plexDIA (Derks et al. (2022)) enables the profiling of the proteome of single cells using a multiplexed DIA data acquisition strategy. The pipeline includes the nPOP sample processing protocole (Leduc, Gray Huffman, and Slavov (2022)), with mTRAQ labeling of the samples. The authors used the DIA-NN software (Demichev et al. (2020)) to identify and quantify the MS data.

Let’s first load the replication package to make use of some helper functions. Those functions are only meant for this replication vignette and are not designed for general use.


scp and the plexDIA data analysis workflow

The code provided along with the article can be retrieved from this GitHub repository. The objective of this vignette is to replicate the analysis script while providing standardized, easy-to-read, and well documented code. Therefore, our first contribution is to formalize the data processing into a conceptual flow chart.

Overview of the processing workflow by Derks et al.

Overview of the processing workflow by Derks et al.

This replication vignette relies on a data framework dedicated to SCP data analysis that combines two Bioconductor classes (Vanderaa and Gatto (2021)):

  • The SingleCellExperiment class provides an interface to many cutting edge methods for single-cell analysis
  • The QFeatures class facilitates manipulation and processing of MS-based quantitative data.

The scp vignette provides detailed information about the data structure. The scp package extends the functionality of QFeatures for single-cell application. scp offers a standardized implementation for single-cell processing methods.

The required packages for running this workflow are listed below.

## Core packages of this workflow

## Utility packages for data manipulation and visualization

scpdata and the leduc2022 dataset

We also implemented a data package called scpdata (Vanderaa and Gatto (2022)). It distributes published SCP datasets, such as the derks2022 dataset. The datasets were downloaded from the data source provided in the publication and formatted to a QFeatures object so that it is compatible with our software. The underlying data storage is based on the ExperimentHub package that provides a cloud-based storage infrastructure.

The derks2022 dataset is provided at different levels of processing:

  • The .raw and .d files that were generated by the mass-spectrometers. This data is not included in scpdata.
  • The DIA-NN main output report table that contains the results of the spectrum identification and quantification.
  • The DIA-NN MS1 extracted signal table, if a precursor was identified in at least one of the mTRAQ channels in the main report, signal (if any) will be extracted from the other channels regardless of whether there is sufficient evidence in those channels at 1% FDR.
  • A processed protein data table is provided as the final output of the data processing. This workflow will try to replicate this table.

The workflow starts with precursor tables and will generate the processed protein data. The authors provided the DIA-NN output tables and the sample annotation table through a Google Drive repo. Protein data are shared a TXT files. We highly value the effort the authors have made to publicly share all the data generated in their project, from raw files to final expression tables (see the Slavov Lab website).

We formatted the derks2022 dataset following our data framework. The formatted data can be retrieved from the scpdata package using the derks2022() function. All datasets in scpdata are called after the first author and the date of publication.

The derks2022 data combines 3 main datasets: the 100-cell equivalent samples acquired with a Q-Exactive instrument, the single-cell samples acquired with a Q-Exactive instrument (we call this dataset qe), and the the single-cell samples acquired with a timsTOF-SCP instrument (we call this dataset tims). Each dataset contains the DIA-NN main report data split over each run and the combined MS1 extracted data (see above for more details). The data also contains the protein data for all samples as processed by the authors.

(derks <- derks2022())
## An instance of class QFeatures containing 66 assays:
##  [1] F..JD.plexDIA.nPOP.wJD1194.raw: SingleCellExperiment with 19618 rows and 3 columns 
##  [2] F..JD.plexDIA.nPOP.wJD1195.raw: SingleCellExperiment with 19587 rows and 3 columns 
##  [3] F..JD.plexDIA.nPOP.wJD1196.raw: SingleCellExperiment with 19176 rows and 3 columns 
##  ...
##  [64] F..JD.plexDIA.nPOP.wJD1193.raw: SingleCellExperiment with 2643 rows and 3 columns 
##  [65] qe_prec_extracted: SingleCellExperiment with 8590 rows and 144 columns 
##  [66] proteins: SingleCellExperiment with 1475 rows and 164 columns

The datasets are stored in a QFeatures object. In total, it contains 66 different SingleCellExperiment objects that we refer to as assays. Each assay contains expression data along with feature metadata. Each row in an assay represents a feature, in this case a precursor or a protein depending on the assay. Each column in an assay represents a sample. During sample preparation, 3 samples are pooled using mTRAQ labeling, hence some assays contain 3 columns corresponding to each measured channel.

Using plot(), we can have a quick overview of the assays.

