Flow cytometry has been used for the last two decades to identify which immune cell subsets diapedese from the periphery into the brain parenchyma following injuries, including ischemic and hemorrhagic stroke. 2016; 16(7):449. Cell. To address the aforementioned problems, we propose a new probabilistic approach for identifying unknown cell populations associated with clinical outcomes of interest which we have named LAMBDA (Latent Allocation Model with Bayesian Data Analysis). We denote hi and lo that the marker is high and low level expressed respectively. The data generative process of LAMBDA for flow cytometry data is defined as follows: where wn,l is l-th element of wn and DL matrix is the effect of clinical information. Keywords: Machine Learning Algorithms for High Dimensional Flow Cytometry Data Nat Rev Immunol. We propose a novel statistical framework, called LAMBDA (Latent Allocation Model with Bayesian Data Analysis), for simultaneous identification of unknown cell populations and discovery of associations between these populations and clinical information. By contrast, our procedure involves a Gibbs sampling that substitutes for this requirement, significantly reducing the computational cost. Flow cytometry Immunology Immune monitoring Unsupervised clustering Myelodysplastic syndrome 1. 2018:459867. In contrast, GMM estimates often have a large bias. \\ &\left. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. The Results section includes an analysis of the efficiency of LAMBDA using synthetic and real data. The site is secure. -, Linderman GC, Rachh M, Hoskins JG et al (2019) Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. We used a multivariate normal distribution to generate the synthetic data, and values less than 0 were replaced by 0. -, Belkina AC, Ciccolella CO, Anno R et al (2019) Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Thus, we use time, dosage, anti-CD3, and anti-CD28 as the covariate X. Model-based clustering for flow and mass cytometry data with clinical Our method is a one-step procedure that directly uses cytometry data at the single cell level to simultaneously discover cell populations and to identify the associations of these populations with clinical outcomes of interest. 2017. Flow cytometry is a powerful tool that has applications in multiple disciplines such as immunology, virology, molecular biology, cancer biology and infectious disease monitoring. -, Liechti T, Roederer M (2019) OMIP-060: 30-parameter flow cytometry panel to assess T cell effector functions and regulatory T cells. The white nodes indicate latent variables and the gray nodes indicate observed variables. The .gov means its official. This article has been published as part of BMC Bioinformatics Volume 21 Supplement 13, 2020: Selected articles from the 18th Asia Pacific Bioinformatics Conference (APBC 2020): bioinformatics. Arranged by severity, clinical responses include healthy donors (ND), partial response (PR), stable disease (SD), and progressive disease (PD). Flow cytometry; Flow cytometry analysis; FlowJo; Nonlinear dimensionality reduction; UMAP; tSNE. As an alternative to manual gating, researchers have developed several computational methods, including Citrus [7], cydar [8], and diffcyt [9], to infer cell populations or states associated with an outcome variable in high-dimensional cytometry data. SWIFT: Scalable Weighted Iterative Flow-clustering Technique . government site. LAMBDA is implemented in the R environment, which is available from, Flow cytomety, Mass cytometory, Bayesian mixture model, Stochastic EM algorithm. 2018; 94(1):1218. We also applied LAMBDA to mass cytometry data from Jins study [14]. Epub 2020 Apr 15. Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration. Suppose that we observe the flow cytometry dataset ynK, (n=1,,N) and clinical information xnD. The mixture proportion is shown in Fig. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering. Motivation: For flow cytometry data, there are two common approaches to the unsupervised clustering problem: one is based on the finite mixture model and the other on spatial exploration of the histograms. 