In UCLouvain-CBIO/2021-SCoPE2-replication: SCP Replication Vignettes

## Options for Rmarkdown compilation
knitr::opts_chunk$set(
    fig.width = 7,
    fig.height = 5,
    fig.align = "center",
    collapse = TRUE,
    crop = NULL ## Related to https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html
)
## Time the compilation
timeStart <- Sys.time()

Introduction

In this vignette, we will analyse the woo2022_macophage dataset using the scplainer approach. The data were acquired using the TIFF acquisition protocol (Woo et al. 2022). The authors performed a label-free experiment in DDA mode, and showed that TIFF could improve the sensitivity and accuracy of label-free single-cell proteomics (SCP).

Packages and data

We rely on several packages to compile this vignette.

## Core packages
library("scp")
library("scpdata")
## Utility packages
library("ggplot2")
library("patchwork")
library("dplyr")
library("scater")

The data set is available from the scpdata package.

woo <- woo2022_macrophage()

The data set contains 155 RAW264.7 cells: 54 are unstimulated cells, 52 are LPS-stimulated cells at 24 h, and 49 LPS-stimulated cells at 48 h.

table(woo$Treatment)

The data were acquired as part of 4 sample preparation chips that we will consider as a potential source for batch effects.

table(woo$Chip)

Minimal data processing

The minimal data processing workflow in scplainer consists of 5 main steps:

• Data cleaning
• Feature quality control
• Sample quality control
• Log2-transformation

Cleaning data

The data available in scpdata were provided by the authors and were analysed with MaxQuant. We here start with the non-normalised peptide data. We recommend starting with data with the least prior processing. We therefore remove the maxLFQ normalised data and the protein data generated by MaxQuant.

names(woo)
assaysToRemove <- c(
    "peptides_LFQ", "proteins_intensity", "proteins_iBAQ", "proteins_LFQ"
)
woo <- removeAssay(woo, assaysToRemove)

Below is an overview of the Qfeatures object used as input data for this data analysis.

woo

We remove feature annotations that won't be used in the remainder of the vignette. This is to avoid overcrowding of the annotation tables later in the vignette.

requiredRowData <- c(
    "Sequence", "Proteins", "Leading.razor.protein", "Gene.names",
    "Protein.names", "Potential.contaminant", "Reverse", "PEP"
)
woo <- selectRowData(woo, requiredRowData)

We replace zeros with missing values. A zero may be a true zero (the feature is not present in the sample) or because of technical limitations (due to the technology or the computational pre-processing). Because we are not able to distinguish between the two, zeros should be replaced with NA.

woo <- zeroIsNA(woo, i = names(woo))

Feature quality control

We remove low-quality PSMs that may propagate technical artefacts and bias data modelling. The quality control criteria are:

• We remove contaminants and decoy peptides.
• We remove low-confidence peptides with less than 1% FDR.

First, we need to convert PEP into q-value to control for FDR.

woo <- pep2qvalue(
    woo, i = names(woo), PEP = "PEP", rowDataName = "qvalue"
)

All the criteria to perform feature filtering are stored in the rowData. We remove any peptide matched to a contaminant protein or to a decoy peptide and control for 1% FDR. Here is an overview of the distributions of each criteria.

df <- data.frame(rbindRowData(woo, names(woo)))
df$ContaminantOrReverse <- df$Reverse == "+" |
    df$Potential.contaminant == "+" |
    grepl("REV|CON", df$Leading.razor.protein)
ggplot(df) + 
    aes(x = ContaminantOrReverse) +
    geom_bar() +
    ## q-value plot
    ggplot(df) + 
    aes(x = log10(qvalue)) +
    geom_histogram(bins = 50) +
    geom_vline(xintercept = -2) +
    xlim(-20, 0)

Peptide identification are already controlled for 1% FDR. We here remove contaminant and decoy peptides.

woo <- filterFeatures(
    woo, ~ Reverse != "+" & 
        Potential.contaminant != "+")

Cell quality control

Similarly to the features, we also remove low-quality cells. The quality control criteria are:

• We remove samples with low number of detected peptides. The criterion is computed as follows:

woo <- countUniqueFeatures(
    woo, i = names(woo), colDataName = "NumberPeptides"
)

• We remove samples with low median intensity. The metric (note we will later use it for normalisation) is computed as follows:

woo$MedianIntensity <- colMedians(
    log10(assay(woo[["peptides_intensity"]])), na.rm = TRUE
)

