# Reporting missing values for Single Cell Proteomics

Christophe Vanderaa and Laurent Gatto

#### 24 October 2023

#### Package

scp 1.12.0

# 1 Introduction

This vignette demonstrates how to use `scp` to report missing values, following our recommendations in Vanderaa and Gatto (2023). Briefly, we recommend reporting at least 4 metrics:

- Total sensitivity, i.e. the total number of features found in the dataset
- Local sensitivity, i.e. the number of features per cell
- Data completeness, i.e. the proportion of values that are not missing
- Number of samples

We will also demonstrate how to estimate total sensitivity when the number of samples is too low and how to report data consistency using the distribution of the Jaccard indices.

In this vignette, we will assume you are familiar with the `scp` framework. If this is not the case, we suggest you first read the introduction vignette.

# 2 Minimal data processing

First, we load the `scp` package and retrieve a real-life dataset from the `scpdata` package.

    library("scp")
    library("scpdata")
    leduc <- leduc2022()

Next, we reduce the size of the dataset to the 30 first acquisitions. This allows for a fast execution of the code for this vignette while still being a representative demonstration on a real dataset. We also keep only the feature annotations that will be used later in the vignette.

    leduc <- leduc[, , 1:30]
    #> Warning: 'experiments' dropped; see 'drops()'
    #> harmonizing input:
    #>   removing 8057 sampleMap rows not in names(experiments)
    #>   removing 1872 colData rownames not in sampleMap 'primary'
    leduc <- selectRowData(leduc, c(
        "Sequence", "Leading.razor.protein", "Reverse",
        "Potential.contaminant", "PEP"
    ))

This is the actual minimal processing: 1. filtering contaminant and low-quality features 2. replacing zeros by missing values 3. keep only samples that correspond to single cells 4. remove the feature absent in all samples.

    leduc <- filterFeatures(leduc, ~ Reverse != "+" &
                               Potential.contaminant != "+" &
                               PEP < 0.01) ## 1.
    #> 'Reverse' found in 30 out of 30 assay(s)
    #> 'Potential.contaminant' found in 30 out of 30 assay(s)
    #> 'PEP' found in 30 out of 30 assay(s)
    leduc <- zeroIsNA(leduc, i = names(leduc)) ## 2.
    leduc <- filterNA(leduc, i = names(leduc), pNA = 0.9999)
    leduc <- subsetByColData( ## 3.
        leduc, leduc$SampleType %in% c("Monocyte", "Melanoma cell")
    )
    leduc <- dropEmptyAssays(leduc) ## 4.

# 3 Peptide identification data

Next, we build the peptide identification matrix, that is a matrix with single cells as columns and peptides as rows that indicate whether a given peptide is observed or not in a given cell. To build this matrix, we need data at peptide level, hence we aggregate the peptide to spectrum match (PSM) data to peptide data. To do so, we need an aggregation function, `isIdentified()`. This functions considers that a peptide is observed in a cell (returns a `TRUE`) if there is at least one observed PSM belonging to the peptide in that cell.

    isIdentified <- function(x) colSums(!is.na(x)) != 0
    leduc <- aggregateFeatures(
        leduc, i = names(leduc),
        name = paste0("id_", names(leduc)),
        fcol = "Sequence",
        fun = isIdentified
    )

What about proteins? if we were interested in reporting missing values at the protein level, we simply need to change `fcol = "Sequence"` to `fcol = "Leading.razor.protein"`.

Then, we join all runs in a single large assay.

    leduc <- joinAssays(
        leduc, i = grep("^id_", names(leduc)),
        name = "id"
    )

Since not all peptides are found in all assays, some entries have been filled with `NA`s. These entries are missing and hence are replaced with `FALSE`.

    isMissing <- is.na(assay(leduc[["id"]]))
    assay(leduc[["id"]])[isMissing] <- FALSE

# 4 Report missing values

We can now compute the metrics of interest. We recommend computing these for each cell type separately, since biological properties specific to the cell type could influence the outcome. You can perform this using `reportMissingValues()`. We provide the dataset and point towards the assay with the identification matrix (`id`). The metrics are computed based on the cell annotation `SampleType` that is available in the `colData`.

    reportMissingValues(leduc, "id", by = leduc$SampleType)
    #>          LocalSensitivityMean LocalSensitivitySd TotalSensitivity Completeness
    #> Monocyte             2753.904           351.2502             7029    0.3872737
    #>          NumberCells
    #> Monocyte         197

