
#> [1] 1.5

The plot is automatically updated.

scpModelFilterPlot(leduc_minimal)
#> To change the threshold, use:
#> scpModelFilterThreshold(object, name) <- threshold

There is no guidelines for defining a suitable threshold. If too low, you may include noisy peptides that have too few observations. If too high, you may remove many informative peptides. The definition of the threshold relies on a trade off between precision and sensitivity.

Model exploration: analysis of variance

The variance analysis reports the relative amount of information that is captured by each cell annotation included in the model. The model also includes the residual information that is not captured by the model. This offers a first glimpse into what information is contained in the data.

(vaRes <- scpVarianceAnalysis(leduc_minimal))
#> DataFrameList of length 5
#> names(5): Residuals Channel Set MedianIntensity SampleType

The results are a list of tables, one table for each variable. Each table reports for each peptide the variance captures (SS), the residual degrees of freedom for estimating the variance (df) and the percentage of total variance explained (percentExplainedVar).

vaRes$SampleType
#> DataFrame with 95 rows and 4 columns
#>           feature         SS        df percentExplainedVar
#>       <character>  <numeric> <numeric>           <numeric>
#> 1        APNVVVTR 27.8682309        52           33.052188
#> 2   GFQEVVTPNI...  0.1106189        28            0.392004
#> 3       IGPLGLSPK  0.0442532        16            0.242434
#> 4   VAETANEEEV...  0.4973533        11            2.694342
#> 5   IDATSASVLA...  0.9271610        36            2.174504
#> ...           ...        ...       ...                 ...
#> 91  SVPTSTVFYP...    1.88199        48             4.60836
#> 92      HIVENAVQK    1.05939        19             4.80442
#> 93  TDMDNQIVVS...    4.31709         9            24.56762
#> 94       AILGSVER    2.51736        16            15.90057
#> 95     LGAEVYHTLK    2.47027        15            10.14771

By default, we explore the variance for all peptides combined:

scpVariancePlot(vaRes)

We explore the top 20 peptides that are have the highest percentage of variance explained by the biological variable (top) or by the batch variable (bottom).

scpVariancePlot(
    vaRes, top = 10, by = "percentExplainedVar", effect = "SampleType",
    decreasing = TRUE, combined = FALSE
) +
    scpVariancePlot(
    vaRes, top = 10, by = "percentExplainedVar", effect = "Set",
    decreasing = TRUE, combined = FALSE
) +
    plot_layout(ncol = 1, guides = "collect")

We can also group the peptide by protein. To do so, we first need to add the peptides annotations available from the rowData.

vaRes <- scpAnnotateResults(
    vaRes, rowData(leduc_minimal), by = "feature", by2 = "Sequence"
)
vaRes$SampleType
#> DataFrame with 95 rows and 9 columns
#>           feature         SS        df percentExplainedVar     Reverse
#>       <character>  <numeric> <numeric>           <numeric> <character>
#> 1        APNVVVTR 27.8682309        52           33.052188            
#> 2   GFQEVVTPNI...  0.1106189        28            0.392004            
#> 3       IGPLGLSPK  0.0442532        16            0.242434            
#> 4   VAETANEEEV...  0.4973533        11            2.694342            
#> 5   IDATSASVLA...  0.9271610        36            2.174504            
#> ...           ...        ...       ...                 ...         ...
#> 91  SVPTSTVFYP...    1.88199        48             4.60836            
#> 92      HIVENAVQK    1.05939        19             4.80442            
#> 93  TDMDNQIVVS...    4.31709         9            24.56762            
#> 94       AILGSVER    2.51736        16            15.90057            
#> 95     LGAEVYHTLK    2.47027        15            10.14771            
#>     Potential.contaminant Leading.razor.protein.id Leading.razor.protein.symbol
#>               <character>              <character>                  <character>
#> 1                                           P52566                        GDIR2
#> 2                                           P26639                         SYTC
#> 3                                           P30050                         RL12
#> 4                                           P61221                        ABCE1
#> 5                                           P13667                        PDIA4
#> ...                   ...                      ...                          ...
#> 91                                          P29401                          TKT
#> 92                                          O00410                         IPO5
#> 93                                          P50991                         TCPD
#> 94                                          P26639                         SYTC
#> 95                                          P09104                         ENOG
#>            gene
#>     <character>
#> 1       ARHGDIB
#> 2          TARS
#> 3         RPL12
#> 4         ABCE1
#> 5         PDIA4
#> ...         ...
#> 91          TKT
#> 92         IPO5
#> 93         CCT4
#> 94         TARS
#> 95         ENO2