• We remove the samples that have a high median coefficient of variation (CV). The CV is computed within each sample, by grouping the peptides that belong to the same protein or protein group. This is computed as follows:

woo <- medianCVperCell(
    woo, i = "peptides_intensity", groupBy = "Leading.razor.protein",
    nobs = 5, norm = "SCoPE2", colDataName = "MedianCV"
)

We plot the metrics used to perform sample quality control.

ggplot(data.frame(colData(woo))) +
    aes(
        y = MedianIntensity,
        x = NumberPeptides,
        color = MedianCV,
        shape = Treatment
    ) +
    geom_point(size = 2) +
    scale_color_continuous(type = "viridis")

There are a few suspicious cells with low number of detected peptides and high median CV. We will remove cells with less than 400 peptides and with a median CV higher than 0.5. We will not use the median intensity as a QC metric. We apply the filter and keep only single cells that pass the quality control.

passQC <- !is.na(woo$MedianCV) & woo$MedianCV < 0.5 &
    woo$NumberPeptides > 400
woo <- subsetByColData(woo, passQC)

Log-transformation

We log2-transform the quantification data.

woo <- logTransform(woo, i = "peptides_intensity", name = "peptides_log")

Data modelling

Model the data using scplainer, the linear regression model implemented in scp. scplainer is applied on a SingleCellExperiment so we extract it from the processed data set along with the colData.

sce <- getWithColData(woo, "peptides_log")

First, we must specify which variables to include in the model. We here include 3 variables:

• MedianIntensity: this is the normalisation factor used to correct for cell-specific technical differences.
• Chip: the sample preparation chip is a potential source of batch effect.
• Treatment: this is the biological variable of interest.

scpModelWorkflow() fits linear regression models to the data, where the model is adapted for each peptide depending on its pattern of missing values.

sce <- scpModelWorkflow(
    sce, 
    formula = ~ 1 + ## intercept
        ## normalisation
        MedianIntensity +
        ## batch effects
        Chip + 
        ## biological variability
        Treatment
)

Once the model is prepared, we can explore the distribution of the n/p ratios.

scpModelFilterThreshold(sce) <- 2
scpModelFilterPlot(sce)

Many peptides do not have sufficient observations to estimate the model. We have chosen to continue the analysis with peptides that have $n/p >= 2$. You could consider $n/p$ a rough average of the number of replicates per parameter to fit (for categorical variables, the number of replicates per group). We recommend moving the threshold away from 1 to increase statistical power and remove noisy peptides. This comes of course at the cost of less peptides included in the analysis.

Model analysis

The model analysis consists of three steps:

• Analysis of variance
• Differential abundance analysis
• Component analysis

Analysis of variance

The analysis of variance explores the proportion of data captures by each variable in the model.

(vaRes <- scpVarianceAnalysis(sce))
vaRes[[1]]

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). To better explore the results, we add the annotations available in the rowData.

vaRes <- scpAnnotateResults(
    vaRes, rowData(sce), by = "feature", by2 = "Sequence"
)

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 = 20, by = "percentExplainedVar", effect = "Treatment",
    decreasing = TRUE, combined = FALSE
) +
    scpVariancePlot(
    vaRes, top = 20, by = "percentExplainedVar", effect = "Chip",
    decreasing = TRUE, combined = FALSE
) +
    plot_layout(ncol = 1, guides = "collect")

We can also group these peptide according to their protein. We simply need to provide the fcol argument.

scpVariancePlot(
    vaRes, top = 20, by = "percentExplainedVar", effect = "Treatment",
    decreasing = TRUE, combined = FALSE, fcol = "Gene.names"
) +
    scpVariancePlot(
    vaRes, top = 20, by = "percentExplainedVar", effect = "Chip",
    decreasing = TRUE, combined = FALSE, fcol = "Gene.names"
) +
    plot_layout(ncol = 1, guides = "collect")

Differential abundance analysis

Next, we explore the model output to understand the differences between treatment conditions. 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. Since we have 3 groups, we explore the three possible contrasts, provided as a list.