2018; 18(8):48597. official website and that any information you provide is encrypted Synthetic data was analyzed by LAMBDA along with ordinaly Gaussian mixture model (GMM) which cannot incorporate explanatory variables. Plate diagram of the data generating process in LAMBDA for flow cytometry data. However, these methods require two steps: a first step in which cell populations are identified using a clustering algorithm, and a second step in which the summary statistics of the identified cell populations are concatenated into a clinical outcome of interest which can lead to information loss and analysis bias. 1). It is known that stimulation of CD3 triggers activation of naive CD4+ T cells, which accompany the phosphorylation of SLP76/S6 and CD247 (pSLP76/pS6, pCD246) [12]. Cluster 2 is characterized by CD8+, T-bet lo, EOMES hi, PD1hi, and Ki67 lo. The recent development of high-dimensional flow cytometry and mass cytometry (CyTOF) allows for characterizing cell types and states by detecting the expression levels of pre-defined sets of surface and intracellular proteins at single cell resolution [1]. JP17ek0109281]. (Suppl 13), 393 (2020). Publication costs are funded by all of the funding. CD28 is the co-stimulatory factor that enhances and prolongs T cell activation [13]. The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Analyzing high-dimensional cytometry data using FlowSOM Background: High-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. For flow cytometry, FlowSOM clustering seems to be the best performing algorithm (11, 28). Frontiers | Meeting the Challenges of High-Dimensional Single-Cell Data It encompasses techniques to visualize, analyze and interpret the acquired. FOIA Request PDF | A Guide on Analyzing Flow Cytometry Data Using Clustering Methods and Nonlinear Dimensionality Reduction (tSNE or UMAP) | Flow cytometry has been used for the last two decades to . For the positive sample, (a) the BIC reaches a maximum . BioRxiv. 1 It can be implemented at many points in a single cell analysis workflow: prior to, after, or even instead of manual gating. Single and multi-subject clustering of flow cytometry data for cell Please enable it to take advantage of the complete set of features! Ann M. Stowe . Figure10 shows the estimated mixture proportion \(\hat \phi \). Automatic B cell lymphoma detection using flow cytometry data When associating clinical information with cytometry data, traditional approaches require two distinct steps for identification of cell populations and statistical test to determine whether the difference between two population proportions is significant. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Forward and side scatter of cells give information about the size and complexity of the cells. High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data. The first concerns the order in which pairs of markers are explored. J Cereb Blood Flow Metab 42:280291, Spidlen J, Breuer K, Rosenberg C et al. Five cases were tested: unstimulated, stimulated by only the anti-CD3 antibody, stimulated by both the anti-CD3 and anti-CD28 antibodies, and two cases with different dosages for the anti-CD3 antibody (0.3 g/mL and 0.8 g/mL). 2016 Dec;89(12):1084-1096. doi: 10.1002/cyto.a.23030. arXiv, Monaco G, Chen H, Poidinger M et al (2016) flowAI: automatic and interactive anomaly discerning tools for flow cytometry data. The authors declare that they have no competing interests. 6) determined the setting of 13 clusters. We observed that the value of the mixture proportion for cluster 2 increases as cancer progresses from PR to PD. Regulatory mechanisms in T cell receptor signalling. 1). Introduction There are many occasions where it is important to examine the composition of immune cells in our body. Figure8 shows that as activated T cells decrease, the memory T cell population increases, indicating the transformation of naive T cells to memory T cells. Therefore, lack of consideration for this distribution difference in the existing methods masks the underlying difference in cell populations and gives rise to a misleading conclusion in both basic and clinical research. Based on the cytometry measurements, cell sorters isolate one or more cell . In the case of flow cytometry, LAMBDA assumes that the data is generated from a mixture of multivariate normal distributions, each of which represents an unknown cell population. Recently, with the development of next generation sequencing technologies, single cell sequencing was introduced to the field of biomedical research. BioRxiv. 