#  The current state of single-cell proteomics data analysis

Christophe Vanderaa  Computational Biology and Bioinformatics Unit (CBIO), de
Duve Institute, UCLouvain, Belgium  Laurent Gatto  Computational Biology and
Bioinformatics Unit (CBIO), de Duve Institute, UCLouvain, Belgium

###### Abstract

Sound data analysis is essential to retrieve meaningful biological information
from single-cell proteomics experiments. This analysis is carried out by
computational methods that are assembled into workflows, and their
implementations influence the conclusions that can be drawn from the data. In
this work, we explore and compare the computational workflows that have been
used over the last four years and identify a profound lack of consensus on how
to analyze single-cell proteomics data. We highlight the need for benchmarking
of computational workflows, standardization of computational tools and data,
as well as carefully designed experiments. Finally, we cover the current
standardization efforts that aim to fill the gap and list the remaining
missing pieces, and conclude with lessons learned from the replication of
published single-cell proteomics analyses.

Email<EMAIL_ADDRESS>
Keywords: mass spectrometry, proteomics, single-cell, data analysis,
reproducible research.

##  1 Introduction

Conducting a principled data analysis is not trivial, especially when
technologies and the data they generate increase in complexity at a fast pace.
This is particularly true for mass spectrometry (MS)-based single-cell
proteomics (SCP) data analysis. Several hurdles need to be overcome in order
to extract biologically meaningful information from these complex data [61].
Numerous methods exist to correct for technical issues, and each method has
its respective advantages and drawbacks. In this review article, we show that
the variety of available methods to process proteomics data and the current
lack of computational standards has lead to a great heterogeneity in SCP data
analysis practices. This computational heterogeneity is a reflection of the
technical heterogeneity since MS-based SCP has undergone many improvements.
For instance, two sample preparation strategies currently co-exist: SCP by
label-free quantification (LFQ) and multiplexed SCP [31, 47, 13]. Multiplexing
strategies include isobaric labelling, using tandem mass tags (TMT), or non-
isobaric labelling, using mass differential tags for relative and absolute
quantification (mTRAQ) [41, 17]. Several chips have been developed starting
with the nanoPOTS chip [70], followed by the N2chip [65], the proteoCHIP [27],
or the microfluidic SciProChip [24]. Efforts have also focused on automation
of the sample processing and reported the successful integration of robot
handlers such as the Mantis [41], the OT-2 [35] or the CellenOne [27, 33, 65]
dispensing devices. Several MS instruments have been used such as orbitraps or
time of flight instruments [40, 6, 17] Furthermore, new acquisition strategies
are implemented such as data independent acquisition mode [14, 17, 15, 6, 24],
prioritized data acquisition [26], or increased precursor sampling and
identification transfer [62, 66] that all allow for reduced missing values.
Finally, several groups reported the acquisition of post-translational
modifications, further increasing the biological resolution of the technology
[40, Li2021-qv]. This technical heterogeneity is thoroughly justified and
benchmarked; each publication demonstrates the added value of its experimental
workflow. As the field demonstrates its potential, efforts are made to make
the technology broadly accessible and standardized through detailed protocols
[41, 33, 26, 17] or by replacing custom-built material with commercially
available devices [35, 57]. Several groups performed a thorough fine-tuning of
experimental and instrumental parameters to better understand their impact on
analytical performance [56, 9, 51]. The current state of the field and the
opportunities to push the SCP technology to its full potential are regularly
being discussed, sparking the interest of a growing community [34, 52, 45, 31,
47, 13, 49, 48, 46]. These efforts however mostly focus on the technical
aspects of the technology and overlook the current computational practices.

In this review, we provide a computational perspective to the discussion and
examine the current approaches and practices for analysing SCP data,
specifically focusing on quantitative data processing. The first section
highlights the current heterogeneity in SCP data processing. The next section
covers the existing tools that bring a solution to the current hurdles.
Finally, the last section provides several guidelines on how to improve SCP
data analysis practices.

##  2 Quantitative data processing lacks consensus

Proteomics data analysis encompasses three main tasks: spectral data
processing, quantitative data processing and downstream data analysis.
Spectral data processing identifies and quantifies the peptides from the
acquired MS spectra. Assigning peptide sequences to MS spectrum was
spotlighted as an important challenge for SCP data analysis [46] and several
groups have contributed to methodological and software improvements. For
instance, Yu et al. extended the match between run (MBR) algorithm from
MaxQuant to TMT data, taking advantage of the quantification data present in
unidentified MS2 spectra [67]. The iceR package also propagates information
across runs. The algorithm dramatically improves peptide identification and
outperforms MBR [30]. Unfortunately, iceR is only applicable to label-free
data. Another approach to improve peptide identification is to increase the
confidence of matching by re-scoring. Re-scoring uses the annotations
generated by the search engines such as the deviation between expected and
measured elution times or m/z, the peptide length, or the ion charge [59], to
update the score or probability that measured spectra correctly match spectra
from a theoretical or empirical spectral library. DART-ID, a Bayesian
framework to update posterior error probabilities based on an accurate
estimation of elution times, has been applied to SCP data and showed a
significant increase in the number of identified spectra [8]. Others have also
improved the Percolator re-scoring algorithm for SCP experiments [20, 19],
although the measured improvements were subtle. While these developments
considerably improve the quality of spectrum identification, no dedicated
developments in quantitative data processing have been reported.

