Proprietary methods and reliable results

AltraBio’s offering encompasses the entire data analysis workflow for flow, spectral and mass cytometry data analysis, from automated gating to cross-sample analysis, quality controls, and biomarker identification.

You need to apply your gating strategy to a large number of files
  • 1 to 4 weeks to generate a dedicated gating automaton
  • Fast processing (5-10 min/file), 24/7
  • Apply your expertise to the development of new strategies and/or the biological interpretation of your results instead of spending your time gating
  • Our automata consider all the markers used in your study. This allows them to better discriminate cell populations than with biplots.
  • Once its performance validated, your gating automaton will be frozen and then used on all the files of your study. Updates are still possible but will lead to a new automaton with a new serial number.
  • Thanks to automation, the use of cytometry for large clinical studies is no longer a problem.
You need to identify marker cell populations for diagnosis or for predicting the response to a treatment
Our validated solutions identified the relevant cell populations to:

  • evaluate the measurable residual disease (MRD) in different blood cancers.
  • predict responder patients for the anti-CTL4 anti-cancer drug.
  • diagnose an autoimmune disease.

Our methods are able to identify discriminative cell subsets at different granularities along an axis of cell differentiation resulting in nested cell subsets: for instance in the T cell population, the relevant subsets can range from the broader subset of memory CD8 cells to the more specific embedded subset of effector memory CD8.

  • Our approach is less sensitive to batch effects.
  • Our methods can incorporate additional information such as patient outcome to guide cluster identification to further increase the relevance of identified populations and avoid spurious artefacts.
You need to explore your cytometry data
  • Gating by dimension reduction: Principal Component Analysis, Minimum Spanning Tree layouts (e.g. SPADE), Multi Dimensional Scaling, t-stochastic neighbor embeddings (e.g. ViSNE), UMAP, etc.
  • Clustering: topological/graph-based approaches (e.g. SamSPECTRAL), density-based approaches (e.g. Flock), model-based approaches (e.g. immunoClust, FLAME, FlowClust, flowMerge), hybrid approaches (e.g. FlowSOM, Phenograph, FlowPeaks, FlowMeans, etc.), ensemble approaches, etc.
  • Statistical modeling
  • Generalized linear models, mixed models (etc) for (1) differential analysis of abundance of cell populations or (2) differential analysis of marker expression stratified by cell population.
  • Machine learning 
  • Supervised learning (e.g., Random Forest, Boosting, SVM, (sparse) PLS), correlation identification, etc.
  • Specific task-dedicated algorithms
  • CITRUS, RchyOptimyx, etc.


« They are highly efficient and agile, you will interact with only a few people so they are quick to respond and provide high quality services »

« They do that extra bit of quality control, they also check the transfers, they put that extra effort to make sure that what we do is accurate »

« There were some timelines that needed to be met and they stepped up and said OK we‘ll get this done in a few days, not in a week, not in a month … when you have that relationship, when you understand the value and you understand the timelines of the customer, that really feels like a partnership”

Our Publications In Cytometry Data Analysis

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Soret, Perrine; Dantec, Christelle Le; Desvaux, Emiko; Foulquier, Nathan; Chassagnol, Bastien; Hubert, Sandra; Jamin, Christophe; Barturen, Guillermo; Desachy, Guillaume; Devauchelle-Pensec, Valérie; Boudjeniba, Cheïma; Cornec, Divi; Saraux, Alain; Jousse-Joulin, Sandrine; Barbarroja, Nuria; Rodríguez-Pintó, Ignasi; Langhe, Ellen De; Beretta, Lorenzo; Chizzolini, Carlo; Kovács, László; Witte, Torsten; Bettacchioli, Eléonore; Buttgereit, Anne; Makowska, Zuzanna; Lesche, Ralf; Borghi, Maria Orietta; Martin, Javier; Courtade-Gaiani, Sophie; Xuereb, Laura; Guedj, Mickaël; Moingeon, Philippe; Alarcón-Riquelme, Marta E; Laigle, Laurence; Pers, Jacques-Olivier

A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome Journal Article

In: Nat Commun, vol. 12, no. 1, pp. 3523, 2021, ISSN: 2041-1723.

Abstract | Links | BibTeX

Bossini-Castillo, Lara; Villanueva-Martin, Gonzalo; Kerick, Martin; Acosta-Herrera, Marialbert; López-Isac, Elena; Simeón, Carmen P; Ortego-Centeno, Norberto; Assassi, Shervin; Hunzelmann, Nicolas; Gabrielli, Armando; de Vries-Bouwstra, J K; Allanore, Yannick; Fonseca, Carmen; Denton, Christopher P; Radstake, Timothy Rdj; Alarcón-Riquelme, Marta Eugenia; Beretta, Lorenzo; Mayes, Maureen D; Martin, Javier

Genomic Risk Score impact on susceptibility to systemic sclerosis Journal Article

In: Ann Rheum Dis, vol. 80, no. 1, pp. 118–127, 2021, ISSN: 1468-2060.

Abstract | Links | BibTeX


Beretta, Lorenzo; Barturen, Guillermo; Vigone, Barbara; Bellocchi, Chiara; Hunzelmann, Nicolas; Langhe, Ellen De; Cervera, Ricard; Gerosa, Maria; Kovács, László; Castro, Rafaela Ortega; Almeida, Isabel; Cornec, Divi; Chizzolini, Carlo; Pers, Jacques-Olivier; Makowska, Zuzanna; Lesche, Ralf; Kerick, Martin; Alarcón-Riquelme, Marta Eugenia; Martin, Javier

Genome-wide whole blood transcriptome profiling in a large European cohort of systemic sclerosis patients Journal Article

In: Ann Rheum Dis, vol. 79, no. 9, pp. 1218–1226, 2020, ISSN: 1468-2060.

Abstract | Links | BibTeX

11 entries « 2 of 4 »