15H05912, 18H04798, and 19H05210]. Flow cytometry is a technique for measuring physical, chemical and biological characteristics of individual microscopic particles such as cells and chromosomes. The error bars indicates SE. 2018:459867. Because of this novel feature, we expect that LAMBDA will be efficiently applied to studies that seek an association between cell populations and clinical information, advancing our ability to predict disease and predict outcomes of treatment. For identifiability, the first column of is always set as zero. Department of Neurology, Department of Neuroscience, The University of Kentucky, Lexington, KY, USA, FlowJo, BD Life SciencesBiosciences, Ashland, OR, USA, You can also search for this author in Flow Cytometry Gating and Clustering - GenePattern Rawstron AC, Kreuzer KA, Soosapilla A, Spacek M, Stehlikova O, Gambell P, Milani R, et al.Reproducible diagnosis of chronic lymphocytic leukemia by flow cytometry: An European Research Initiative on CLL (ERIC) & European Society for Clinical Cell Analysis (ESCCA) Harmonisation project. Nat Commun. Our method is based on correctly specified probabilistic models that are designed for modeling the different distribution information of flow and mass cytometry data respectively. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The https:// ensures that you are connecting to the On the other hand, in flow cytometry, the boundary between on and off is more ambiguous, which leads to a bimodal Gaussian distribution (Fig. Synthetic data was analyzed by LAMBDA along with ordinaly Gaussian mixture model (GMM) which cannot incorporate explanatory variables. Gaud G, Lesourne R, Love PE. The dataset includes N cells, K markers and D-dimensional clinical information. We provide a simple and efficient learning procedure for the proposed model using a stochastic EM algorithm that reduces computational cost. If the latent variable wn,l is given, the complete likelihood of this model is represented by the following formula: where \(\mathcal {N}(\boldsymbol {y} | \boldsymbol {\mu },\boldsymbol {\Sigma })\) is the density function of the multivariate Gaussian distribution with mean and covariance . To meet this challenge, we proposed a statistical framework that uses flow and mass cytometry data to discover cell clusters and the associations between individual clusters and clinical information. The BIC is defined as follows: where is the likelihood and f is the number of estimated parameters. Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP. Clustering was performed on the cells preselected from the first stage. Saeys Y, Van Gassen S, Lambrecht BN. The contributions of our proposed method are summarized as follows: Our method is a one-step procedure that directly uses cytometry data at the single cell level to simultaneously discover cell populations and to identify the associations of these populations with clinical outcomes of interest. Pauken & Wherry [15] reported that the CD8+ T cells of T-bet hi and EOMES lo become T-bet lo, EOMES hi, and PD1 hi through exhaustion. In the M-step, we update the parameters using: Because closed form solutions for are unavailable, we use Newtons method to obtain estimates. Simulation result of . A Guide on Analyzing Flow Cytometry Data Using Clustering Methods and The authors declare that they have no competing interests. On the other hand, in the case of mass cytometry, LAMBDA assumes that the data is generated from a mixture of zero-inflated distributions that represent censoring of expression below a substantial limit of detection. Cookies policy. In the case of mass cytometry data, for determination of the number of clusters, we used the elbow method, which is performed by plotting the SSE within each cluster against the number of clusters. IMFC SR is a 24 X 7 Flow cytometry instrumentation facility providing multiparametric spectral (up to 34 colors) and conventional flow cytometry (14 colors) assay design and hi-dimensional data analysis tools (Astrolabe, tSNE, UMAP, TriMAP, Flow-SOM, etc.). National