## Warning in plot.QFeatures(derks): The QFeatures object contains many assays. You
## may want to consider creating an interactive plot (set 'interactive = TRUE')

This figure is crowded, you can use plot(derks, interactive = TRUE) to interactively explore this map. However, we can see that the different runs in the top of the figure converge to 3 assays that contain the extracted MS1 precursor signal for the three datasets: bulk, tims and qe. Finally, the protein table contains data derived from all three datasets.

The objective of this vignette is to replicate the combined protein data table from the precursor assays following the same data processing workflow as the original study by Derks et al. but using standardized functionality.

We extract the proteins assay and keep it for later benchmarking. getWithColData() extract an assay of interest along with the associated sample annotations. We then remove the assay from the S object for the remainder of the processing. We apply this using removeAssays.

proteins_derks <- getWithColData(derks, "proteins")
## Warning: 'experiments' dropped; see 'metadata'
## Warning: Ignoring redundant column names in 'colData(x)':
derks <- removeAssay(derks, "proteins")
## Warning: 'experiments' dropped; see 'metadata'

Remove unwanted samples

Before starting the data processing, we remove the samples that are not considered by the authors to generate the protein data. The timsTOF-SCP dataset was acquired with a few bulk samples diluted to single-cell equivalent. These samples are ending with "_t_DB". The Q-Exactive dataset contains single-cells, but also a dozen negative controls (encoded as "Neg").

table(derks$Celltype, derks$dataset)
##                 bulk Q-Exactive timsTOFSCP
##   Melanoma         0         45          0
##   Melanoma_DB      3          0          0
##   Melanoma_t       0          0         10
##   Melanoma_t_DB    0          0          1
##   Neg              0         12          0
##   PDAC             0         45          0
##   PDAC_DB          3          0          0
##   PDAC_t           0          0         10
##   PDAC_t_DB        0          0          1
##   U-937            0         42          0
##   U-937_DB         3          0          0
##   U-937_t          0          0         10
##   U-937_t_DB       0          0          1

We remove those unwanted samples by accessing the information from the colData using subsetByColData().

derks <- subsetByColData(derks, !grepl("Neg|_t_DB", derks$Celltype))

Isotopic carry over correction

The first data processing step is to correct for isotopic carry over from one mTRAQ channel to another. This is performed using correctIsotopicCarryover(). The function takes an assay and returns the same assay with corrected quantifications. Since there are 3 datasets, we will loop over the 3 assays containing the extracted MS1 signal. These corrected assays are added to the QFeatures object using addAssay() and the links between the precursors are added using

for (i in c("bulk", "qe", "tims")) {
    inputAssay <- paste0(i ,"_prec_extracted")
    outputAssay <- paste0(i ,"_prec_corrected")
    x <- correctIsotopicCarryover(derks[[inputAssay]])
    derks <- addAssay(derks, x, name = outputAssay)
    derks <- addAssayLinkOneToOne(derks, from = inputAssay, 
                                  to = outputAssay)    
    cat("Finished correction for the", i, "dataset\n")
## Finished correction for the bulk dataset
## Finished correction for the qe dataset
## Finished correction for the tims dataset

Precursor FDR control

Precursors are then filtered to control for protein FDR. To perform this, we will again loop over the 3 datasets as the set of protein that comply to the FDR threshold is dataset-dependent. We retrieve the assays that link to extracted signal assay as these contain the FDR threshold. Once identified, we combine all the available rowData in a single table using rbindRowData(). This table is then filtered to retrieve the proteins with low FDR and we subset the corrected signal assay to contain only precursors that map those proteins. Finally, we restore any lost link between the precursors of corrected assay with its parent assay using addAssayLink().

for (i in c("bulk", "qe", "tims")) {
    ## Find the assays that contain the FDR (q-value) information
    repAssays <- assayLink(derks, paste0(i ,"_prec_extracted"))@from
    ## Combine all rowData in a single table
    rd <- rbindRowData(derks, repAssays)
    ## Perform filtering
    PGs_0.01FDR <- unique(rd$Protein.Group[rd$Lib.PG.Q.Value < 0.01])
    ## Apply the filter to the corrected signal assay
    x <- derks[[paste0(i ,"_prec_corrected")]]
    ind <- rowData(x)$Protein.Group %in% PGs_0.01FDR
    derks[[paste0(i ,"_prec_corrected")]] <- x[ind, ]
    ## AssayLinks are lost when removing proteins, we recreate them
    derks <- addAssayLink(derks, from = paste0(i ,"_prec_extracted"), 
                          to = paste0(i ,"_prec_corrected"),
                          varFrom = "Precursor.Id",
                          varTo = "Precursor.Id")
    cat("Finished FDR control for the", i, "dataset\n")
## Finished FDR control for the bulk dataset
## Warning in replaceAssay(x = x, y = value, i = i): Links between assays were
## lost/removed during replacement. See '?addAssayLink' to restore them manually.
## Finished FDR control for the qe dataset
## Finished FDR control for the tims dataset

discussion you find this chuk too complicated? Let’s discuss this by opening an issue here.