Then, we draw the same plot, but this time we provide the fcol argument.

scpVariancePlot(
    vaRes, top = 10, by = "percentExplainedVar", effect = "SampleType",
    decreasing = TRUE, combined = FALSE, fcol = "gene"
) +
    scpVariancePlot(
    vaRes, top = 10, by = "percentExplainedVar", effect = "Set",
    decreasing = TRUE, combined = FALSE, fcol = "gene"
) +
    plot_layout(ncol = 1, guides = "collect")

In this example dataset, we retrieve peptides that all belong to a different protein, however grouping becomes interesting when analyzing real data sets.

Alternatively, we can generate protein level results by aggregating peptide level results.

vaProtein <- scpVarianceAggregate(vaRes, fcol = "gene")
scpVariancePlot(
    vaProtein, effect = "SampleType", top = 10, combined = FALSE
)

Model exploration: differential abundance analysis

Differential abundance analysis dives deeper into the exploration of the data, namely for exploring the biological effects. Given two groups of interest, such as two cell types or two treatment groups, the differential analysis derives estimated fold changes from the linear model’s coefficients. This provides information, for each peptide or protein, the amount of change between the two groups and the direction of the change. Moreover, the model provides the uncertainty of the differences, enabling the assessment of the statistical significance.

The difference of interest is specified using the contrast argument. The first element points to the variable to test and the two following element are the groups of interest to compare. You can provide multiple contrast in a list.

(daRes <- scpDifferentialAnalysis(
    leduc_minimal,
    contrasts = list(c("SampleType", "Melanoma", "Monocyte"))
))
#> DataFrameList of length 1
#> names(1): SampleType_Melanoma_vs_Monocyte

Similarly to variance analysis, the results are a list of tables, one table for each contrast. Each table reports for each peptide the estimated difference between the two groups, the standard error associated to the estimation, the degrees of freedom, the t-statistics, the associated p-value and the p-value FDR-adjusted for multiple testing across all peptides.

daRes$SampleType_Melanoma_vs_Monocyte
#> DataFrame with 95 rows and 7 columns
#>           feature   Estimate        SE        Df tstatistic      pvalue
#>       <character>  <numeric> <numeric> <numeric>  <numeric>   <numeric>
#> 1        APNVVVTR  1.2357291  0.140225        52   8.812466 6.77782e-12
#> 2   GFQEVVTPNI... -0.0950269  0.312985        28  -0.303615 7.63666e-01
#> 3       IGPLGLSPK -0.0721544  0.375395        16  -0.192209 8.49997e-01
#> 4   VAETANEEEV... -0.2619169  0.486300        11  -0.538591 6.00898e-01
#> 5   IDATSASVLA... -0.2573437  0.249598        36  -1.031031 3.09404e-01
#> ...           ...        ...       ...       ...        ...         ...
#> 91  SVPTSTVFYP...  -0.330304  0.202807        48  -1.628658    0.109932
#> 92      HIVENAVQK   0.338421  0.495880        19   0.682466    0.503181
#> 93  TDMDNQIVVS...   0.814964  1.103851         9   0.738292    0.479146
#> 94       AILGSVER   0.544206  0.368422        16   1.477127    0.159054
#> 95     LGAEVYHTLK   0.547198  0.521144        15   1.049995    0.310344
#>            padj
#>       <numeric>
#> 1   6.43893e-10
#> 2   8.91820e-01
#> 3   9.06232e-01
#> 4   8.64161e-01
#> 5   6.81383e-01
#> ...         ...
#> 91     0.521039
#> 92     0.810207
#> 93     0.789070
#> 94     0.521039
#> 95     0.681383

We then visualize the results using a volcano plot. The function below return a volcano plot for each contrast.

