CBIO’s EuroBioc2023 posters and talks

Long overdue now, but here are the CBIO lab’s contributions to the EuroBioc2023 conference that was organised on Ghent, on the 20 - 22 September 2023.

Poster: A reliable and reproducible resource for CT genes

Julie Devis, Axelle Loriot, Charles De Smet and Laurent Gatto

Cancer-Testis (CT) genes are tissue-specific genes whose expression is limited to the germline. They are normally repressed in somatic tissues, but can be aberrantly activated in tumors. For many CT genes, tumoral activation is enabled by loss of promoter DNA methylation. CT genes are of great interest. First, they have clinical potential as cancer-specific antigens, and can thus be used as target for cancer immunotherapy and as cancer biomarkers. Second, they are also a good model to study DNA demethylation in cancer, which is still poorly understood.

The definition of CT genes differs vastly according to the literature source. Some databases already exist [1,2,3] but they are neither up-to-date nor well annotated, and thus difficult to use. There is therefore a need for a reliable and reproducible resource when studying CT genes. We therefor created CTexploreR, a package that rigorously defines and explores CT genes. Our main objective was to propose a reliable and well-defined list of CT genes based on publicly available RNAseq databases. We also determined their precise transcription start site in order to be able to realise an accurate promoter methylation analysis. Our list contains 307 CT genes that were carefully classified as regulated by DNA methylation or not. We also developed functions to visualise CT genes expression and promoter DNA methylation in normal and tumoral tissues.

We also performed a thorough comparison of CTexploreR with the available resources mentioned above. It allowed us to clearly establish and characterise the difference between CT lists and clarify the origins of the inconsistencies. These analyses demonstrate that CTexploreR is a clear, curated and rigorously established up-to-date reference. Our package can thus be used as the starting point for further investigations.

[1] Almeida, L. G., Sakabe, N. J., deOliveira, A. R., Silva, M. C. C., Mundstein, A. S., Cohen, T., Chen, Y.-T., Chua, R., Gurung, S., Gnjatic, S., Jungbluth, A. A., Caballero, O. L., Bairoch, A., Kiesler, E., White, S. L., Simpson, A. J. G., Old, L. J., Camargo, A. A., & Vasconcelos, A. T. R. (2009). CTdatabase: a knowledge-base of high-throughput and curated data on cancer-testis antigens. Nucleic Acids Research, 37(Database issue), D816-9.

[2] Jamin, S. P., Hikmet, F., Mathieu, R., Jégou, B., Lindskog, C., Chalmel, F., & Primig, M. (2021). Combined RNA/tissue profiling identifies novel Cancer/testis genes. Molecular Oncology, 15(11), 3003–3023.

[3] Wang, C., Gu, Y., Zhang, K., Xie, K., Zhu, M., Dai, N., Jiang, Y., Guo, X., Liu, M., Dai, J., Wu, L., Jin, G., Ma, H., Jiang, T., Yin, R., Xia, Y., Liu, L., Wang, S., Shen, B., … Hu, Z. (2016). Systematic identification of genes with a cancer-testis expression pattern in 19 cancer types. Nature Communications, 7, 10499.

Workshop: CytoPipeline: Building and visualizing automated pre-processing and quality control pipelines for flow cytometry data

Workshop repo and pre-print

Philippe Hauchamps, Dan Lin and Laurent Gatto

With the increase of the dimensionality in conventional flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. Examples of such pipelines have been documented in the recent literature (e.g. [1],[2],[3]). A crucial part of these pipelines consists of pre-processing and applying quality control filtering to the raw data, in order to use high quality events in the downstream statistical analysis. This part can in turn be split into a number of elementary steps : margin events removal, signal compensation, scale transformations, debris and dead cells removal, batch effect correction,… etc.

However, when designing automated flow cytometry data analysis pipelines, assembling and assessing the pre-processing part can be challenging for a number of reasons. First, each of the involved elementary steps can be implemented using various methods and R packages. Second, the order of the steps can have an impact on the downstream analysis results. Finally, each method typically comes with its specific, unstandardized diagnostic and visualizations, making objective comparison difficult for the end user.