Combine the datasets

Up to now, we kept the data belonging to each MS run in separate assays. We now combine all batches into a single assay. This can easily be done using the joinAssays() function from the QFeatures package.

Consensus mapping of precursors to proteins

We need to account for a limitation when combining the data. joinAssays() will only keep the metadata variables that have the same value between matching rows. However, some precursors map to one protein group in one run and to another protein group in another run. Hence, the protein group is not constant for all precursors and is removed during joining. It is important we keep the protein group information in the rowData since we will later need it to aggregate precursors to proteins. To avoid this issue, we replace the problematic precursor to protein group mappings through a majority vote.

## Generate a list of DataFrames with the information to modify
precAssays <- grep("corrected$", names(derks), value = TRUE)
ppMap <- rbindRowData(derks, i = precAssays) %>%
    data.frame %>%
    group_by(Precursor.Id) %>%
    ## The majority vote happens here
    mutate(Protein.Group =
                          decreasing = TRUE))[1]) %>%
    select(Precursor.Id, Protein.Group) %>%
    filter(!duplicated(Precursor.Id, Protein.Group))
consensus <- lapply(precAssays, function(i) {
    ind <- match(rowData(derks[[i]])$Precursor.Id, 
    DataFrame(Protein.Group = ppMap$Protein.Group[ind])
## Name the list
names(consensus) <- precAssays
## Modify the rowData
rowData(derks) <- consensus

We now join the three datasets in a single assay. This is performed using joinAssays().

(derks <- joinAssays(derks, i = grep("corrected$", names(derks)),
                     name = "prec_corrected"))
## An instance of class QFeatures containing 69 assays:
##  [1] F..JD.plexDIA.nPOP.wJD1194.raw: SingleCellExperiment with 19618 rows and 3 columns 
##  [2] F..JD.plexDIA.nPOP.wJD1195.raw: SingleCellExperiment with 19587 rows and 3 columns 
##  [3] F..JD.plexDIA.nPOP.wJD1196.raw: SingleCellExperiment with 19176 rows and 3 columns 
##  ...
##  [67] qe_prec_corrected: SingleCellExperiment with 8416 rows and 132 columns 
##  [68] tims_prec_corrected: SingleCellExperiment with 8722 rows and 30 columns 
##  [69] prec_corrected: SingleCellExperiment with 23585 rows and 171 columns

The last assay, prec_corrected contains the data for all 3 datasets.

Filter missing data

Next, the authors require less than 60% missing data per cell. We compute the percent missing data for each column. We store it in the colData using derks$ <- for later use.

derks$pNA <- colMeans(assay(derks[["prec_corrected"]]) == 0, na.rm = TRUE)

In this step, the authors ignore NA’s and only count zeros. This underestimates the missing data rate per-cell. An alternative is to include NA’s as well, although it may overestimate the missing data rate when the different acquisition runs or dataset do not contain overlapping proteins. This is shown by the code chunk below as an example, but it is not executed and we’ll stick to the author’s original procedure.

derks <- zeroIsNA(derks, "prec_corrected")
derks$pNA <- nNA(derks, "prec_corrected")$nNAcols$pNA

We plot the percent missingness for each data set.

data.frame(colData(derks)) %>% 
    ggplot() +
    aes(y = pNA, 
        x = dataset, 
        color = Celltype) %>% 
    geom_beeswarm() +
    scale_size_manual(values = c(3, 0.8))

Most samples comply to the threshold. We remove the few single-cells with too many missing data using subsetByColdata() because we have stored pNA in the colData.

derks <- subsetByColData(derks, derks$pNA < 0.6)

Clean missing data

We replace the zero values by NA’s using zeroIsNA().

derks <- zeroIsNA(derks, "prec_corrected")

Batch correction

The authors then apply a first batch correction by aligning the precursor means across the different acquisition runs. First, we extract the combined precursor assay along with the colData using getWithColData() and extract the quantification matrix from it using assay()

sce <- getWithColData(derks, "prec_corrected")
## Warning: 'experiments' dropped; see 'metadata'
m <- assay(sce)