Quantitative data processing plays a critical role to overcome many technical
artefacts and to satisfy downstream analysis requirements. It consists of
several steps. Quality controls ensure the analysed data are composed of
reliable information and remove features of low quality that could otherwise
compromise the validity of the results. Aggregation combines peptide level
data into protein level data. Log-transformation shapes the data so that the
quantitative values follow normal distributions. Imputation generates
estimates for missing values. Finally, normalization and batch correction aim
to remove technical differences between samples and are essential to avoid
biased results. Each of these steps is implemented using different methods.
For instance, many methods exist for missing value imputation: replace by
zero, replace with random values sampled from an estimated background
distribution, replace by values estimated from the K-nearest neighbours
(KNN),…The imputation methods have different underlying assumptions that have
been extensively reviewed in the bulk proteomics field [5], but further
research is required to assess whether these assumptions remain valid or not
for SCP data. Besides choosing the right method, finding a correct sequence of
steps is another challenge. For instance, batch effects influence missing data
and vice versa [61]. It has been suggested to correct for batch effects before
imputation [16], but batch correction methods such as ComBat [29] break with
highly missing data as in SCP data.

As of today, developing computational workflows for SCP quantitative data
processing requires expert knowledge. We refer to “computational workflow” or
“computational pipeline” as the sequence of steps and methods that process
quantification data for downstream statistical testing or visualization.
Computational workflows are built from scratch and their development often
lacks an explicit rationale. Since we lack systematic comparisons, benchmarks
or guidelines, the processing approaches become fundamentally different
between publications. To illustrate our claim, we review the computational
approaches from several studies that shaped the SCP landscape since 2018
(Table 1). These studies present significant contributions to the field and
showcase applications on actual single cells (as opposed to bulk lysate
dilutions). Five studies supplemented their publication with material allowing
to repeat, at least partially, their computational analysis. Three studies
from the Slavov Lab provide the R code and the data required to fully repeat
their results [53, 33, 17]. The code is however poorly documented and
difficult to re-use by other labs. Schoof et al. also offer the data used to
repeat their study and distribute their computational workflow as a documented
python library, sceptre [44]. Their library heavily relies on scanpy, a
popular python library for scRNA-Seq analysis [64]. Finally, Brunner et al.
provide a python script that also relies on scanpy, but it lacks an explicit
link with the input data [6]. Based on the available material (scripts for
[53, 44, 6, 33, 17] or the methods section for the others), we constructed
Figure 1. We divide the workflow steps in 7 general categories and further
group the different steps depending on whether they are applied at the
precursor/peptide to spectrum match (PSM) level, peptide level, protein level
or implicitly embedded in an MS data preprocessing software.

Table 1:  Overview of influential SCP studies. These studies were published between 2018 and 2022. MaxQuant, FragPipe, Proteome Discoverer (PD), and DIA-NN are software tools to conduct peptide identification and quantification. The peptide identification is performed by underlying search engines such as Andromeda, MS-GF+, MSFragger or SEQUEST. Multiplexing relies on TMT or mTRAQ labelling while no labelling implies an LFQ approach. Some publication link to associated computational scripts to reproduce the analysis that were written either in python or R. The throughput is expressed in number of cells retained after sample quality control, if any (Figure 1A). Study | Publication date | Raw data analysis | Labeling | Script | Throughput | Reference
---|---|---|---|---|---|---
Zhu et al. 2018 | Sep 2018 | MaxQuant/Andromeda | — | — | 6 | [69]
Budnik et al. 2018 | Oct 2018 | MaxQuant/Andromeda | TMT-10 | — | 190 | [7]
Dou et al. 2019 | Oct 2019 | MS-GF+,MASIC | TMT-10 | — | 72 | [18]
Zhu et al. 2019 | Nov 2019 | MaxQuant/Andromeda | — | — | 28 | [71]
Cong et al. 2020 | Jan 2020 | MaxQuant/Andromeda | — | — | 4 | [10]
Tsai et al. 2020 | May 2020 | MaxQuant/Andromeda | TMT-11 | — | 104 | [56]
Williams et al. 2020, LFQ | Aug 2020 | MaxQuant/Andromeda | — | — | 17 | [63]
Williams et al. 2020, TMT | Aug 2020 | MaxQuant/Andromeda | TMT-11 | — | 152 | [63]
Liang et al. 2020 | Dec 2020 | FragPipe/MSFragger | — | — | 3 | [35]
Specht et al. 2021 | Jan 2021 | MaxQuant/Andromeda | TMT-11,TMT-16 | R | 1,490 | [53]
Cong et al. 2021 | Feb 2021 | PD/SEQUEST | — | — | 6 | [11]
Schoof et al. 2021 | Jun 2021 | PD/SEQUEST | TMT-16 | Pyhon | 2,025 | [44]
Woo et al. 2021 | Oct 2021 | MaxQuant/Andromeda | TMT-16 | — | 108 | [65]
Brunner et al. 2022 | Feb 2022 | DIA-NN | — | Python | 231 | [6]
Leduc et al. 2022 | Mar 2022 | MaxQuant/Andromeda | TMT-18 | R | 1,556 | [33]
Woo et al. 2022 | Mar 2022 | MaxQuant/Andromeda | — | — | 155 | [66]
Webber et al. 2022 | Apr 2022 | PD/SEQUEST | — | — | 28 | [62]
Derks et al. 2022 | Jul 2022 | DIA-NN | mTRAQ-3 | R | 155 | [17]