scpVolcanoPlot(daRes)
#> $SampleType_Melanoma_vs_Monocyte

Since we subset the data set for only a few cell, we lack statistical power. Still, two peptides come out as significant. Again, to better explore the results, we add peptide annotations available from the rowData, but we also add the n/p ratio as annotation.

daRes <- scpAnnotateResults(
    daRes, rowData(leduc_minimal),
    by = "feature", by2 = "Sequence"
)
np <- scpModelFilterNPRatio(leduc_minimal)
daRes <- scpAnnotateResults(
    daRes, data.frame(feature = names(np), npRatio = np),
    by = "feature"
)

We plot the same volcano plot, but instead of labeling points with the peptide sequence, we will show the associated gene symbol. Also, we can control for point aesthetics by providing a list of ggplot2::geom_point() arguments. For example, we can colour each point based on the n/p ratio, and adjust point size and shape.

scpVolcanoPlot(
    daRes, top = 30, textBy = "gene",
    pointParams = list(aes(colour = npRatio), size = 1.5, shape = 3)
)
#> $SampleType_Melanoma_vs_Monocyte
#> Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

We can also provide protein-level results. To do so, the scpDifferentialAggregate() relies on the metapod package. We here combine the statistical test results for peptides that belong to the same protein using Simes’ method. Simes’ method will reject the combined null hypothesis (that is the mean protein intensities are identical between two groups) if any of the peptide nulls are rejected.

byProteinDA <- scpDifferentialAggregate(
    daRes, fcol = "gene", method = "simes"
)
byProteinDA$SampleType_Melanoma_vs_Monocyte
#> DataFrame with 86 rows and 10 columns
#>         feature   Estimate    pvalue      padj     Reverse
#>     <character>  <numeric> <numeric> <numeric> <character>
#> 1         ABCE1 -0.2619169 0.6008984  0.856797            
#> 2          ACLY -1.0078958 0.0564024  0.404217            
#> 3         ACTN4 -0.0576889 0.8105808  0.910420            
#> 4         AIFM1  0.4300612 0.1488197  0.530259            
#> 5         APMAP  0.0683780 0.7445601  0.901862            
#> ...         ...        ...       ...       ...         ...
#> 82         TPP2 -0.2494580 0.4703385  0.812359            
#> 83       TUBA1C -0.6951016 0.0492061  0.384702            
#> 84         VAT1 -0.4073205 0.0691403  0.457390            
#> 85        XRCC6  0.0289353 0.8832055  0.915129            
#> 86        YWHAG -0.0979855 0.6550366  0.873363            
#>     Potential.contaminant Leading.razor.protein.id Leading.razor.protein.symbol
#>               <character>              <character>                  <character>
#> 1                                           P61221                        ABCE1
#> 2                                           P53396                         ACLY
#> 3                                           O43707                        ACTN4
#> 4                                           O95831                        AIFM1
#> 5                                           Q9HDC9                        APMAP
#> ...                   ...                      ...                          ...
#> 82                                          P29144                         TPP2
#> 83                                          Q9BQE3                        TBA1C
#> 84                                          Q99536                         VAT1
#> 85                                          P12956                        XRCC6
#> 86                                          P61981                        1433G
#>            gene        .n
#>     <character> <integer>
#> 1         ABCE1         1
#> 2          ACLY         1
#> 3         ACTN4         1
#> 4         AIFM1         1
#> 5         APMAP         1
#> ...         ...       ...
#> 82         TPP2         1
#> 83       TUBA1C         1
#> 84         VAT1         2
#> 85        XRCC6         1
#> 86        YWHAG         1

Model exploration: component analysis

Variance and differential analysis are not specific to single-cell applications and explore the data without considering cellular heterogeneity. The purpose of the component analysis is to dive into the cellular heterogeneity by representing highly dimensional data in a few informative dimensions for visual exploration. We integrate the component analysis with the linear regression model thanks to the APCA+ (extended ANOVA-simultaneous component analysis) framework developed by Thiel et al. 2017. Briefly, APCA+ explores the reconstructed data that is captured by each variable separately in the presence of the unmodelled data. The advantage of this framework is it is generic and works for any linear model. Also, this approach is well suited for single-cell applications as it enables the visualization and exploration of the effects of a known variable along the unmodelled information that contains cellular heterogeneity.

