Automated cell type discovery and classification through knowledge transfer

Lee, Hao-Chih; Kosoy, Roman; Becker, Christine; Dudley, Joel T.; Kidd, Brian

doi:10.1093/bioinformatics/btx054

Cited by 61 publications

(63 citation statements)

References 28 publications

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“…We compared the proportions of all cell subsets estimated by MetaCyto with the original manual gating results and found that MetaCyto estimations are highly consistent with the manual gating result (Figure 2A-C). We compared our estimations to two existing methods, flowDensity (Malek et al, 2015) and ACDC (Lee et al, 2017), which can also identify pre-defined cell populations. Our results suggest that MetaCyto’s quantification of both major and rare populations were more accurate than FlowDensity’s (Figure 2D,E).…”

Section: Resultsmentioning

confidence: 99%

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data

Jujjavarapu

Hughey

et al. 2018

Cell Reports

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While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups. Software is released to the public through Bioconductor (http://bioconductor.org/packages/release/bioc/html/MetaCyto.html).

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Section: Resultsmentioning

confidence: 99%

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data

Jujjavarapu

Hughey

et al. 2018

Cell Reports

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“…To evaluate the performance of the LDA classifier, we compared LDA with two recent state‐of‐the‐art methods for classifying CyTOF data, ACDC and DeepCyTOF . We used the AML, BMMC and PANORAMA datasets (used by ACDC) and the Multi‐Center dataset (the only available dataset used by DeepCyTOF).…”

Section: Resultsmentioning

confidence: 99%

“…For the BMMC dataset, we applied the LDA classifier to classify all 24 cell populations, resulting in ~96% accuracy and 0.85 median F1‐score. To have a fair comparison with ACDC, we also considered four populations as unknown then classified only 20 cell populations. In both cases, LDA outperformed ACDC, specially based on the median F1‐score.…”

Section: Resultsmentioning

confidence: 99%

“…Consequently, for CyTOF data, alternative gating approaches need to be considered. Recently, two methods have been developed: Automated Cell‐type Discovery and Classification (ACDC) and DeepCyTOF . ACDC integrates prior biological knowledge on markers of specific cell populations, using a cell‐type marker table in which each marker takes one of three states (1: positively expressed, −1: negatively expressed, 0: do not consider) for each cell population.…”

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confidence: 99%

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Predicting Cell Populations in Single Cell Mass Cytometry Data

et al. 2019

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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.

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“…Modern instruments including both flow and mass cytometers are now capable to quantify between 20 and 50 proteins, leading to high-dimensional data that are impossible to exhaustively explore by manual analysis. Automated cell classification has also been applied to mass cytometry by time of flight (CyTOF) data (16). A number of these have been compared in the open competition set-up by the Flow Cytometry: Critical Assessment of Population (FlowCAP) consortium (1) and many developments have followed as a result (12)(13)(14) as reviewed in Saeys et al (15).…”

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confidence: 99%

cytometree: A binary tree algorithm for automatic gating in cytometry analysis

et al. 2018

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Flow cytometry is a powerful technology that allows the high-throughput quantification of dozens of surface and intracellular proteins at the single-cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time-consuming nature. We present a new unsupervised algorithm called "cytometree" to perform automated population identification (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distributions. Node splitting is done according to a model selection procedure based on a normalized difference of Akaike information criteria between two competing models. Post-processing of the tree structure and derived populations allows us to complete the annotation of the populations. The algorithm is shown to perform better than the state-of-the-art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods project. The algorithm is also applied to a T-cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open-source available algorithm while requiring the shortest computation time. © 2018 International Society for Advancement of Cytometry

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Automated cell type discovery and classification through knowledge transfer

Cited by 61 publications

References 28 publications

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data

Predicting Cell Populations in Single Cell Mass Cytometry Data

cytometree: A binary tree algorithm for automatic gating in cytometry analysis

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