For each acquisition run, we divided each row by its mean.

for (batch in sce$File.Name) {
    ind <- which(sce$File.Name == batch) 
    m[, ind] <- sweep(m[, ind, drop = FALSE], FUN = "/", MARGIN = 1,
                      STATS = rowMeans(m[, ind, drop = FALSE], na.rm = TRUE))
assay(sce) <- m

We add the batch corrected values as a new assay using addAssay() and keep the links with the previous assay using addAssayLinkOneToOne().

sce$pNA <- NULL ## Remove pNA to avoid later conflicts
derks <- addAssay(derks, sce, name = "prec_runnorm")
derks <- addAssayLinkOneToOne(derks, from = "prec_corrected", to = "prec_runnorm")


The next step is to apply normalization. The authors perform it at the sample level and the precursor level.

Median normalize per sample

Median normalization per sample is available among the methods from normalizeSCP(). It will automatically add a new assay that we call prec_sampnorm

derks <- normalizeSCP(derks, "prec_runnorm", name = "prec_sampnorm",
                      method = "div.median")

Mean normalize per precursor

Mean normalization per sample is not available among the methods from normalizeSCP(), but can be applied using sweep() offering more flexibility.

rowm <- rowMeans(assay(derks[["prec_sampnorm"]]), na.rm = TRUE)
derks <- sweep(derks, i = "prec_sampnorm", name = "prec_featnorm", 
               MARGIN = 1, FUN = "/", STATS = rowm)

Protein aggregation

Up to now, the data was processed at the precursor level. One protein group can be represented by several precursors. For instance, there is one protein group that is represented by over 150 precursors.

     xlab = "Number precursors per protein group", main = "")

We aggregate all precursors belonging to the same protein group using aggregateFeatures(). In this case, fun = colMedians will assign a protein group abundance as the median precursor abundance.

derks <- aggregateFeatures(derks, i = "prec_featnorm", name = "prot",
                           fcol = "Protein.Group", fun = colMedians, 
                           na.rm = TRUE)

Batch correction

After protein agregation, the authors perform again a batch correction round. We use the same approach as above.

sce <- getWithColData(derks, "prot")
## Warning: 'experiments' dropped; see 'metadata'
## Warning: Ignoring redundant column names in 'colData(x)':
m <- assay(sce)
for (batch in sce$File.Name) {
    ind <- which(sce$File.Name == batch) 
    m[, ind] <- sweep(m[, ind, drop = FALSE], FUN = "/", MARGIN = 1,
                      STATS = rowMeans(m[, ind, drop = FALSE], na.rm = TRUE))
assay(sce) <- m
sce$pNA <- NULL ## Remove pNA to avoid later conflicts
derks <- addAssay(derks, sce, name = "prot_runnorm")
derks <- addAssayLinkOneToOne(derks, from = "prot", to = "prot_runnorm")


Again, the batch correction is followed by sample and protein normalization. We use the same approach as above.

derks <- normalizeSCP(derks, "prot_runnorm", name = "prot_sampnorm",
                      method = "div.median")
rowm <- rowMeans(assay(derks[["prot_sampnorm"]]), na.rm = TRUE)
derks <- sweep(derks, i = "prot_sampnorm", name = "prot_featnorm", 
               MARGIN = 1, FUN = "/", STATS = rowm)


The protein data is log2-transformed using logTransform().

derks <- logTransform(derks, i = "prot_featnorm", name = "prot_log",
                      base = 2)

Clean missing data

Log-transformation has generated infinite values because zeros were present in the data. We replace these infinite values and zeros by NA’s using infIsNA() and zeroIsNA(), respectively.

derks <- infIsNA(derks, "prot_log")
derks <- zeroIsNA(derks, "prot_log")

Filter missing data

Although we already performed a first filtering based on data missingness, we further filter on protein and sample missingness.

Filter on protein missingness

We filter out proteins with more than 95% missingness. This is performed using filterNA() that removes rows (proteins) with a missingness higher than the given threshold.

derks <- filterNA(derks, i = "prot_log", pNA = 0.95)

Filter on single-cell missingness

We then filter out samples with more than 95% missingness. This is performed using the same procedure as above.

derks$pNA <- colMeans([["prot_log"]])))
derks <- subsetByColData(derks, derks$pNA < 0.95)


Although we filtered on missing data, the protein data is majorly composed of missing values. The graph below shows the distribution of the proportion missingness in cells. Cells contain on average 65 % missing values.