Here, we present CytoPipeline, an R package suite for building, assessing and comparing pre-processing pipelines for flow cytometry data. To exemplify our tool, we present the steps involved in designing a pre-processing pipeline on a real life dataset and demonstrate the visualization utilities. We also show how CytoPipeline can nicely complement benchmarking tools, like e.g. PipeComp [4], by providing user intuitive insight into benchmarking results.

[1] Quintelier, Katrien, Artuur Couckuyt, Annelies Emmaneel, Joachim Aerts, Yvan Saeys, and Sofie Van Gassen. 2021. “Analyzing High-Dimensional Cytometry Data Using FlowSOM.” Nature Protocols 16 (8): 3775–3801.

[2] Ashhurst, Thomas Myles, Felix Marsh-Wakefield, Givanna Haryono Putri, Alanna Gabrielle Spiteri, Diana Shinko, Mark Norman Read, Adrian Lloyd Smith, and Nicholas Jonathan Cole King. 2021. “Integration, Exploration, and Analysis of High-Dimensional Single-Cell Cytometry Data Using Spectre.” Cytometry. Part A: The Journal of the International Society for Analytical Cytology, no. cyto.a.24350 (April). https://doi.org/10.1002/cyto.a.24350.

[3] Nowicka, Malgorzata, Carsten Krieg, Helena L. Crowell, Lukas M. Weber, Felix J. Hartmann, Silvia Guglietta, Burkhard Becher, Mitchell P. Levesque, and Mark D. Robinson. 2017. “CyTOF Workflow: Differential Discovery in High-Throughput High-Dimensional Cytometry Datasets.” F1000Research 6 (May): 748.

[4] Germain, Pierre-Luc, Anthony Sonrel, and Mark D. Robinson. 2020. “pipeComp, a General Framework for the Evaluation of Computational Pipelines, Reveals Performant Single Cell RNA-Seq Preprocessing Tools.” Genome Biology 21 (1): 227.

Poster: fmsne - fast multi-scale neighbour embedding in R

Laurent Gatto and Cyril de Bodt

Dimensionality reduction (DR) has been a workhorse of large scale, multivariate omics data analysis from the early days. Since the advent of single-cell RNA sequencing, non-linear approaches have taken the front stage, with t-distributed stochastic neighbour embedding (t-SNE) [1,2] being one of, if not the main player. Packages such as Rtsne [3] and scater [4] have made it easy to apply t-SNE in R/Bioconductor workflows.

One sticking point with t-SNE is the single perplexity parameter, that controls the number of nearest high-dimensional (HD) neighbours that are taken into account when constructing the low-dimensional (LD) embedding: small (resp. large) values only enable preserving small (resp. large) neighbourhoods from HD to LD during DR, impairing the reproduction of large (resp. small) neighbourhoods. It is thus a key parameter, especially if the LD embedding is used for interpretation, which is often the case in omics-based applications.

Multi-scale neighbour embedding [5] is an extension to single-scale approaches such as t-SNE, that exempt users from having to set a single perplexity (scale) arbitrarily. Multi-scale approaches maximise the LD embedding quality at all scales, preserving both local and global HD neighbourhoods [6]. They have been shown to better capture the structure of data and to significantly improve DR quality [7].

Here, we present fmsne (https://github.com/lgatto/fmsne), an R package that relies on the basiliks package [8] to provide Bioconductor-friendly interface to fast multi-scale methods implemented in python. fmsne implements fast multi-scale functions such as runFMSTSNE() and plotFMSTSNE(), based on scater’s scater::run*() and scater::plot*() interface [4]. It also exposes the drQuality() function to assess DR quality using rank-based criteria [7]. Finally, we illustrate fast multi-scale methods on various single-cell datasets.

[1] van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579-2605.

[2] van der Maaten, L. (2014). Accelerating t-SNE using tree-based algorithms. Journal of Machine Learning Research, 15(1), 3221-3245.

[3] Jesse H. Krijthe (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation, URL: https://github.com/jkrijthe/Rtsne

[4] McCarthy DJ, Campbell KR, Lun ATL, Willis QF (2017). Scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R. Bioinformatics, 33, 1179-1186. doi:10.1093/bioinformatics/btw777

[5] C. de Bodt, D. Mulders, M. Verleysen and J. A. Lee, “Fast Multiscale Neighbor Embedding,” in IEEE Transactions on Neural Networks and Learning Systems, 2020, doi: 10.1109/TNNLS.2020.3042807.

