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Comparison of Batch Effect Removal Methods for High Dimensional Mass Cytometry Data

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13347))

Abstract

Mass cytometry is one of the most popular single-cell technology that can measure over 50 markers simultaneously for millions of cells. Due to the high-dimensional nature of the dataset, manual analysis is difficult. Furthermore, during biological sample preparation, some problems may arise that need to be dealt with. One of the problems is a batch effect that can be introduced to the data because of experimental design or different experimental settings and techniques used. There are several bioinformatical solutions to correct the batch effect. Some of them require technical replicates (CytofBatchAdjust, CytoNorm and CytofRUV), others can work for a limited number of cells only (iMUBAC). An interesting aspect is how the batch correction method affects the results in terms of the number and quality of identified cell groups and to what extent the batch effect was removed. In the study, the two batch effect methods were compared, that do not require technical replicates, cyCombine and iMUBAC, applied to a real dataset with over 2 million bronchoalveolar lavage cells. Results were presented with the original mISO plots. Cells were clustered based on the original and corrected marker profiles with the PARC algorithm. After the correction, the number of clusters decreased from 24 to 22 (iMUBAC) and 18 (cyCombine). The homogeneity of clusters expressed as an effect size measure increased after the cyCombine correction (p-value = 4.38*10–7) in contrast to iMUBAC (p-value = 0.4628). The results indicate the superiority of cyCombine over iMUBAC for the real dataset if the within-cluster marker profile similarity is considered.

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Acknowledgment

AS benefits from the European Union scholarship through the European Social Fund (grant POWR.03.05.00–00-Z305). JP was financed by 02/070/BK_22/0033 project. Calculations were carried out using GeCONiI infrastructure funded by NCBiR project no. POIG.02.03.01–24-099/13.

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Authors and Affiliations

  1. Department of Data Science and Engineering, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland

    Aleksandra Suwalska & Joanna Polanska

  2. Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa

    Nelita du Plessis-Burger & Gian van der Spuy

Authors
  1. Aleksandra Suwalska
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  2. Nelita du Plessis-Burger
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  3. Gian van der Spuy
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  4. Joanna Polanska
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Corresponding authors

Correspondence to Aleksandra Suwalska or Joanna Polanska .

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Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Suwalska, A., du Plessis-Burger, N., van der Spuy, G., Polanska, J. (2022). Comparison of Batch Effect Removal Methods for High Dimensional Mass Cytometry Data. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_34

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  • DOI: https://doi.org/10.1007/978-3-031-07802-6_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07801-9

  • Online ISBN: 978-3-031-07802-6

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