data.frame(colData(derks)) %>% 
    ggplot() +
    aes(y = pNA, 
        x = dataset, 
        color = Celltype) %>% 

The datasets containing single-cells have more than 50% missing data that is imputed by the authors using a custom hierarchical clustering function. This function is available from SCP.replication as imputeKnnSCoPE2().

derks <- imputeKnnSCoPE2(derks, i = "prot_log", name = "prot_imp", k = 3)

Batch correction

A final batch correction is applied, this time using the ComBat algorithm from the sva package.

sce <- getWithColData(derks, "prot_imp")
## Warning: 'experiments' dropped; see 'metadata'
## Warning: Ignoring redundant column names in 'colData(x)':
batch <- sce$Label
mod <- model.matrix(~ Celltype, data = colData(sce))
assay(sce) <- ComBat(assay(sce), batch = batch, mod = mod)
derks <- addAssay(derks, sce, name = "prot_bc")
derks <- addAssayLinkOneToOne(derks, from = "prot_imp", to = "prot_bc")


Again, the batch correction is followed by sample and protein normalization. We use the same approach as above.

derks <- normalizeSCP(derks, "prot_bc", name = "prot_bc_sampnorm",
                      method = "center.median")
rowm <- rowMeans(assay(derks[["prot_bc_sampnorm"]]), na.rm = TRUE)
derks <- sweep(derks, i = "prot_bc_sampnorm", name = "prot_bc_featnorm", 
               MARGIN = 1, FUN = "-", STATS = rowm)

This last normalization step leads to the final data as processed by Derks et al. We will now compare our results with the results published by the authors.

Benchmark the replication

Compare with the authors’s processed data

We first compare the data generated by the authors’s orginal script and the data generated by this vignette.

Let’s first compare the cells that were retained after processing.

allElements <- union(colnames(proteins_derks), 
table(derks2022 = allElements %in% colnames(proteins_derks),
      scp = allElements %in% colnames(derks[["prot_bc_featnorm"]]))
##          scp
## derks2022 FALSE TRUE
##     FALSE     0    7
##     TRUE      7  157

There is a good agreement between the set of filtered cells after the data processing by the authors and the data processing in this vignette.

Let’s now compare the protein groups retained after processing.

allElements <- union(rownames(proteins_derks), 
table(derks2022 = allElements %in% rownames(proteins_derks),
      scp = allElements %in% rownames(derks[["prot_bc_featnorm"]]))
##          scp
## derks2022 FALSE TRUE
##     FALSE     0  561
##     TRUE    124 1351

Most protein groups are found in both data processing workflows, but the agreement is low.

Let’s now compare the quantitative data between the two matrices. To do so, we need to intersect the column names and the rownames.

rows <- intersect(rownames(proteins_derks),
cols <- intersect(colnames(proteins_derks),
err <- assay(proteins_derks)[rows, cols] - 
    assay(derks[["prot_bc_featnorm"]])[rows, cols]
data.frame(difference = as.vector(err[!])) %>%
    ggplot() +
    aes(x = difference) +
    geom_histogram(bins = 50) +
    xlab("plexDIA - scp") +
    scale_y_continuous(labels = scales::scientific) +

There are very large differences between the two workflows. Note that the differences are computed on a log2 scale. However, the mode of the distribution peaks around 0. These differences may arise because we had to change some implementations and constraints to use our scp framework. For instance, we had to re-implement the isotopic carryover correction using matrix operations instead of working with data.frames. Furthermore, as mentioned when combining the datasets, we had to perform a consensus vote when mapping precursors to protein groups across different datasets. These decisions taken at the beginning may have led to small deviations from the original workflow and may have propagated to larger deviations throughout the data processing steps.

Number precursors per cell

We here attempt to replicate figures 6b, 6c, 6h and 6i where the authors show the number of precursors and protein groups that were found for each cell type in each dataset. We first compute the number of non missing precursors per sample using countUniqueFeatures() on the joined precursor assay, prec_corrected. The counts are directly stored in the colData under nprecs. Next, we do the same for the protein groups. countUniqueFeatures() allows to group the features by a variable of the rowData. In this case, we set groupBy = "Protein.Groups". Note that we would get the same results if we applied countUniqueFeatures on the prot assay, or any protein level assay before imputation.

derks <- countUniqueFeatures(derks, i = "prec_corrected", colDataName = "nprecs")
derks <- countUniqueFeatures(derks, i = "prec_corrected", groupBy = "Protein.Group",
                             colDataName = "nprots")

We next retrieve the counts from the `colData’ and create the plot. We perform some faceting to display precursor and protein counts for all datasets in a single panel.