[6] Lee, J. A., Peluffo-Ordóñez, D. H., & Verleysen, M. (2015). Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure. Neurocomputing, 169, 246-261.

[7] Lee, J. A., & Verleysen, M. (2009). Quality assessment of dimensionality reduction: Rank-based criteria. Neurocomputing, 72(7-9), 1431-1443.

[8] Lun ATL (2022). basilisk: a Bioconductor package for managing Python environments. Journal of Open Source Software, 7, 4742. doi:10.21105/joss.04742.

Talk: Linear models for single-cell proteomics

Christophe Vanderaa and Laurent Gatto

Mass spectrometry (MS)-based single-cell proteomics (SCP) has become a credible player in the single-cell biology arena [1,2]. Continuous technical improvements have pushed the boundaries of sensitivity and throughput. However, the computational efforts to support the analysis of these complex data have been missing. Strong batch effects coupled to high proportions of missing values complicate the analysis, causing strong entanglement between biological and technical variability [3,4].

We propose a simple, yet powerful approach to address this need: linear models. We use linear regression to model and remove undesired technical factors while retaining the biological variability, even in the presence of high proportions of missing values. The key advantage of linear models lies in the interpretability of the results they generate. Inspired by previous research [5], we streamlined modelling and exploration of the patterns induced by known technical and biological factors. The exploration enables a thorough assessment of the model coefficients, and highlights key factors influencing SCP experiments. Further exploration of the unmodelled variance recovers unknown but biologically relevant patterns in the data, leveraging the power of single-cell proteomics technologies. We successfully applied our approach to a diverse collection of SCP datasets [6], and could demonstrate that it is also amenable for integrating datasets acquired using different technologies.

We implemented and documented this approach in our Bioconductor package scp [7]. In summary, our approach represents a turning point for principled SCP data analysis, moving the tension point from how to perform the analysis to result generation and interpretation.

[1] “Single-Cell Proteomics: Challenges and Prospects.” 2023. Nature Methods 20 (3): 317–18.

[2] Bennett HM, Stephenson W, Rose CM, and Darmanis S. 2023. “Single-Cell Proteomics Enabled by next-Generation Sequencing or Mass Spectrometry.” Nature Methods, March.

[3] Vanderaa C, and Gatto L. 2021. “Replication of Single-Cell Proteomics Data Reveals Important Computational Challenges.” Expert Review of Proteomics, October, 1–9.

[4] Vanderaa C, and Gatto L. 2023. “The Current State of Single-Cell Proteomics Data Analysis.” Current Protocols 3 (1): e658.

[5] Thiel M, Féraud B, Govaerts B. 2017. “ASCA+ and APCA+: Extensions of ASCA and APCA in the Analysis of Unbalanced Multifactorial Designs.” Journal of Chemometrics 31 (6): e2895.

[6] Vanderaa C, and Gatto L.. scpdata: Single-Cell Proteomics Data Package. R package verison 1.6.0, https://bioconductor.org/packages/release/data/experiment/html/scpdata.html.

[7] Vanderaa C, and Gatto L.. scp: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis. R package version 1.8.0, https://bioconductor.org/packages/release/bioc/html/scp.html.

Workshop: Spectra - an expandable infrastructure to handle mass spectrometry data

Johannes Rainer, Sebastian Gibb, Laurent Gatto

Mass spectrometry (MS) data is a key technology in modern metabolomics and proteomics experiments. Continuous improvements in MS instrumentation, larger experiments and new technological developments lead to ever growing data sizes and increased number of available variables making standard in-memory data handling and processing difficult.

The Spectra package provides a modern infrastructure for MS data handling specifically designed to enable extension to additional data resources or alternative data representations. These can be realized by extending the virtual MsBackend class and its related methods. Implementations of such MsBackend classes can be tailored for specific needs, such as low memory footprint, fast processing, remote data access, or also support for specific additional data types or variables. Importantly, data processing of Spectra objects is independent of the backend in use due to a lazy evaluation mechanism that caches data manipulations internally.

This workshop discusses different available data representations for MS data along with their properties, advantages and performances. In addition, Spectra’s concept of lazy evaluation for data manipulations is presented, as well as a simple caching mechanism for data modifications. Finally, it explains how new MsBackend instances can be implemented and tested to ensure compliance.