Several conclusions can be drawn from Figure 1. First, one publication
corresponds to one workflow. This variability cannot be explained solely by
different experimental protocols. The computational pipelines by Schoof et al.
and Specht et al. differ substantially, while their TMT-based acquisition
protocols are closely related [53, 44], and the computational pipeline by
Liang et al. for processing LFQ data [35] is more similar to the TMT
processing workflow of Williams et al. than its LFQ alternative. Moreover,
some publications provide a minimalistic computational workflow, with only 3
steps, while others perform extensive processing, with 20 steps. These
observations highlight the lack of consensus and the need to identify critical
steps in computational pipelines. Second, some processing steps are applied at
the peptide level or at the protein level. For instance, Budnik et al. perform
normalization at the peptide level, whereas Dou et al. perform normalization
at protein level (Figure 1I). A clear pattern is that most pipelines process
the data at the protein level, which is questionable since processing data at
an earlier stage could avoid the propagation of technical artefacts to the
protein data [32, 54]. Third, a great majority of the methods are taken from
bulk proteomics. We foresee that developing new methods that account for the
properties inherent to single-cell data would significantly improve the
workflows. For instance, batch correction could benefit from dedicated single-
cell methods as the strong dependency between batch effects and missing data
requires robust and tailored models [61]. Horizontal integration of samples
from different batches is an active field of research in single-cell omics [3]
that will probably be beneficial to the SCP community.

Figure 1:  (Continued on the following page.)

Overview of quantitative data processing workflows. A-H The workflows are
split into 8 categories represented by different colours. Each category
contains a set of methods that are represented by different shapes (see Table
LABEL:table:method_description for a description of the methods). Each point
indicates which method (column) is implemented in which publication (row).
Some methods are used in several workflows (points align vertically) and some
workflows used several methods (points align horizontally). I Summary of the
sequence of processing steps for each workflow. Depending on the workflow, the
processing steps are implicitly executed by the MS data software and applied
at the PSM/precursor, peptide or protein level. Colours and shapes follow the
structure from the previous panels. Each horizontal line represents a workflow
and should be read from left to right.

Figure 1 highlights the need for a better understanding on how to process and
model SCP data. Identifying which workflows perform best or demonstrating
whether a new workflow improves performance requires thorough computational
benchmarking. The scRNA-Seq field already offers tools for method benchmarking
that could readily be used for SCP applications [55, 25]. In order to run
these tools, the computational workflows should be accessible to the
benchmarking software. Another key consideration is that benchmarking datasets
are required to enable an objective comparison between computational
pipelines. While many SCP datasets are available from public sources, these
data are provided as different formats. Proper benchmarking necessitates a
standardization of the computational pipelines and the data. In the next
section, we cover the recent developments that attempt to harmonize
quantitative data processing for SCP.

##  3 Current solutions for quantitative data processing

We recently published an R/Bioconductor package called scp [61] (Table 2).
First, scp is thoroughly documented as we want to facilitate its re-use.
Second, it is designed as a modular tool where each processing step, such as
those defined in Figure 1 can easily be chained, and returns a consistent and
standardized output format. Third, the software is part of the Bioconductor
project [28] that is well known for exemplary coding practices and promotes
long term maintenance. Fourth, scp can be integrated with other tools that
rely on QFeatures and SingleCellExperiment, two data structures widely used
for proteomics and single-cell data analysis, respectively [22, 1]. Finally,
scp is maintained and improved to include the current state-of-the-art
methods. For instance, it reimplements functionality from the SCoPE2 script
released by Specht et al. [53]. Next to scp, Schoof et al. developed sceptre,
a python module that implements their computational workflow [44] (Table 2).
The code is well documented, modular and relies on scanpy [64], a python data
structure equivalent to SingleCellExperiment. The tool however lacks
flexibility as it was developed primarily to offer a reproducible data
analysis environment. Minor code refactoring could overcome this lack of
flexibility.