(daRes <- scpDifferentialAnalysis(
    sce, contrast = list(c("Treatment", "CON", "LPS24"),
                         c("Treatment", "CON", "LPS48"),
                         c("Treatment", "LPS24", "LPS48"))
))

Similarly to analysis of variance, the results are a list of tables, one table for each contrast.

daRes[[1]]

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. Again, to better explore the results, we add the annotations available in the rowData.

daRes <- scpAnnotateResults(
    daRes, rowData(sce), 
    by = "feature", by2 = "Sequence"
)

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

scpVolcanoPlot(daRes)[[1]]

To help interpretation of the results, we will label the peptides with their protein name. Also we increase the number of labels shown on the plot. Finally, we can add colors to the plot. For instance, let's explore the impact of the number of observations using the $n/p$ ratio. We create a new annotation table, add it to the results and redraw the plot. The $n/p$ ratio is retrieved using scpModelFilterNPRatio. We show the improved plots for all contrasts.

np <- scpModelFilterNPRatio(sce)
daRes <- scpAnnotateResults(
    daRes, data.frame(feature = names(np), npRatio = np), 
    by = "feature"
)
scpVolcanoPlot(
    daRes, top = 30, textBy = "Gene.names", 
    pointParams = list(aes(colour = npRatio))
) |> 
    wrap_plots(guides = "collect")

As expected, higher number of observations (higher $n/p$) lead to increased statistical power and hence to more significant results. We can already see that some proteins are upregulated upon treatment such as heat shock proteins, Npm1 and Mdh2.

Finally, we offer functionality to report results at the protein level.

scpDifferentialAggregate(daRes, fcol = "Gene.names") |> 
    scpVolcanoPlot(top = 30, textBy = "Gene.names") |> 
    wrap_plots()

Component analysis

Finally, we perform component analysis to link the modelled effects to the cellular heterogeneity. We here run an APCA+ (extended ANOVA-simultaneous principal component analysis) for the treatment effect. In other words, we perform a PCA on the data that is capture by the treatment variable along with the residuals (unmodelled data).

(caRes <- scpComponentAnalysis(
    sce, ncomp = 20, method = "APCA", effect = "Treatment", 
    maxiter = 200
))

The results are contained in a list with 2 elements. bySample contains the PC scores, that is the component results in sample space. byFeature contains the eigenvectors, that is the component results in feature space.

caRes$bySample

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_Treatment). Each elements is a table with the computed components. Let's explore the component analysis results in cell space. Similarly to the previous explorations, we annotate the results.

caResCells <- caRes$bySample
sce$cell <- colnames(sce)
caResCells <- scpAnnotateResults(caResCells, colData(sce), by = "cell")

We then generate the component plot, colouring by Treatment. To assess the impact of batch effects, we shape the points according to the sample preparation batch (cf intro) as well.

scpComponentPlot(
    caResCells,
    pointParams = list(aes(colour = Treatment, shape = Chip))
) |>
    wrap_plots() +
    plot_layout(guides = "collect")

The data before modelling already shows some data pattern driven by the treatment as control samples are mostly characterized by low PC1 values, while treated cells show on average higher PC1 scores. However, the data also shows batch effects as cells from the same batch tend to group together. Batch effects are removed upon modelling and treatment conditions are better separated although they still overlap. There is not apparent structure in the residuals, indicating that the remaining variation may be attributed to noise.

We explore further the variance that is hidden in later components. In the chunk above, we computed 20 principal components. To explore the patterns they capture, we reduce these 20 PCs to 2 dimensions using t-SNE. We rely on the scater package to compute the t-SNE.

lapply(caResCells, function(ca) {
    pcs <- ca[, grep("^PC", colnames(ca))]
    tsne <- calculateTSNE(t(as.matrix(pcs)))
    data.frame(cbind(ca, tsne)) |> 
        ggplot() +
        aes(x = TSNE1,
            y = TSNE2,
            colour = Treatment,
            shape = Chip) +
        geom_point() +
        theme_minimal()
}) |> 
    wrap_plots(guides = "collect")

Taking the 20 principal components into account, we can see good separation between treatment after modelling while correctly mixing the batch effects. We can also clearly see a clear clustering of the sample preparation batches when data is not modelled. Again, the residuals show no interesting clustering.

Interactive visualisation with iSEE

You can manually explore the data through an interactive interface thanks to iSEE:

library("iSEE")
iSEE(sce)

Session information {-}

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "",
    crop = NULL
)

sessionInfo()

Citation

citation("scp")

License

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

UCLouvain-CBIO/2021-SCoPE2-replication documentation built on March 29, 2025, 12:10 p.m.