# 5 Advanced criteria

## 5.1 Jaccard index distribution

The Jaccard index between a pair of cells is the number of features shared by the two cells divided by the number of features identified in any of the two columns. This provides a good measure of how consistent the identifications are across single-cells. Again, biological differences between cell types may decrease the consistency between single cells and we therefore suggest to compute the Jaccard index for each cell type separately. We compute the Jaccard index using `jaccardIndex()`.

    ji <- jaccardIndex(leduc, "id", by = leduc$SampleType)

The function returns a `data.frame` that we visualize using the `ggplot2` package.

    library("ggplot2")
    ggplot(ji) +
        aes(x = jaccard) +
        geom_histogram() +
        facet_grid(~ by)
    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

The Jaccard indices are mainly distributed between 40 and 60 %, meaning that about half of the features are consistently found across single-cells within the same cell type. Note also that some pairs of cells have consistency above 80 %. These are pairs of cells from the same acquisition runs that were multiplexed together with TMT labelling.

## 5.2 Assessing the total sensitivity

To assessing whether we can accurately estimate the total sensitivity, we generate a cumulative sensitivity curve (CSC). More precisely, we sample the identification matrix for an increasing number of cells (or runs) and count the number of distinct features found across the sampled cells. We repeat each sampling multiple times to account for the stochasticity of the approach. The approach is implemented in `cumulativeSensitivityCurve()`. Again, we compute the curve for each cell type separately. In the `leduc` dataset, several cells are acquired in an MS run. When a features is identified in a cell, it is most of the time also identified in all other cells of that run, and this will distort the cumulative sensitivity curve. Therefore, the function provides a `batch` argument to account for this. Finally, `nSteps` defines the number of random draws with increasing sample size, and `niters` defines how many times each draw must be iterated.

    csc <- cumulativeSensitivityCurve(leduc, "id", by = leduc$SampleType,
                                      batch = leduc$Set, niters = 10,
                                      nsteps = 30)

The function returns a `data.frame` that we visualize using the `ggplot2` package.

    (plCSC <- ggplot(csc) +
        aes(x = SampleSize, y = Sensitivity, colour = by) +
        geom_point(size = 1))

The cumulative sensitivity does not reach a plateau. This means that we underestimated the total sensitivity in the previous section. We use `predictSensitivity()` to predict the total sensitivity from these curves. The function fits an asymptotic regression model to assess the relationship between the sensitivity and the sample size. Then, it uses the model to predict the sensitivity for any sample size. (supplied through `nSamples`). The function requires the `data.frame` generated by `cumulativeSensitivityCurve()`. To assess the quality of the fit, we first predict the sensitivity for the range of sample size.

    predCSC <- predictSensitivity(csc, nSample = 1:30)
    plCSC + geom_line(data = predCSC)

We finally predict the total sensitivity, that is the sensitivity if we had an infinite number of samples.

    predictSensitivity(csc, nSamples = Inf)
    #>   SampleSize Sensitivity       by
    #> 1        Inf    7160.515 Monocyte

The total sensitivity is predicted to be about 7200 peptides for both cell types. So in the previous section, we underestimated the total sensitivity by about 150-200 peptides.

# 6 Citation

    citation("scp")
    #> To cite the scp package in publications use:
    #>
    #>   Vanderaa, Christophe, and Laurent Gatto. 2023.  Revisiting the Thorny
    #>   Issue of Missing Values in Single-Cell Proteomics. Journal of
    #>   Proteome Research 22 (9): 2775–84.
    #>
    #>   Vanderaa Christophe and Laurent Gatto. The current state of
    #>   single-cell proteomics data analysis. Current Protocols 3 (1): e658.;
    #>   doi: https://doi.org/10.1002/cpz1.658 (2023).
    #>
    #>   Vanderaa Christophe and Laurent Gatto. Replication of Single-Cell
    #>   Proteomics Data Reveals Important Computational Challenges. Expert
    #>   Review of Proteomics, 1–9 (2021).
    #>
    #> To see these entries in BibTeX format, use 'print(<citation>,
    #> bibtex=TRUE)', 'toBibtex(.)', or set
    #> 'options(citation.bibtex.max=999)'.

# 7 License

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

# Reference

Vanderaa, Christophe, and Laurent Gatto. 2023. “Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics,” April. http://arxiv.org/abs/2304.06654.