Computational solutions require data in order to develop, test and benchmark
individual methods and complete workflows. We therefore also recently
developed another R/Bioconductor package, scpdata, that distributes curated
SCP datasets ready for analysis [61] (Table 2). The datasets were retrieved
from published work and are accessible using a single command. The
standardization effort provides an thoroughly annotated and consistent data
structure, facilitating data analysis with tools such as scp. Furthermore,
scpdata relies on Bioconductor’s storage services, ExperimentHub [39], that
offers cloud-based data access. Easy access and consistent formats enable
method development on a variety of different datasets, avoiding dataset-
specific over-fitting. scpdata can also be used for benchmarking, although
ground truths are missing to perform accuracy validation.

Standardized data processing tools allow going beyond the reproduction of
existing SCP data analyses, it enables their replication. While reproduction
allows others to regenerate the same results using the same software or
computational setup, replication uses different software or analysis methods
to generate the same, or equivalent results. Replication, therefore,
consolidates our trust in previous work. Although a replicable analysis does
not imply the results are correct, it guarantees the results do not rely on
undocumented steps or on software peculiarities. For instance, we have shown
that the SCoPE2 analysis script by Specht et al. could be fully replicated
using scp and scpdata [53, 61]. Replication efforts further have beneficial
side effects. Replication can highlight hurdles that prevent accurate data
analysis. Continuing with the SCoPE2 example, our replication study identified
batch effects and missing data, and their dependence, as prominent challenges
that future SCP computational tools will need to tackle. Another beneficial
side effect is that replication studies are easily repurposed for
demonstration. As part of this overview, we offer a website, SCP.replication,
with replication studies that demonstrate the analysis of SCP data using the
scp and scpdata packages (Table 2). It contains several replication articles,
spanning TMT and LFQ protocols, and DDA and DIA data. We also converted the
replication material into openly available workshop material. The workshop can
be run without prior installation requirements thanks to the Orchestra
platform supported by the Bioconductor project.

##  4 Different workflows lead to different results

To illustrate the impact of data processing on the analysis outcome, we
compare two computational workflows: SCoPE2, released by Specht et al., and
SCeptre111We use sceptre (all lowercase) to refer to the python library, and
we use SCeptre (uppercase “SC”) to refer to the computational workflow.,
released by Schoof et al. We retrieved the data from scpdata and recreated
both pipelines with scp. SCeptre uses a custom implementation for batch
correction that is provided by the sceptre library. Thanks to two R packages,
reticulate [58] and zellkonverter [68], we could easily integrate the python
utilities to scp. We then ran the two workflows on the two datasets and
compared the results in Figure 2 and Figure 3. Both the cell type consistency,
given by the silhouette widths (Figure 2A and Figure 3A), and the within cell
type correlation distributions (Figure 2B and Figure 3B) are affected by the
computational workflow. The effect is most visible on the principal component
analysis (PCA) plots for the Specht et al. 2021 dataset (Figure 3D, E). The
data processed with the SCeptre workflow are organized in a horseshoe shape in
lower dimensions. This effect is commonly attributed to the presence of a
latent continuous variable or gradient, and a careful data exploration
revealed the presence of residual batch effects (Figure 4D). Unsupervised
clustering of the processed protein data leads to different groups, even
though we used identical methods and parameters (Figure 2C and Figure 3C). The
number of identified clusters differs between the data processed by SCoPE2 and
SCeptre. Furthermore, some clusters from one workflow are scattered throughout
clusters from the other workflow. Unsupervised clustering is used to identify
groups of cells from which to infer a functional state. So, different
clustering results can lead to different biological interpretation. To
objectively quantify the performance improvement between the two workflows, we
need controlled designs with known expectations. Benchmarking efforts using
mixture designs have already been performed for scRNA-Seq [55]. Tian et al.
acquired both intact single cells and diluted bulk lysates from 3 cell lines
mixed at different proportions and different quantities. These data were used
to assess the ability of computational pipelines to retrieve the original
design. However, increased performance of a computational workflow on a single
data set is not sufficient. Different types of SCP data exist (LFQ and TMT
data, DDA and DIA data, orbitrap and time of flight data,…) and computational
workflows may not generalize well for all SCP protocols and data. In other
words, we do not expect a single computational workflow to perform optimally
for all SCP datasets as different workflows may focus on different
characteristics in the data, such as the pattern of missing values. Therefore,
strengths and weaknesses of computational pipelines need to be identified and
documented. To evaluate this, a community effort could replicate the mixture
design using the different SCP protocols as already seen for scRNA-Seq [38].
This approach would allow to further assess robustness of the workflows on
different types of SCP data. Although published SCP datasets are available
and, as demonstrated above, they can be processed using standardized software,
we are still lacking the data needed to quantify the performance of
computational workflows.