(caRes <- scpComponentAnalysis(
    leduc_minimal, ncomp = 20, method = "APCA", effect = "SampleType"
))
#> List of length 2
#> names(2): bySample byFeature

The results are contained in a list with 2 elements. bySample contains the PC scores, that is the component results in cell space. byFeature contains the eigenvectors, that is the component results in peptide space. Each of the two elements contains components results for the data before modelling (unmodelled), for the residuals or for the APCA on the sample type variable (APCA_SampleType).

(caResCells <- caRes$bySample)
#> List of length 3
#> names(3): unmodelled residuals APCA_SampleType
caResCells[[1]]
#> DataFrame with 73 rows and 21 columns
#>                    PC1       PC2       PC3       PC4        PC5       PC6
#>              <numeric> <numeric> <numeric> <numeric>  <numeric> <numeric>
#> eAL00219RI5   1.652635  3.711020  -4.21156 -1.079196  0.0666983  1.408647
#> eAL00219RI6   0.122763  5.156328  -5.49091  0.184137 -0.3458518  1.048142
#> eAL00219RI7  -1.356183  6.042980  -5.04466  0.372945 -2.1629620  0.234317
#> eAL00219RI8  -2.700162  4.483173  -5.34451  0.548601 -1.5492215 -0.345117
#> eAL00219RI9   2.546416  0.572361  -5.35620  0.723007 -0.1841323  4.479647
#> ...                ...       ...       ...       ...        ...       ...
#> wAL00286RI12  -7.98580 -1.856855  0.757797  2.716883   0.389962  1.670132
#> wAL00286RI14  -8.20192 -0.746896  1.535207 -1.595404   2.795661  0.498499
#> wAL00286RI16  -1.81072 -2.407762  3.963677 -1.732718   0.143329 -0.745458
#> wAL00286RI17  -2.86441 -3.393967 -1.515343 -1.529139  -2.434284  0.121065
#> wAL00286RI18  -2.25612 -4.270107  0.987672 -0.145896  -1.382700 -1.306674
#>                    PC7       PC8       PC9       PC10       PC11        PC12
#>              <numeric> <numeric> <numeric>  <numeric>  <numeric>   <numeric>
#> eAL00219RI5   -1.40664 -0.569028  1.816091  -0.335920  -3.425797   -0.261528
#> eAL00219RI6   -2.03264  0.242179  2.257062   0.808499  -1.271131    0.486000
#> eAL00219RI7   -1.26036 -1.921333 -0.241230   0.825305  -1.820185    2.704122
#> eAL00219RI8   -1.31727 -0.876115 -0.826323   2.179410   2.105049    0.760836
#> eAL00219RI9    1.28303  0.785622 -0.545883  -0.625327  -0.676794   -0.943669
#> ...                ...       ...       ...        ...        ...         ...
#> wAL00286RI12  1.117698 -0.812645 -0.223081 -0.4827100  1.1006363 -0.99808069
#> wAL00286RI14  0.979609 -0.959699  3.834839 -0.9982881  0.3246867 -0.00491314
#> wAL00286RI16  3.800138 -1.646333  1.374409 -1.5396094 -0.8875749  2.42672153
#> wAL00286RI17  1.894991 -0.681766 -0.612739  0.0177883 -0.4717603  1.93884428
#> wAL00286RI18  1.586430 -1.000224 -0.140993  0.8425007 -0.0447968  1.01835905
#>                    PC13       PC14      PC15      PC16       PC17      PC18
#>               <numeric>  <numeric> <numeric> <numeric>  <numeric> <numeric>
#> eAL00219RI5   1.3248564 -0.6213675  0.275210 -0.376301  0.2819763 -0.112223
#> eAL00219RI6  -0.0312991  0.1282374 -0.977545 -0.638003 -0.0905137  0.443276
#> eAL00219RI7   0.2210126 -0.8594984 -1.371305  0.293012 -1.2920848 -0.211974
#> eAL00219RI8  -0.1068177 -0.0299786 -0.293472 -1.618090  1.0066113  0.699695
#> eAL00219RI9  -0.1327679  0.8920529  0.332752 -1.424903  0.2110955  0.484973
#> ...                 ...        ...       ...       ...        ...       ...
#> wAL00286RI12   1.258691  -1.038123  0.715850  0.594017 -0.0320364  0.386167
#> wAL00286RI14  -1.463484  -1.115054  1.304641  0.471641 -1.2468862  1.135240
#> wAL00286RI16  -0.128652   0.438629  0.405675 -1.249913  0.8714631 -0.245134
#> wAL00286RI17  -2.385663  -0.696050 -0.636856 -0.639397 -0.5319913  0.677671
#> wAL00286RI18   0.286674   0.861364 -0.262798  1.071512 -0.0554080 -0.132846
#>                    PC19       PC20          cell
#>               <numeric>  <numeric>   <character>
#> eAL00219RI5  -0.2408148 -0.4442979 eAL00219RI...
#> eAL00219RI6  -0.0921259  0.0940622 eAL00219RI...
#> eAL00219RI7  -0.3802431  0.7556116 eAL00219RI...
#> eAL00219RI8   0.2000259 -0.5325636 eAL00219RI...
#> eAL00219RI9  -1.0358553  1.0556220 eAL00219RI...
#> ...                 ...        ...           ...
#> wAL00286RI12  -0.244162  1.4868339 wAL00286RI...
#> wAL00286RI14   0.602194  0.0939671 wAL00286RI...
#> wAL00286RI16  -1.108754  0.3540490 wAL00286RI...
#> wAL00286RI17   0.955012  0.7391520 wAL00286RI...
#> wAL00286RI18   2.147393  0.3731584 wAL00286RI...