df <- data.frame(colData(derks))
ggplot(df) +
    aes(x = Celltype, y = nprecs, fill = Celltype) +
    facet_wrap(~ dataset, scales = "free") +
    labs(y = "Precursors per cell") +
    ggplot(df) +
    aes(x = Celltype, y = nprots, fill = Celltype) +
    facet_wrap(~ dataset, scales = "free") +
    labs(y = "Protein groups per cell") +
    plot_layout(guides = "collect", ncol = 1) &
    geom_boxplot() &
    geom_beeswarm(alpha = 0.7) &
    xlab("") &
    theme_classic() &
    theme(legend.position = "none")

The plot is in line with the article’s figure.

Bulk vs single-cell

We here attempt to replicate figures 6f and 6l where the authors show the correlation between the PDAC vs monocyte log fold change computed from single-cell samples and from bulk samples. Although the authors devoted a separate data processing to create the plot, we here decide to keep compare single-cells and bulk using the same processing steps. We extract the processed assay, compute the fold changes between PDAC and monoyctes for the three datasets and compute the correlations between the single-cell and bulk log fold changes.

sce <- getWithColData(derks, "prot_bc_featnorm")
## Warning: 'experiments' dropped; see 'metadata'
## Warning: Ignoring redundant column names in 'colData(x)':
m <- assay(sce)
logFC <- data.frame(PvsU_tims = rowMeans(m[, sce$Celltype == "PDAC_t"], na.rm = TRUE) - 
                        rowMeans(m[, sce$Celltype == "U-937_t"], na.rm = TRUE),
                    PvsU_qe = rowMeans(m[, sce$Celltype == "PDAC"], na.rm = TRUE) - 
                        rowMeans(m[, sce$Celltype == "U-937"], na.rm = TRUE),
                    PvsU_bulk = rowMeans(m[, sce$Celltype == "PDAC_DB"], na.rm = TRUE) -
                        rowMeans(m[, sce$Celltype == "U-937_DB"], na.rm = TRUE),
                    Protein.Group = rownames(m))
logFC <- left_join(logFC, data.frame(Protein.Group = c("P17096;P17096-3", "P07437", "P08729"),
                                     Protein.Name = c("HMGA1", "TUBB", "KRT7")), 
                   by = c("Protein.Group"))
corQE <- round(cor(logFC$PvsU_qe, logFC$PvsU_bulk, 
                   use = "pairwise.complete.obs", method = "spearman"), 2)
corTims <- round(cor(logFC$PvsU_tims, logFC$PvsU_bulk, 
                     use = "pairwise.complete.obs", method = "spearman"), 2)

We then plot the results as performed in the article.

ggplot(logFC) +
    aes(x = PvsU_bulk, 
        y = PvsU_qe) +
    ggtitle("Q-Exactive vs Bulk", subtitle = paste0("rho = ", corQE)) +
    ggplot(logFC) +
    aes(x = PvsU_bulk, 
        y = PvsU_tims) +
    ggtitle("timsTOF-SCP vs Bulk", subtitle = paste0("rho = ", corTims)) +
    plot_layout() &
    geom_pointdensity() &
    labs(x = "Log2 PDAC/U-937 100 cells", 
         y = "Log2 PDAC/U-937 1 cell") &
    scale_color_continuous(type = "viridis") &
    theme_classic() &
    theme(legend.position = "none") &
    geom_label_repel(aes(label = Protein.Name),
                     min.segment.length = unit(0, 'lines'),
                     box.padding = 2,
                     size = 2.75) 
## Warning: Removed 1909 rows containing missing values (geom_label_repel).
## Removed 1909 rows containing missing values (geom_label_repel).

We generated a similar figure as in the article.


We now plot the PCA to reproduce Figure 6p from the author’s paper. We use the pcaSCoPE2() function that implements the weighted PCA as suggested in Specht et al. (2021).

m <- assay(derks[["prot_bc_featnorm"]])
pcaRes <- pcaSCoPE2(m)
## Percent of variance explained by each principle component
pca_var <- pcaRes$values
percent_var<- pca_var/sum(pca_var)*100
## PCA scores data
pcaDf <- data.frame(PC = pcaRes$vectors[, 1:2],
## Format the sample annotations
pcaDf$Celltype[grepl("PDAC", pcaDf$Celltype)] <- "PDAC"
pcaDf$Celltype[grepl("Mel", pcaDf$Celltype)] <- "Melanoma"
pcaDf$Celltype[grepl("^U", pcaDf$Celltype)] <- "Monocyte"
pcaDf$Label <- paste0("mTRAQ", pcaDf$Label)