Workshop: The R for Mass Spectrometry initiative - from raw data to identifications and quantitative proteomics data analysis

Laurent Gatto, Sebastien Gibb and Johannes Rainer

The aim of the RforMassSpectrometry initiative (https://www.rformassspectrometry.org/) is to provide efficient, thoroughly documented, tested and flexible R software for the analysis and interpretation of high throughput mass spectrometry assays. In this software demo, we will demonstrate three software packages that are central for proteomics data analysis.

• The Spectra package [1], that defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualise and compare spectra data.

• The PSMatch package [2] allows to load, process and analyse peptide-spectrum matches, and can, among others, explore and deconvolute the peptide-protein (group) relations using adjacency matrices and connected components.

• The QFeatures package [3] provides infrastructure to management and process quantitative features for high-throughput mass spectrometry assay, in particular so across assay levels (such as precursors, peptide spectrum matches, peptides and proteins or protein groups) in a coherent and tractable format.

We will conclude by illustrating how the MsExperiment package [5] can be used to bundle these three types of data together.

[1] Gatto L et al. (2023). Spectra Infrastructure for Mass Spectrometry Data, R package version 1.9.15. https://rformassspectrometry.github.io/Spectra/.

[2] Gatto L, Rainer J, Gibb S (2023). PSMatch: Handling and Managing Peptide Spectrum Matches. R package version 1.3.3, https://github.com/RforMassSpectrometry/PSM.

[3] Gatto L, Vanderaa C (2023). QFeatures: Quantitative features for mass spectrometry data. R package version 1.9.3, https://github.com/RforMassSpectrometry/QFeatures.

[4] Gatto L, Rainer J, Gibb S (2022). MsExperiment: Infrastructure for Mass Spectrometry Experiments. R package version 1.0.0, https://github.com/RforMassSpectrometry/MsExperiment.

Poster: A mixed-cell control design to assess data processing in single-cell proteomics

Samuel Grégoire, Sébastien Pyr dit Ruys, Christophe Vanderaa, Didier Vertommen and Laurent Gatto

Single-cell proteomics (SCP) aims at studying cellular heterogeneity by focusing on the functional effectors of the cells, proteins. While this is essential to identify cells undergoing subtle processes and point out underlying relevant protein and proteoform abundance patterns, assessing protein content inside a single cell is challenging.

Thanks to recent breakthroughs in mass spectrometry and sample processing, it has become possible to increase the depth of proteome covered, reduce the time needed to analyse a cell and make this technology more accessible [1].

However, extracting meaningful biological information from this type of data requires robust and suitable data analysis methods. Progress in this field is tempered by the lack of standardised workflows. Currently, data analysis workflows are custom made and substantially different from one research team to another [2]. Moreover, it is difficult to evaluate specific steps or entire pipelines as ground truths are missing. In an effort to bridge the gap towards the standardisation of SCP data analysis, our team has developed the scp package [3] relying on the QFeatures and SingleCellExperiment infrastructures to provide a standardised framework for SCP data analysis. In addition, we produced our own SCP datasets to constitute a basis for data analysis benchmarking. To this end, we used a design containing cell lines mixed in known proportions to generate controlled variability [4].

In this work, we used the scp package to test different combinations of data processing steps and evaluated them using our ground truth data. We illustrate how we benefited from this modular, standardised framework and highlight some crucial steps.

[1] Slavov, Nikolai. Scaling Up Single-Cell Proteomics. Molecular & Cellular Proteomics 21, no 1 (2022): 100179. https://doi.org/10.1016/j.mcpro.2021.100179.

[2] Vanderaa, Christophe, and Laurent Gatto. 2023. The Current State of Single-Cell Proteomics Data Analysis. Current Protocols 3 (1): e658. https://doi.org/10.1002/cpz1.658

[3] Vanderaa Christophe and Laurent Gatto. Replication of Single-Cell Proteomics Data Reveals Important Computational Challenges. Expert Review of Proteomics, 1–9 (2021). https://doi.org/10.1080/14789450.2021.1988571

[4] Tian, L., Dong, X., Freytag, S. et al. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat Methods 16, 479–487 (2019). https://doi.org/10.1038/s41592-019-0425