Figure 2: Impact of quantitative data processing workflows on the Schoof et
al. 2021 dataset. All results presented in this figure were computed from the
protein data processed by the corresponding workflow. A The silhouette widths
provide a measure of cell type consistency. Cell types are defined based on
the known labels provided with the data. The silhouettes were computed using
the Jaccard similarity on the shared nearest neighbour graph (K = 15). B
Pearson correlations are computed between all cells with the same cell type
label and provide a measure of protein quantification consistency. C
Unsupervised clustering is performed using Louvain clustering [Blondel2008-cx]
on a shared nearest neighbour graph (K = 15). The heatmap illustrates the cell
distributions across the clustering results computed from the SCoPE2 and the
SCeptre workflow. Frequency is given as the number of cells. D,E The first 2
principal components of the protein data. Colours indicate the cell type
labels.  Figure 3:  Impact of quantitative data processing workflows on the
Specht et al. 2021 dataset. All results presented in this figure were computed
from the protein data processed by the corresponding workflow. A The
silhouette widths provide a measure of cell type consistency. Cell types are
defined based on the known labels provided with the data. The silhouettes were
computed using the Jaccard similarity on the shared nearest neighbour graph (K
= 15). B Pearson correlations are computed between all cells with the same
cell type label and provide a measure of protein quantification consistency. C
Unsupervised clustering is performed using Louvain clustering [Blondel2008-cx]
on a shared nearest neighbour graph (K = 15). The heatmap illustrates the cell
distributions across the clustering results computed from the SCoPE2 and the
SCeptre workflow. Frequency is given as the number of cells. D,E The first 2
principal components of the protein data. Colours indicate the cell type
labels.

##  5 Lessons learned from replication of SCP data analysis

When building the replication studies, we faced several practical challenges
regarding the computational analysis of SCP data. In this section, we provide
several recommendations that we hope will help practitioners to improve and
facilitate future SCP data analyses. While several of these lessons are
applicable to bulk proteomics, we focus on SCP examples here.

###  5.1 Complex analyses require suitable tools

Several search engines for raw MS data identification have been applied to SCP
data: MaxQuant/Andromeda, SEQUEST, MS-GF+, MSFragger (Table 1). While
MaxQuant/Andromeda is by far the most popular among those search engines,
several authors observed that it was the worst performing tool in the context
of SCP [59, 35, 57, 11]. These observations indicate that one should compare
the results of several search engines in order to maximize the number of
reliable spectrum identifications. Moreover, all search engines applied to SCP
data so far have been developed for bulk proteomics data. Boekweg et al.
showed that SCP data have different spectral properties compared to bulk
proteomics data and, hence, the field would benefit from new search engines
developed specifically for SCP data [4]. Quantitative data processing is also
carried out using different analysis software. Spreadsheet-based and graphical
user-drive software are currently predominant. However, method development is
facilitated by programming languages such as R and python. Utilizing
programming languages involves a steep learning curve and is often limited to
expert data analysts, but it offers access to more advanced methods and is a
direct and proven solution for assessable, replicable and re-usable
computational analyses. Finally, an important criterion when choosing software
is its maintenance activity. It has recently been shown that software accuracy
best correlates with the author’s commitment to its maintenance [21].

###  5.2 Consistent input formats facilitate data analysis

Formatting input data is a time-consuming and error-prone task when performing
data analysis. To limit this hurdle, scp and sceptre implement functionality
to read structured data tables. sceptre is designed to read Proteome
Discoverer tables and requires plate annotations and FACS data. scp is more
generic and has been used to read tables from MaxQuant, Proteome Discoverer,
DIA-NN and requires a sample annotation table. Both implementations require
consistent inputs, as provided by software that export consistent output
tables. Conversely, sample annotation tables depend on the experimenter. When
building the scpdata packages, we realized that annotation tables are often
lacking and hence needed to be created from the methods section or from the
file names. In other cases, annotations were available through different files
and required heavy data wrangling. This process is labor-intensive and error-
prone. We suggest creating consistent annotation tables where each row
represents a sample (single-cell, TMT carrier, negative control, …) and
columns represent technical or biological variables [23]. These variables are
then used during statistical modelling to distinguish biological and technical
variability. The annotation tables also require thorough documentation of the
information each column contains. Consistent input formats streamline data
analysis, facilitate the evaluation of the experimental design (Figure 4B and
C), and provide the information needed for principled data modelling.