Library of Medicine All variables are treated as dummy variables. \end{array} $$, $$\begin{array}{*{20}l} \tilde{ \boldsymbol{w}}_{n} \sim \text{Categorical} (\boldsymbol{\eta}_{n}) \end{array} $$, $$\begin{array}{*{20}l} \eta_{nl} = \frac{ \phi_{n,l} \mathcal{N}(\tilde{\boldsymbol{z}}_{n} | \boldsymbol{\mu}_{l},\boldsymbol{\Sigma}_{l}) }{{\sum\nolimits}_{l=1}^{L}\phi_{n,l} \mathcal{N}(\tilde{\boldsymbol{z}}_{n} | \boldsymbol{\mu}_{l},\boldsymbol{\Sigma}_{l}) }. This is a preview of subscription content, access via your institution. However, the increase in number of parameters and complexity in experiments is leading to the use of newer cluster data analysis algorithms such a PCA, SPADE and . Through this analysis we can see that LAMBDA is a method that can efficiently estimate various clusters within cell populations and identify the associations between these cell clusters and their clinical outcomes in cases of both flow and mass cytometry data. A Guide on Analyzing Flow Cytometry Data Using Clustering - PubMed It is known that stimulation of CD3 triggers activation of naive CD4+ T cells, which accompany the phosphorylation of SLP76/S6 and CD247 (pSLP76/pS6, pCD246) [12]. By using this website, you agree to our the contents by NLM or the National Institutes of Health. Model-based clustering for flow and mass cytometry data with clinical information, \(\boldsymbol {y}_{n} \in \mathbb {R}^{K}\), \(\boldsymbol {x}_{n} \in \mathbb {R}^{D}\), $$\begin{array}{*{20}l} \boldsymbol{y}_{n}|w_{n} &\sim \prod_{l=1}^{L} \text{Gaussian}\left(\boldsymbol{\mu_{l}}, \Sigma_{l}\right)^{w_{n,l}} \\ \boldsymbol{w}_{n} | \boldsymbol{x}_{n} & \sim \text{Categorical}(\boldsymbol{\phi}_{n}) \end{array} $$, $$\begin{array}{*{20}l} \boldsymbol{\phi}_{n} &= \text{softmax}(\boldsymbol{x}_{n}\boldsymbol{\beta})\\[-3pt] \boldsymbol{\mu}_{l} &\sim \text{Gaussian} \left(\boldsymbol{0},\frac{1}{\tau} \boldsymbol{\Sigma}_{l} \right) \\[-3pt] \boldsymbol{\Sigma}_{l}^{-1} &\sim \text{Wishart} \left(\nu,\boldsymbol{\Lambda} \right) \end{array} $$, \(\text {softmax}(\boldsymbol {x})=\frac {\exp (\boldsymbol {x})}{{\sum \nolimits }_{k=1}^{K}{\exp (x_{k})}}\), $$\begin{array}{*{20}l} L^{(c)} = \prod_{n=1}^{N} \prod_{l=1}^{L} \phi_{n,l}^{w_{n},l} \mathcal{N} (\boldsymbol{y}_{n} | \boldsymbol{\mu}_{l},\boldsymbol{\Sigma}_{l})^{w_{n},l}. Nat Methods 16:243245 The and were randomly generated. The error bars indicates SE. The data generative process of LAMBDA for mass cytometry data is defined as follows: Figure3 shows a plate diagram of this data generating process. We also applied LAMBDA to mass cytometry data from Jins study [14]. Bookshelf The marker KI67 indicate cell mass culturing. With the development of high dimensional flow and mass cytometry data, researches have been challenged with the need to properly identify and interpret data about cell populations. proteins). Development, application and computational analysis of high - Nature J R Stat Soc Ser B Stat Methodol. x-axis corresponds to the markers, and the y-axis corresponds to the clusters. The dataset includes N cells, K markers and D-dimensional clinical information. The and were randomly generated. Stephens M. Dealing with label switching in mixture models. Frontiers | How to Prepare Spectral Flow Cytometry Datasets for High Ferrer-Font L, Mayer JU, Old S, Hermans IF, Irish J, Price KM. SWIFT: Scalable Weighted Iterative Flow-clustering Technique Therefore, lack of consideration for this distribution difference in the existing methods masks the underlying difference in cell populations and gives rise to a misleading conclusion in both basic and clinical research. Google Scholar. However, LAMBDA can include any explanatory variables. 