We then plot the PCA results, colouring the cells either depending on the mTRAQ label used to identify tag-specific effects and depending on the cell type to illustrate the ability of the technology to capture biological variability.

ggplot(pcaDf) +
    aes(color = Label) + 
    scale_color_manual(values = c("orange2","purple","turquoise")) +
    ggplot(pcaDf) +
    aes(color = Celltype) + 
    scale_color_manual(values = c("#52B788","#457B9D","#E07A5F")) +
    plot_annotation(title = "PCA of the processed data using 'scp'",
                    subtitle = paste0(sum(pcaDf$dataset != "Bulk"), " cells\n",
                                      nrow(m), " protein groups")) +
    plot_layout(guides = "collect") &
    aes(x = PC.1, 
        y = PC.2, 
        size = dataset,
        shape = dataset, 
        alpha = dataset) &
    geom_point(alpha = 0.66) &
    labs(x = paste0("PC1 (", round(percent_var[1],0),"%)"), 
         y = paste0("PC2 (", round(percent_var[2],0),"%)")) &
    scale_size_manual(values = c(5, 2, 2)) &

Although the we could not accurately replicate the data provided by the authors, we can clearly see the same global pattern as in the original work.

Reproduce this vignette

You can reproduce this vignette using Docker:

docker pull cvanderaa/scp_replication_docker:v1
docker run \
    -e PASSWORD=bioc \
    -p 8787:8787 \

Open your browser and go to http://localhost:8787. The USER is rstudio and the password is bioc. You can find the vignette in the vignettes folder.

See the website home page for more information.


Hardware and software

The system details of the machine that built the vignette are:

## Machine: Linux (5.15.0-48-generic)
## R version: R.4.2.1 (svn: 82513)
## RAM: 16.5 GB
## CPU: 16 core(s) - 11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz


The total time required to compile this vignette is:

## 4.057366 mins


The final leduc object size is:

