Dataset Information

CyTOFmerge: integrating mass cytometry data across multiple panels.

ABSTRACT:

Motivation

High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel.

Results

To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.

Availability and implementation

Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge).

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Abdelaal T

PROVIDER: S-EPMC6792069 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

CyTOFmerge: integrating mass cytometry data across multiple panels.

Abdelaal Tamim T Höllt Thomas T van Unen Vincent V Lelieveldt Boudewijn P F BPF Koning Frits F Reinders Marcel J T MJT Mahfouz Ahmed A

Bioinformatics (Oxford, England) 20191001 20

<h4>Motivation</h4>High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel.<h4>Results</h4>To extend the number of markers per cell, we propose an in silico method to integrate C ...[more]

PMID: 30874801

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Project description:Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Dataset Information

CyTOFmerge: integrating mass cytometry data across multiple panels.

Motivation

Results

Availability and implementation

Supplementary information

Publications

CyTOFmerge: integrating mass cytometry data across multiple panels.

Similar Datasets

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