8. We estimated the parameters from 100 replicates of the experiment. Sci (N Y). FOIA Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus. The exhausted T cells have a low expression level in KI67. In the case of mass cytometry data, for determination of the number of clusters, we used the elbow method, which is performed by plotting the SSE within each cluster against the number of clusters. Abe, K., Minoura, K., Maeda, Y. et al. The bias of \(\hat \theta \) is defined by the difference between the true value and the estimated value \((E[\hat {\theta }]-\theta)\). This high-dimensional cytometry data contains useful information to diagnose diseases such as leukemia [3] and HIV [4], as well as to predict clinical outcomes such as the response to cancer immune-therapies [5]. It has many applications in molecular and cell biology for both clinical diagnosis and research purposes [ 1 ]. Flow cytometry is a lab test used to analyze characteristics of cells or particles. LAMBDA is implemented with R and is available from GitHub (https://github.com/abikoushi/LAMBDA). The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. The comparison true and the mean of estimates \(\boldsymbol {\hat \Sigma }\). Part of Springer Nature. Traditional analysis is done by manual gating which suffers not only from the need to detect unknown cell populations, but also from the need to ensure reproducibility [6]. Dimensionality reduction techniques have been pivotal. Gaud G, Lesourne R, Love PE. Left panel show the result of GMM, right panel show the result of LAMBDA, Simulation result of . A key challenge in the analysis of high-dimensional cytometry data is to identify unknown cell populations that relate as prognostic factors to clinical outcomes of interest. The x-axis corresponds to the markers, and the y-axis corresponds to the clusters. Figure7 shows the \(\boldsymbol {\hat \mu }\). Also as shown in Fig. Comparison of biaxial plots with machine learning analysis of a 20-color panel cytometry data. 8600 Rockville Pike Flow cytometry is a high-throughput, laser-based technology to study cellular heterogeneous populations [ 1 ]. The mean and SE for the estimated \(\hat \phi \) is shown in Table1. In the M-step, we update the parameters using: Because closed form solutions for are unavailable, we use Newtons method to obtain estimates. Quintelier K, Couckuyt A, Emmaneel A, Aerts J, Saeys Y, Van Gassen S. Nat Protoc. The section will describe pre-processing and outline the steps needed to perform unsupervised clustering of your data set in addition to using nonlinear dimensionality reduction for visualizing your analysis. The Methods section details the proposed model and algorithm. This finding is consistent with Pauken and Blackburns study, showing the effectiveness of LAMBDA in interpreting high dimensional mass cytometry data in real situations. Google Scholar. LAMBDA should prove useful as it is described in this paper, but there is room for future study and improvement. \end{array} $$, https://doi.org/10.1186/s12859-020-03671-7, Selected articles from the 18th Asia Pacific Bioinformatics Conference (APBC 2020): bioinformatics, https://community.cytobank.org/cytobank/experiments/35226, https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-21-supplement-13, http://creativecommons.org/licenses/by/4.0/, http://creativecommons.org/publicdomain/zero/1.0/. Fahey MT, Thane CW, Bramwell GD, Coward WA. where (y|,) denotes the distribution function of a univariate Gaussian distribution with mean and variance and xi is the set of all variables in x except for the i-th variable. For the case of flow cytometry we turn to Landrigans study (https://community.cytobank.org/cytobank/experiments/35226), in which naive CD4+ T cells were purified and stimulated by anti-CD3 and anti-CD28 antibodies. An official website of the United States government. LAMBDA shows that the cluster 2 cell population is high in patients who underwent a PDL1 inhibitor treatment with a poor prognosis. LAMBDA estimates composition ratio of clusters depends on the clinical information. BMC Bioinformatics 21 Blackburn SD, Shin H, Freeman GJ, Wherry EJ. It tends to give more . The comparison true and the mean of estimates ^. Automatic Clustering of Flow Cytometry Data with Density-Based Merging 1). Jin G, Xue G, Wang RS, Wu LY, Lance M, Lu Y, Zhang W. Single-Cell Modeling of CD8+ T Cell Exhaustion Predicts Response to Cancer Immunotherapy. When estimating parameters, we set =0.01,=K+2, and is an identity matrix, which is equivalent to a weakly-informative prior distribution. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Weber LM, Nowicka M, Soneson C, Robinson MD. Nat Methods 16:243245, McInnes L, Healy J, Melville J (2018) UMAP: uniform manifold approximation and projection for dimension reduction. Figure7 shows the ^. The white nodes indicate latent variables and the gray nodes indicate observed variables. This order often allows some degree of freedom in the gating process, so it might lead to the selection of different cells by alternative gating strategies. bioRxiv. FlowSOM is a powerful clustering algorithm that builds self-organizing maps to provide an overview of marker expression on all cells and reveal cell subsets that could be overlooked with manual gating. When associating clinical information with cytometry data, traditional approaches require two distinct steps for identification of cell populations and statistical test to determine whether the difference between two population proportions is significant. We propose a novel statistical framework, called LAMBDA (Latent Allocation Model with Bayesian Data Analysis), for simultaneous identification of unknown cell populations and discovery of associations between these populations and clinical information. The mean and SE for the estimated ^ and ^ are shown in Figs. Here, LAMBDA steps in to properly assess the data. Teppei Shimamura, Email: pj.ca.u-ayogan.dem@arumamihs. This shows that in the case of flow cytometry data the method is able to provide a reasonable interpretation of the cell population clusters. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature. Regulatory mechanisms in T cell receptor signalling. Reproducible diagnosis of chronic lymphocytic leukemia by flow cytometry: An European Research Initiative on CLL (ERIC) & European Society for Clinical Cell Analysis (ESCCA) Harmonisation project. Throughout the comparisons, we use manually gated cell population labels as the reference populations, or "ground truth," against which the clustering algorithms are . They found that these programs are not accurate enough and too slow for routine use. Analyzing high-dimensional cytometry data using FlowSOM. PMC Mass cytometry data is marked by a zero-inflated distribution (Fig. bioRxiv. 2016; 23(4):25471. LAMBDA should prove useful as it is described in this paper, but there is room for future study and improvement. Thus, we use time, dosage, anti-CD3, and anti-CD28 as the covariate X. Our method is based on correctly specified probabilistic models that are designed for modeling the different distribution information of flow and mass cytometry data respectively. Recent developments have moved the analysis of high-parameter flow cytometry data sets fro 2017. This property allows LAMBDA to analyze experimental results with various settings. In addition to being computationally efficient, our framework also has useful properties from the perspective of data analysis. KM, YM and HN designed the experiments. Unauthorized use of these marks is strictly prohibited. LAMBDA is implemented with R and is available from GitHub (https://github.com/abikoushi/LAMBDA). All authors have read and approved the final manuscript. Selective expansion of a subset of exhausted CD8 T cells by PD-L1 blockade. A variation of flow cytometry, known as cytometry by time-of-flight (CyTOF) or mass cytometry, was developed in 2009, which could query over 50 parameters per cell, in contrast to only limited parameters in conventional flow cytometry. In this case, the elbow method determined 14 clusters (Fig. Flow Cytometry vs Cell Sorting - Biocompare The BIC is defined as follows: where \(\mathcal {L}\) is the likelihood and f is the number of estimated parameters. In the case of synthetic data, the algorithm of LAMBDA uses parameters estimated with small biases and is able to produce reasonable estimates. FCM is widely used in medical diagnostics and health research. Frontiers | Flow Cytometric Analyses of Lymphocyte Markers in Immune High-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. As described in the Methods section, this model uses a stochastic EM to estimate parameters. Springer Nature is developing a new tool to find and evaluate Protocols. Here, the softmax function is defined by \(\text {softmax}(\boldsymbol {x})=\frac {\exp (\boldsymbol {x})}{{\sum \nolimits }_{k=1}^{K}{\exp (x_{k})}}\) for vector x=(x1,,xK) using an element-wise exponential function.
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