###  5.3 Beware of confounding effects

All experiments are prone to technical variability and noise, but a good
design of experiment and principled data analysis can disentangle this
undesired technical variability from the desired biological variability. This
is also the case for bulk proteomics, but the technical challenges are
exacerbated when dealing with single-cell data [61]. Real-life SCP experiments
require the acquisition of over hundreds or thousands of cells spread across
many MS runs. Each acquisition run is prone to technical factors that
influence the quantification results. For instance, the MS signal drift arises
from a continuous distortion of the signal between sequential runs, as already
described for bulk proteomics [16]. Figure 4A confirms that MS drift is also
present in SCP data. Differences between cells over acquisition time are
higher than the differences between cell types at each time point. A careful
design of experiment has spread the two cell types over time and hence the
biological effects can be decoupled from MS drift thanks to batch correction
or statistical modelling. Neglecting this technical effect can have dramatic
consequences. As an example, Figure 4B depicts an SCP experiment where single
cells are blocked at one of 4 different cell division stages. Unfortunately,
these 4 categories were acquired sequentially, confounding desired biological
and unwanted technical sources of variation, and impairing deconvolution of
the technical and the biological variability. When conducting multiplexed
experiments, one must keep in mind that the labels also influence single-cell
quantification [44]. This effect was overlooked in Figure 4C where each TMT
tag is assigned to only one cell type. Again, this impairs computational
modelling of the TMT effects although in this case the biological variance is
more important than the variance associated with the TMT label.

To overcome such confounding effect, it is crucial to carefully design an
experiment using an adequate statistical blocking scheme and collect data
about any technical factors that may influence the results of an experiment
(such as LC-MS/MS maintenance, the type of instrument used, the multiplexing
labels, the sample preparation batch, the lab that performed the experiment or
cell culture batch), that can interact with known biological factors should
also be gathered, such as cell line, subject ID or tr eatment condition.
Single cells should then be randomized across all the identified factor
levels. [44, 53, 41, 23]. Unfortunately, technical constraints may not allow
for randomized designs. For instance, precious samples from patients may need
to be processed on-the-fly. Hence, the patient identity and their clinical
phenotype will inevitably be correlated with other technical factors. In
scRNA-Seq, pseudo-bulking has been successfully applied to perform
differential expression analysis when the experimental condition is correlated
with the subject. Pseudo-bulking consist in aggregating cells belonging to the
same individual after identification and separation of the cell
(sub-)populations [37, 12]. However, how to aggregate proteomics data is still
to be explored. Another alternative is to use linear mixed models. Although
these models are computationally more expensive, they overcome the need for
aggregation by modelling protein estimates from the peptide data
[Goeminne2020-op]. Finally, dedicated efforts are required to better monitor
and control batch effects in SCP data.

Finally, it is important to validate the batch correction and exclude residual
batch effects. For instance, we observed that the cell types in the Specht et
al. 2021 dataset cannot be separated in lower dimensions when processed by the
SCeptre pipeline. This is because most of the variability is explained by
residual batch effects (Figure 4D) indicating that the batch correction method
implemented in SCeptre is not suited for this dataset. We recommend exploring
the effect of technical variables in lower dimension to offer an intuition on
residual batch effects. Batch correction assessment can be adapted from bulk
proteomics, such as comparing correlations within batches and within
conditions or correlations between unrelated peptides and peptides from the
same protein. We refer to [16] for a thorough discussion.

Figure 4:  Confounding effects cause undesired variability. A MS drift for the
KLLEGEESR peptide in the Specht et al. 2021 dataset [53]. The peptide data was
processed using scp according to the script provided along the original paper
up to log-transformation (Figure 1I). The batch index is ordered by time of
acquisition. The MS drift is highlighted by a grey line computed using LOESS.
B PCA plot that replicates the results by Brunner et al. [6]. The protein data
was processed using scp as an R alternative to the python script provided
along the original paper (Figure 1I). Shapes represent the cell cycle stage;
colours represent the day of acquisition. C PCA plot that replicates the
results by Williams et al. [63]. The protein data was processed using scp
according to the workflow described in the Methods section of the original
paper (Figure 1I). Shapes represent the cell type; colours represent the TMT
label. D Same as Figure 3D, but coloured according to the MS acquisition batch
(n = 149 batches).

###  5.4 Quantitative data processing depends on downstream analyses

The purpose of quantitative data processing is to prepare the data for
downstream analyses. Downstream analyses process data into interpretable
statistical results that in turn can lead to new biological knowledge. Several
approaches have successfully been applied to SCP data. Dimension reduction
condenses the data in fewer variables. These data embeddings are often used
for data visualization, clustering or trajectory analysis. Differential
abundance or differential detection tests identify proteins whose abundance
are statistically different given a distribution model between experimental
conditions or cell clusters. For the latter, significant proteins are called
marker proteins that can be used to perform cell type identification.
Trajectory analysis infers differentiation or response tracks in the data by
estimating a pseudo-timeline. It was recently speculated that SCP technologies
enable the study of direct protein regulatory interactions, opening the
analysis to the discovery of new regulatory mechanisms using an untargeted
approach [50]. Quantitative data processing and downstream analysis could also
be combined in a single statistical framework. While no such methods have been
developed for SCP data yet, there are several examples from the scRNA-Seq
field that could be adapted to SCP. For instance, scVI [36] or ZINB-WaVE [42]
implement a modelling procedure that performs normalization, batch correction,
imputation and dimension reduction as part of the same fitting process.
Furthermore, scVI offers a Bayesian approach to perform hypothesis testing
directly from the estimated model parameters. While these compelling modelling
procedures get rid of many processing steps, they still require a thorough
sample quality control and feature selection. Whatever the chosen downstream
method is, quantitative data processing workflows must match the underlying
assumptions and data distributions. For instance, dimension reduction using
PCA requires data imputation, but dimension reduction using non-iterative
partial least squares (NIPALS) offers a similar alternative that is robust
against missing values, hence does not require imputation [2, 61].
Quantitative values need to be batch-corrected when running a t-test otherwise
the results will become biased and inaccurate. However, when using linear
regression, technical factors can be directly included as part of the model
and do not require previous batch correction [43].