Let’s explore the component analysis in cell space. Similarly to the previous explorations, we annotate the results.

leduc_minimal$cell <- colnames(leduc_minimal)
caResCells <- scpAnnotateResults(
    caResCells, colData(leduc_minimal), by = "cell"
)

We then generate the component plot. Providing the pointParams argument, we can shape the points by SampleType. To assess the impact of batch effects, we also colour the points according to the MS acquisition run.

scpComponentPlot(
    caResCells,
    pointParams = list(aes(shape = SampleType, colour = Set))
) |>
    wrap_plots(ncol = 1, guides = "collect")

While the data before modelling is mainly driven by batch effects, the APCA clearly separates the two cell populations. The plot can however only show 2 components at a time. We can explore more components using a subsequent dimension reduction, such as t-SNE. The scater package offers a comprehensive set of tools for dimension reduction on data contained in a SingleCellExperiment object and requires the components to be stored in the reducedDim slot. This is streamlined thanks to addReducedDims().

leduc_minimal <- addReducedDims(leduc_minimal, caResCells)
reducedDims(leduc_minimal)
#> List of length 3
#> names(3): unmodelled residuals APCA_SampleType

We can now explore the SampleType effects for the 20 computed components through t-SNE.

library("scater")
leduc_minimal <- runTSNE(leduc_minimal, dimred = "APCA_SampleType")
plotTSNE(leduc_minimal, colour_by = "Set", shape_by = "SampleType") +
    ggtitle("t-SNE on 20 APCA components")

The two cell populations remain clearly separated with an excellent mixing of the acquisition runs, even when considering the 20 first APCA components.

We use the same approach to explore the component results in peptide space.

caResPeps <- caRes$byFeature
caResPeps <- scpAnnotateResults(
    caResPeps, rowData(leduc_minimal), by = "feature", by2 = "Sequence"
)
scpComponentPlot(
    caResPeps, pointParams = list(size = 0.8, alpha = 0.4)
) |>
    wrap_plots(ncol = 1)

This exploration may identify groups of covarying peptides, although no clear patterns appear in the example data set.

