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The current state of single-cell proteomics data analysis

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.


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 nonisobaric 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? ].
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.

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.  [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. 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).  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 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.

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 reimple-  Table S1 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.
ments functionality from the SCoPE2 script released by Specht et al. [53]. Next to scp, Schoof et al.
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.

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 SCeptre 1 , 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 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. 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.

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.

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) 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].

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.

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-thefly. 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 [? ]. 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.

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  [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).
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].

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.

Supplementary information
Supplementary tables Table S1: Description of SCP data processing methods. Step

Method Description
Sample quality control  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. Table S1: Description of SCP data processing methods. Step

Method Description
Missing data Remove peptides or proteins that contain too many missing values.    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.