## [1] "0.5 Gb"

Session info

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [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            
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## other attached packages:
##  [1] benchmarkme_1.0.8           ggpointdensity_0.1.0       
##  [3] patchwork_1.1.2             ggrepel_0.9.1              
##  [5] ggbeeswarm_0.6.0            forcats_0.5.2              
##  [7] stringr_1.4.1               dplyr_1.0.10               
##  [9] purrr_0.3.4                 readr_2.1.3                
## [11] tidyr_1.2.1                 tibble_3.1.8               
## [13] ggplot2_3.3.6               tidyverse_1.3.2            
## [15] sva_3.45.0                  BiocParallel_1.31.12       
## [17] genefilter_1.79.0           mgcv_1.8-40                
## [19] nlme_3.1-159                SCP.replication_0.2.1      
## [21] SingleCellExperiment_1.19.1 scpdata_1.5.4              
## [23] ExperimentHub_2.5.0         AnnotationHub_3.5.2        
## [25] BiocFileCache_2.5.0         dbplyr_2.2.1               
## [27] scp_1.7.4                   QFeatures_1.7.3            
## [29] MultiAssayExperiment_1.23.9 SummarizedExperiment_1.27.3
## [31] Biobase_2.57.1              GenomicRanges_1.49.1       
## [33] GenomeInfoDb_1.33.7         IRanges_2.31.2             
## [35] S4Vectors_0.35.4            BiocGenerics_0.43.4        
## [37] MatrixGenerics_1.9.1        matrixStats_0.62.0         
## [39] BiocStyle_2.25.0           
## loaded via a namespace (and not attached):
##   [1] readxl_1.4.1                  backports_1.4.1              
##   [3] BiocBaseUtils_0.99.12         systemfonts_1.0.4            
##   [5] igraph_1.3.5                  lazyeval_0.2.2               
##   [7] splines_4.2.1                 digest_0.6.29                
##   [9] foreach_1.5.2                 htmltools_0.5.3              
##  [11] fansi_1.0.3                   magrittr_2.0.3               
##  [13] memoise_2.0.1                 doParallel_1.0.17            
##  [15] googlesheets4_1.0.1           cluster_2.1.4                
##  [17] tzdb_0.3.0                    limma_3.53.10                
##  [19] Biostrings_2.65.6             annotate_1.75.0              
##  [21] modelr_0.1.9                  pkgdown_2.0.6                
##  [23] colorspace_2.0-3              rvest_1.0.3                  
##  [25] blob_1.2.3                    rappdirs_0.3.3               
##  [27] textshaping_0.3.6             haven_2.5.1                  
##  [29] xfun_0.33                     crayon_1.5.2                 
##  [31] RCurl_1.98-1.9                jsonlite_1.8.2               
##  [33] iterators_1.0.14              survival_3.4-0               
##  [35] glue_1.6.2                    gtable_0.3.1                 
##  [37] gargle_1.2.1                  zlibbioc_1.43.0              
##  [39] XVector_0.37.1                OrgMassSpecR_0.5-3           
##  [41] DelayedArray_0.23.2           scales_1.2.1                 
##  [43] DBI_1.1.3                     edgeR_3.39.6                 
##  [45] Rcpp_1.0.9                    viridisLite_0.4.1            
##  [47] xtable_1.8-4                  clue_0.3-61                  
##  [49] reticulate_1.26               bit_4.0.4                    
##  [51] MsCoreUtils_1.9.1             httr_1.4.4                   
##  [53] ellipsis_0.3.2                farver_2.1.1                 
##  [55] pkgconfig_2.0.3               XML_3.99-0.11                
##  [57] sass_0.4.2                    here_1.0.1                   
##  [59] locfit_1.5-9.6                utf8_1.2.2                   
##  [61] labeling_0.4.2                tidyselect_1.1.2             
##  [63] rlang_1.0.6                   later_1.3.0                  
##  [65] AnnotationDbi_1.59.1          munsell_0.5.0                
##  [67] BiocVersion_3.16.0            cellranger_1.1.0             
##  [69] tools_4.2.1                   cachem_1.0.6                 
##  [71] cli_3.4.1                     generics_0.1.3               
##  [73] RSQLite_2.2.17                broom_1.0.1                  
##  [75] evaluate_0.16                 fastmap_1.1.0                
##  [77] yaml_2.3.5                    ragg_1.2.3                   
##  [79] knitr_1.40                    bit64_4.0.5                  
##  [81] fs_1.5.2                      KEGGREST_1.37.3              
##  [83] AnnotationFilter_1.21.0       mime_0.12                    
##  [85] xml2_1.3.3                    compiler_4.2.1               
##  [87] beeswarm_0.4.0                filelock_1.0.2               
##  [89] curl_4.3.2                    png_0.1-7                    
##  [91] interactiveDisplayBase_1.35.0 reprex_2.0.2                 
##  [93] bslib_0.4.0                   stringi_1.7.8                
##  [95] highr_0.9                     desc_1.4.2                   
##  [97] lattice_0.20-45               ProtGenerics_1.29.0          
##  [99] Matrix_1.5-1                  vctrs_0.4.2                  
## [101] pillar_1.8.1                  lifecycle_1.0.2              
## [103] BiocManager_1.30.18           jquerylib_0.1.4              
## [105] bitops_1.0-7                  httpuv_1.6.6                 
## [107] R6_2.5.1                      bookdown_0.29                
## [109] promises_1.2.0.1              vipor_0.4.5                  
## [111] codetools_0.2-18              benchmarkmeData_1.0.4        
## [113] MASS_7.3-58.1                 assertthat_0.2.1             
## [115] rprojroot_2.0.3               withr_2.5.0                  
## [117] GenomeInfoDbData_1.2.9        parallel_4.2.1               
## [119] hms_1.1.2                     grid_4.2.1                   
## [121] rmarkdown_2.16                googledrive_2.0.0            
## [123] lubridate_1.8.0               shiny_1.7.2


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


Demichev, Vadim, Christoph B Messner, Spyros I Vernardis, Kathryn S Lilley, and Markus Ralser. 2020. DIA-NN: Neural Networks and Interference Correction Enable Deep Proteome Coverage in High Throughput.” Nat. Methods 17 (1): 41–44.
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Leduc, Andrew, R Gray Huffman, and Nikolai Slavov. 2022. “Droplet Sample Preparation for Single-Cell Proteomics Applied to the Cell Cycle.” bioRxiv.
Specht, Harrison, Edward Emmott, Aleksandra A Petelski, R Gray Huffman, David H Perlman, Marco Serra, Peter Kharchenko, Antonius Koller, and Nikolai Slavov. 2021. “Single-Cell Proteomic and Transcriptomic Analysis of Macrophage Heterogeneity Using SCoPE2.” Genome Biol. 22 (1): 50.
Vanderaa, Christophe, and Laurent Gatto. 2021. “Replication of Single-Cell Proteomics Data Reveals Important Computational Challenges.” Expert Rev. Proteomics, October.
———. 2022. “The Current State of Single-Cell Proteomics Data Analysis,” October.