We can also combine the exploration of the components in cell and peptide space. This is possible thanks to biplots.

scpComponentBiplot(
    caResCells, caResPeps,
    pointParams = list(aes(colour = SampleType)),
    labelParams = list(size = 1.5, max.overlaps = 15),
    textBy = "gene", top = 10
) |>
    wrap_plots(ncol = 1, guides = "collect")

Finally, we offer functionality to aggregate the results at the protein level instead of the peptide level.

caResProts <- scpComponentAggregate(caResPeps, fcol = "gene")
#> Components may no longer be orthogonal after aggregation.
caResProts$APCA_SampleType
#> DataFrame with 86 rows and 26 columns
#>                PC1        PC2        PC3        PC4         PC5          PC6
#>          <numeric>  <numeric>  <numeric>  <numeric>   <numeric>    <numeric>
#> ABCE1   -0.0523935 -0.0136777  0.0903348  0.1693285   0.0665345    0.0493461
#> ACLY    -0.2188357  0.0295186 -0.2475794 -0.0555763  -0.1122432    0.0431707
#> ACTN4   -0.0119062  0.0384471  0.2774156 -0.0797822  -0.0941029   -0.1243163
#> AIFM1    0.1006538 -0.0362258  0.1976476  0.0301861  -0.1747488    0.0622619
#> APMAP    0.0195111  0.0211236  0.0436172  0.0675338   0.0634203    0.1031632
#> ...            ...        ...        ...        ...         ...          ...
#> TPP2   -0.05528458 -0.0310194  0.0431430 -0.1027764 -0.08156241 -0.015980101
#> TUBA1C -0.14532912 -0.0163971  0.0198204  0.0211471 -0.02163806 -0.000503111
#> VAT1   -0.08475507 -0.0831661  0.1167588  0.0591425 -0.00581566 -0.013318437
#> XRCC6   0.00292548  0.0614277  0.1692137  0.0295988 -0.02905814 -0.000397610
#> YWHAG  -0.00908836  0.1134883 -0.2952287  0.0902165 -0.12814301  0.008616979
#>               PC7        PC8        PC9        PC10       PC11       PC12
#>         <numeric>  <numeric>  <numeric>   <numeric>  <numeric>  <numeric>
#> ABCE1   0.0288414  0.0154912 -0.0128954  -0.0168423  0.0485774 -0.0481859
#> ACLY    0.0716542 -0.1018500 -0.0276287  -0.0739296  0.0719484  0.1163738
#> ACTN4  -0.0886529  0.0746792 -0.0886262  -0.2615099 -0.0949204  0.2439664
#> AIFM1   0.0447698 -0.1740009  0.0274838   0.0931568 -0.0445237  0.1537508
#> APMAP  -0.0839194  0.0436465 -0.0291126  -0.1366302  0.0780494 -0.0467466
#> ...           ...        ...        ...         ...        ...        ...
#> TPP2   0.01002283  0.0613254 -0.0657707 -0.02449126 -0.0726705  0.0386026
#> TUBA1C 0.02632304 -0.0177832  0.0100838 -0.00880753 -0.0311755  0.0127917
#> VAT1   0.00580065 -0.0525127 -0.0188442 -0.13095970  0.1962062  0.0253535
#> XRCC6  0.10658824 -0.0676993 -0.0815302  0.23972712  0.1148027  0.0353619
#> YWHAG  0.01593590 -0.0617372 -0.0961599  0.18878619  0.0708525  0.1197667
#>                PC13       PC14        PC15       PC16        PC17       PC18
#>           <numeric>  <numeric>   <numeric>  <numeric>   <numeric>  <numeric>
#> ABCE1  -0.001663287  0.0343296   0.0343627  0.0990232  0.01865067 -0.0327103
#> ACLY    0.192738461  0.1474133  -0.1739438  0.1289705  0.05033950  0.0287668
#> ACTN4  -0.133406589  0.0132553  -0.0693790 -0.0808312 -0.08205540  0.2087751
#> AIFM1  -0.077020737 -0.0493539  -0.1162488  0.0365235  0.00510608 -0.1441099
#> APMAP  -0.000121253 -0.1390497  -0.0867093 -0.0410053 -0.11611495  0.1792133
#> ...             ...        ...         ...        ...         ...        ...
#> TPP2     0.07597089  0.0558818 -0.00337644 -0.0121018  0.04283655  0.0476116
#> TUBA1C  -0.02998746 -0.0217296 -0.00789350  0.0565058 -0.00373714 -0.0119916
#> VAT1    -0.05204097 -0.0852005 -0.02017030  0.0952139 -0.16910390 -0.0715073
#> XRCC6   -0.00257737  0.0259900  0.10724778 -0.0564281  0.08246124  0.0499968
#> YWHAG    0.03318137  0.0621676 -0.05404994  0.0792021 -0.12921509 -0.0384467
#>              PC19       PC20     Reverse Potential.contaminant
#>         <numeric>  <numeric> <character>           <character>
#> ABCE1  -0.0365880 -0.0681561                                  
#> ACLY    0.1118103 -0.0208683                                  
#> ACTN4   0.0596604  0.0966149                                  
#> AIFM1  -0.2174666  0.1085721                                  
#> APMAP  -0.0331397  0.1518794                                  
#> ...           ...        ...         ...                   ...
#> TPP2   -0.0150711  0.0560492                                  
#> TUBA1C -0.0269349  0.0296168                                  
#> VAT1    0.0480006  0.1489793                                  
#> XRCC6   0.0529253 -0.0198865                                  
#> YWHAG  -0.1012201  0.1127549                                  
#>        Leading.razor.protein.id Leading.razor.protein.symbol        gene
#>                     <character>                  <character> <character>
#> ABCE1                    P61221                        ABCE1       ABCE1
#> ACLY                     P53396                         ACLY        ACLY
#> ACTN4                    O43707                        ACTN4       ACTN4
#> AIFM1                    O95831                        AIFM1       AIFM1
#> APMAP                    Q9HDC9                        APMAP       APMAP
#> ...                         ...                          ...         ...
#> TPP2                     P29144                         TPP2        TPP2
#> TUBA1C                   Q9BQE3                        TBA1C      TUBA1C
#> VAT1                     Q99536                         VAT1        VAT1
#> XRCC6                    P12956                        XRCC6       XRCC6
#> YWHAG                    P61981                        1433G       YWHAG
#>               .n
#>        <integer>
#> ABCE1          1
#> ACLY           1
#> ACTN4          1
#> AIFM1          1
#> APMAP          1
#> ...          ...
#> TPP2           1
#> TUBA1C         1
#> VAT1           2
#> XRCC6          1
#> YWHAG          1