##  6 Concluding remarks

While standardized SCP protocols are applied outside the pioneering labs [41,
33, 35, 57], computational workflows to process SCP data still lack any form
of standardization. The overwhelming diversity of pipelines makes it difficult
to make informed decisions as how to analyse SCP data. We provide important
guidelines to orient the design of the data analysis. First, SCP designs and
data analyses are complex, and analysis tools should be carefully chosen.
Second, robust data analysis relies on consistent and standardized data
formats. Standardized data structures should facilitate sample annotation
biological and technical factors that influence data acquisition. Third,
accounting for batch effects is essential to avoid assigning biological
discoveries to technical variation, especially for SCP experiments, comprising
ever-increasing numbers of single cell samples. Finally, the processing of
quantitative data highly depends on the research question at hand and, hence,
on the downstream analysis to perform. It is not possible to define a good
computational workflow without defining the task to accomplish.

More work is required to offer clear answers on how to set up optimal SCP
experimental designs and associated computational pipeline. The field still
lacks understanding of the impact of each processing step on the final
results. Workflows are still built based on empirical and arbitrary decisions.
As the technology gains in momentum and more groups start to embrace SCP,
setting more complex designs, standardized and benchmarked computational
pipelines are needed to guarantee sound data interpretation. Indeed, strong
data analyses principles and frameworks will enable the technology to reach
its full potential. Low quality results generated by flawed analysis practices
could penalize the field instead of incentivize for better analysis.
scp/scpdata and sceptre represent strong foundations that can support
computational benchmarking efforts.

#### Conflict of Interest Statement

The authors have no relevant affiliations or financial involvement with any
organization or entity with a financial interest in or financial conflict with
the subject matter or materials discussed in the manuscript. This includes
employment, consultancies, honoraria, stock ownership or options, expert
testimony, grants or patents received or pending, or royalties.

#### Acknowledgments

This work was funded by a research fellowship of the Fonds de la Recherche
Scientifique-FNRS.

#### Data Availability Statement

All data used to create the figures in this article are available from the
scpdata package [60]. The R code to reproduce the figures is available at:
https://github.com/UCLouvain-CBIO/2022-scp-data-analysis.

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## Internet resources

Table 2:  List of SCP data, computational and didactic resources. |  Tool |  Language |  Description and link
---|---|---|---
Data |  scpdata |  R |  R/Bioconductor package providing published SCP datasets ready for analysis in R.
|  |  |  https://bioconductor.org/packages/release/data/experiment/html/scpdata.html
Processing |  scp |  R |  R/Bioconductor package to process quantitative SCP data, supporting all steps and methods depicted in Figure 1.
|  |  |  https://bioconductor.org/packages/release/bioc/html/scp.html
|  sceptre |  Python |  Python package extending the functionalities of Scanpy to analyse multiplexed SCP data. It implements the Schoof et al. 2021 workflow in Figure 1.
|  |  |  https://github.com/bfurtwa/SCeptre
Reproduction |  SCP.replication |  R |  Website with 8 SCP data analysis articles that replicate published studies.
|  |  |  https://uclouvain-cbio.github.io/SCP.replication
|  SCeptre notebooks |  Python |  Code repository containing a set of jupyter notebooks that reproduce the results presented in Schoof et al. 2021.
|  |  |  https://github.com/bfurtwa/SCeptre/tree/master/Schoof_et_al/code
|  SlavovLab code |  R |  Set of code repositories that reproduce the results published by the Slavov lab.
|  |  |  https://github.com/Single-cell-proteomics
|  Brunner code |  Python |  Code repository with a jupyter notebook to reproduce the results in Brunner et al. 2022.
|  |  |  https://github.com/theislab/singlecell_proteomics
Tutorials |  SCP workshop |  R |  Online tutorial with hands-on exercises.
|  |  |  https://lgatto.github.io/QFeaturesScpWorkshop2021

