---
title: "Reporting missing values for Single Cell Proteomics"
author:
- name: Christophe Vanderaa
- name: Laurent Gatto
output:
BiocStyle::html_document:
self_contained: yes
toc: true
toc_float: true
toc_depth: 2
code_folding: show
bibliography: scp.bib
date: "`r BiocStyle::doc_date()`"
package: "`r BiocStyle::pkg_ver('scp')`"
vignette: >
%\VignetteIndexEntry{Reporting missing values}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
crop = NULL
)
```
# Introduction
This vignette demonstrates how to use `scp` to report missing values,
following our recommendations in @Vanderaa2023-gu. Briefly, we
recommend reporting at least 4 metrics:
- Total sensitivity, i.e. the total number of features found in the
dataset
- Local sensitivity, i.e. the number of features per cell
- Data completeness, i.e. the proportion of values that are not
missing
- Number of samples
We will also demonstrate how to estimate total sensitivity when the
number of samples is too low and how to report data consistency using
the distribution of the Jaccard indices.
In this vignette, we will assume you are familiar with the `scp`
framework. If this is not the case, we suggest you first read the
[introduction vignette](https://uclouvain-cbio.github.io/scp/articles/scp.html).
# Minimal data processing
First, we load the `scp` package and retrieve a real-life dataset from
the `scpdata` package.
```{r, warning=FALSE, message=FALSE}
library("scp")
library("scpdata")
leduc <- leduc2022()
```
Next, we reduce the size of the dataset to the 30 first acquisitions.
This allows for a fast execution of the code for this vignette while still being a
representative demonstration on a real dataset. We also keep only the
feature annotations that will be used later in the vignette.
```{r}
leduc <- leduc[, , 1:30]
leduc <- selectRowData(leduc, c(
"Sequence", "Leading.razor.protein", "Reverse",
"Potential.contaminant", "PEP"
))
```
This is the actual minimal processing:
1. filtering contaminant and low-quality features
2. replacing zeros by missing values
3. keep only samples that correspond to single cells
4. remove the feature absent in all samples.
```{r}
leduc <- filterFeatures(leduc, ~ Reverse != "+" &
Potential.contaminant != "+" &
PEP < 0.01) ## 1.
leduc <- zeroIsNA(leduc, i = names(leduc)) ## 2.
leduc <- filterNA(leduc, i = names(leduc), pNA = 0.9999)
leduc <- subsetByColData( ## 3.
leduc, leduc$SampleType %in% c("Monocyte", "Melanoma cell")
)
leduc <- dropEmptyAssays(leduc) ## 4.
```
# 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.
```{r, message=FALSE}
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.
```{r, message=FALSE}
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`.
```{r}
isMissing <- is.na(assay(leduc[["id"]]))
assay(leduc[["id"]])[isMissing] <- FALSE
```
# 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`.
```{r}
reportMissingValues(leduc, "id", by = leduc$SampleType)
```
# Advanced criteria
## Jaccard index distribution
The Jaccard index between a pair of cells is the number of features
shared by the two cells divided by the number of features identified
in any of the two columns. This provides a good measure of how
consistent the identifications are across single-cells. Again,
biological differences between cell types may decrease the consistency
between single cells and we therefore suggest to compute the Jaccard
index for each cell type separately. We compute the Jaccard index
using `jaccardIndex()`.
```{r}
ji <- jaccardIndex(leduc, "id", by = leduc$SampleType)
```
The function returns a `data.frame` that we visualize using the
`ggplot2` package.
```{r}
library("ggplot2")
ggplot(ji) +
aes(x = jaccard) +
geom_histogram() +
facet_grid(~ by)
```
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.
## 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.
```{r}
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.
```{r}
(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.
```{r}
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.
```{r}
predictSensitivity(csc, nSamples = Inf)
```
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.
# Citation
```{r citation}
citation("scp")
```
# License
This vignette is distributed under a
[CC BY-SA license](https://creativecommons.org/licenses/by-sa/2.0/)
license.
# Reference