Note that the aggregated tables in caResProts can be explored with the visualisation function scpComponentPlot().

Batch correction

Based on the estimated model, we generate batch-corrected data, that is data with only the effect of cell type and the residual data. We also remove the intercept.

(leduc_batchCorrect <- scpRemoveBatchEffect(
    leduc_minimal, effects = c("Set", "Channel", "MedianIntensity"),
    intercept = TRUE
))
#> class: SingleCellExperiment 
#> dim: 95 73 
#> metadata(0):
#> assays(1): ''
#> rownames(95): APNVVVTR GFQEVVTPNIFNSR ... AILGSVER LGAEVYHTLK
#> rowData names(6): Sequence Reverse ... Leading.razor.protein.symbol
#>   gene
#> colnames(73): eAL00219RI5 eAL00219RI6 ... wAL00286RI17 wAL00286RI18
#> colData names(13): Set Channel ... passQC cell
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):

Note that the batch-corrected data still contain missing values. The leduc_batchCorrect object can be used for downstream analysis.

Session information

R version 4.4.2 (2024-10-31)
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] patchwork_1.3.0             scater_1.35.0              
 [3] scuttle_1.17.0              SingleCellExperiment_1.29.1
 [5] sva_3.55.0                  BiocParallel_1.41.0        
 [7] genefilter_1.89.0           mgcv_1.9-1                 
 [9] nlme_3.1-166                dplyr_1.1.4                
[11] scpdata_1.13.0              ExperimentHub_2.15.0       
[13] AnnotationHub_3.15.0        BiocFileCache_2.15.0       
[15] dbplyr_2.5.0                ggplot2_3.5.1              
[17] scp_1.17.0                  QFeatures_1.17.0           
[19] MultiAssayExperiment_1.33.1 SummarizedExperiment_1.37.0
[21] Biobase_2.67.0              GenomicRanges_1.59.1       
[23] GenomeInfoDb_1.43.2         IRanges_2.41.1             
[25] S4Vectors_0.45.2            BiocGenerics_0.53.3        
[27] generics_0.1.3              MatrixGenerics_1.19.0      
[29] matrixStats_1.4.1           BiocStyle_2.35.0           