## Supplementary information

### Supplementary tables

Table S1:  Description of SCP data processing methods. |  |
---|---|---
|  |
Step |  Method |  Description
|  |
|  |
Sample quality control |  Failed runs |  Remove samples that belong to an MS acquisition that failed based on the number of PSMs.
|  Missing data |  Remove samples that contain too many missing values. Too many is defined by the authors either using the percentage missing value per cell or the number of identified proteins per cell.
|  Median CV |  Remove samples for which the median coefficient (CV) of variation is higher than a user provided threshold. CVs are computed from peptides belonging to the same proteins and CVs per cell across proteins are summarised using the median.
|  MAD sum intensity |  Remove samples for which the total protein signal intensity per cell falls outside the median absolute deviation (MAD). Briefly, the summed intensities are computed for each cell. The median of the absolute differences between the summed intensities and the computed median is the MAD. All cells that have their summed intensity higher than the median plus MAD or lower than the median minus MAD are removed.
|  Grubb’s test |  Iteratively remove outlying cells based on the ratio of the absolute deviation to the mean of all cells and the standard deviation. Outlying cells are remove based on a significance threshold ($\alpha=0.05$).
Feature quality control |  Contaminants and decoys |  Remove identified peptides that match the contaminant and/or the decoy database.
|  Empty signal |  Remove features for which no signal is recorded across all single cells.
|  FDR filtering |  Remove peptides or proteins that have an associated false discovery rate (FDR) greater than a user provided threshold, usually 1%. FDR is computed by the search engine during peptide identification.
|  Missing data |  Remove peptides or proteins that contain too many missing values. Too many is defined by the authors either using the percentage missing value per feature or the number of cells the feature was identified in.
|  Unique peptides per protein |  Remove proteins that were identified and quantified from less than 2 unique peptides.
|  Spectral purity |  Remove PSMs that have a low spectral purity, that is the proportion of signal from the identified peptide compared to signal from background or co-eluting peptide. Spectral purity is computed by the search engine during peptide identification.
|  Sample to carrier ratio |  Remove the PSMs for which the ratio between the average intensity across single-cells over the signal in the carrier channel is higher than a user provided threshold, usually 10%.
Imputation |  KNN |  Missing values are imputed using the k-nearest neighbours (KNN) averaging. In other words, for each cell, the missing value for a protein is replaced with the average intensity of K most similar cells (in Euclidean space) for which that protein is not missing. K is defined y the user or optimized by maximizing the silhouette width between known cell types.
|  zero |  Missing values are replaced by a zero value.
|  Normal distribution |  Missing values are replaced by values sampled from a normal distribution. The mean of the distribution is defined as the global mean of non-missing values downshifted by a user provided value, and the standard deviation of the distribution is set by the user as well.
Log-transform |  log2 |  Replace quantitative values by the $log_{2}$ of the values.
|  log1p |  Replace quantitative values by the $ln$ of the values with a pseudo count of one.
Aggregation |  Random selection |  Pick one feature to represent the aggregated feature. This method was used to aggregate PSMs to charged peptides. In this context there are about 0.1% of charged peptides that originate from more than one PSM.
|  Summed |  Combine quantitative data from multiple features by summing their intensities.
|  Top N |  Combine quantitative data from multiple features by summing intensities of the N most intense features.
|  Median |  Combine quantitative data from multiple features by taking the median of their intensities.
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|  iBAQ |  intensity based absolute quantification (iBAQ) combines peptides into protein quantifications by summing the peptide intensities divided by the number of theoretically observable peptides.
|  maxLFQ |  Protein quantifications are computed from peptide quantification using delayed normalization and maximal peptide ratio extraction. See [Cox2014-bs] for detailed information on the method.
Normalization |  Sample median |  Quantitative values for each single cell are divided or subtracted by the median for that cell.
|  Feature mean |  Quantitative values for each feature are divided or subtracted by the mean for that feature.
|  Feature median |  Quantitative values for each feature are divided or subtracted by the median for that feature.
|  Total |  Quantitative values for each feature are divided or subtracted by the median for that feature.
|  Quantile |  Quantitative values are transformed so that the qualtile distribution are aligned between all single cells.
|  standardization |  Quantitative values for each feature are subtracted by the average and divided by the standard deviation.
Batch correction |  Reference |  For all cells within a batch and for each feature, the quantitative data are divided or subtracted by the reference samples within that run.
|  Median |  For all cells within a batch and for each feature, the quantitative data are divided or subtracted by the median values within that run.
|  ComBat |  Batch effects are removed using linear regression with an Empirical Bayes framework. The method models the quantifications of each protein with respect to a batch factor as well as the biological factors to protect from correction.
|  limma |  This method is very similar to ComBat. It allows for more than one batch factors.
Other |  Isotope impurity correction |  The signal associated to a label is corrected by removing the signal associated to impurities from the other labels. This is performed using a compensation matrix provided by the label manufacturer or from theoretical isotypic composition. This is only applicable for multiplexed experiments.
|  Quantification thresholding |  Any quantitative value below a user provided threshold is set to be missing. See [9] for more detailed information on how to set the threshold.
|  S/N computation |  Convert MS signal intensities to signal-to-noise ratio (S/N). The noise signal is estimated by the spectrum quantification software.