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3      sys_3.4.3               jsonlite_1.8.9         
  [4] magrittr_2.0.3          ggbeeswarm_0.7.2        farver_2.1.2           
  [7] rmarkdown_2.29          zlibbioc_1.52.0         vctrs_0.6.5            
 [10] memoise_2.0.1           htmltools_0.5.8.1       S4Arrays_1.7.1         
 [13] BiocBaseUtils_1.9.0     curl_6.0.1              BiocNeighbors_2.1.1    
 [16] SparseArray_1.7.2       sass_0.4.9              bslib_0.8.0            
 [19] plyr_1.8.9              impute_1.81.0           cachem_1.1.0           
 [22] buildtools_1.0.0        igraph_2.1.1            mime_0.12              
 [25] lifecycle_1.0.4         pkgconfig_2.0.3         rsvd_1.0.5             
 [28] Matrix_1.7-1            R6_2.5.1                fastmap_1.2.0          
 [31] GenomeInfoDbData_1.2.13 clue_0.3-66             digest_0.6.37          
 [34] fdrtool_1.2.18          colorspace_2.1-1        AnnotationDbi_1.69.0   
 [37] irlba_2.3.5.1           lpsymphony_1.35.0       RSQLite_2.3.8          
 [40] beachmat_2.23.2         filelock_1.0.3          labeling_0.4.3         
 [43] fansi_1.0.6             httr_1.4.7              abind_1.4-8            
 [46] compiler_4.4.2          bit64_4.5.2             withr_3.0.2            
 [49] viridis_0.6.5           DBI_1.2.3               MASS_7.3-61            
 [52] rappdirs_0.3.3          DelayedArray_0.33.2     tools_4.4.2            
 [55] vipor_0.4.7             beeswarm_0.4.0          glue_1.8.0             
 [58] grid_4.4.2              Rtsne_0.17              cluster_2.1.6          
 [61] reshape2_1.4.4          gtable_0.3.6            tidyr_1.3.1            
 [64] ScaledMatrix_1.15.0     BiocSingular_1.23.0     metapod_1.15.0         
 [67] utf8_1.2.4              XVector_0.47.0          ggrepel_0.9.6          
 [70] BiocVersion_3.21.1      pillar_1.9.0            stringr_1.5.1          
 [73] limma_3.63.2            splines_4.4.2           lattice_0.22-6         
 [76] FNN_1.1.4.1             survival_3.7-0          bit_4.5.0              
 [79] annotate_1.85.0         tidyselect_1.2.1        locfit_1.5-9.10        
 [82] maketools_1.3.1         Biostrings_2.75.1       knitr_1.49             
 [85] gridExtra_2.3           ProtGenerics_1.39.0     edgeR_4.5.0            
 [88] IHW_1.35.0              xfun_0.49               statmod_1.5.0          
 [91] stringi_1.8.4           UCSC.utils_1.3.0        lazyeval_0.2.2         
 [94] yaml_2.3.10             evaluate_1.0.1          codetools_0.2-20       
 [97] nipals_0.8              MsCoreUtils_1.19.0      tibble_3.2.1           
[100] BiocManager_1.30.25     cli_3.6.3               uwot_0.2.2             
[103] xtable_1.8-4            munsell_0.5.1           jquerylib_0.1.4        
[106] Rcpp_1.0.13-1           png_0.1-8               XML_3.99-0.17          
[109] parallel_4.4.2          blob_1.2.4              AnnotationFilter_1.31.0
[112] viridisLite_0.4.2       slam_0.1-55             scales_1.3.0           
[115] purrr_1.0.2             crayon_1.5.3            rlang_1.1.4            
[118] KEGGREST_1.47.0

License

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

Reference

Vanderaa, Christophe, and Laurent Gatto. 2024. “scplainer: Using Linear Models to Understand Mass Spectrometry-Based Single-Cell Proteomics Data.” bioRxiv. https://doi.org/10.1101/2023.12.14.571792.

Single Cell Proteomics data modelling