| Research Article |
Open Access |
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| FlowAnd: Comprehensive Computational Framework for Flow Cytometry
Data Analysis |
| Anna-Maria Lahesmaa-Korpinen1, Sari E. Jalkanen2, Ping Chen1, Erkka Valo1, Javier Núñez-Fontarnau1, Ville Rantanen1, Ali Oghabian1,
Jukka Vakkila2, Kimmo Porkka2, Satu Mustjoki2and Sampsa Hautaniemi1* |
| 1Research Programs Unit, Genome-Scale Biology and Institute of Biomedicine, Biochemistry and Developmental Biology, University of Helsinki, PO Box 63
(Haartmaninkatu 8), 00014 University of Helsinki, Finland |
| 2Hematology Research Unit, Biomedicum, Division of Medicine, Helsinki University Central Hospital and University of Helsinki, Finland |
| *Corresponding author: |
Dr. Sampsa Hautaniemi
Research Programs Unit
Genome-
Scale Biology and Institute of Biomedicine, Biochemistry and Developmental
Biology, University of Helsinki, Finland
E-mail: sampsa.hautaniemi@helsinki.fi |
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| Received September 22, 2011; Accepted November 03, 2011; Published November 29, 2011 |
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| Citation: Lahesmaa-Korpinen AM, Jalkanen SE, Chen P, Valo E, Núñez-
Fontarnau J, et al. (2011) FlowAnd: Comprehensive Computational Framework for
Flow Cytometry Data Analysis. J Proteomics Bioinform 4: 245-249. doi:10.4172/
jpb.1000197 |
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| Copyright: © 2011 Lahesmaa-Korpinen AM, et al. This is an open-access article
distributed under the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided
the original author and source are credited. |
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| Abstract |
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| Flow cytometry is a widely used high-throughput measurement technology in basic research and diagnostics.
Recently the amount of data generated from flow cytometry experiments has been increasing, both in sample
numbers and the number of parameters measured per cell. These highly multivariate datasets have become too
large for use with tools depending mainly on manual analysis. |
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| We have implemented a computational framework (FlowAnd) that is designed to analyze and integrate largescale,
multi-color flow cytometry data. The tool implements methods for data importing, various transformations,
several clustering algorithms for automatic clustering, visualization tools as well as straightforward statistical testing.
We applied FlowAnd to a phosphoproteomics data set from 37 chronic myeloid leukemia patients treated with two
kinase inhibitors. Our results indicate high concordance between automated gating using three clustering algorithms
and manual gating. Analysis of more than 70 flow cytometry experiments demonstrate the utility of features in
FlowAnd, such as a graphical tool for rapid validation of clustering results, in large-scale flow cytometry data analysis. |
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The FlowAnd framework allows accurate, fast and well documented analysis of multidimensional flow cytometry
experiments. It provides several clustering algorithms for automatic gating, the possibility to add novel tools in various
programming languages, such as Java, R, Python or MATLAB in an environment amenable to high-performance
computing. FlowAnd can also be easily modified to comply with various marker panels and parameter settings.
FlowAnd, all data and user guide are freely available under GNU General Public License at
http://csbi.ltdk.helsinki.fi/flowand. |
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| Introduction |
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| Flow cytometry (FCM) is a high-throughput measurement technology
that allows a large variety of cell level measurements from
counting cell populations using cell surface markers to quantification
of signaling protein levels with intracellular staining [1]. In blood cancers,
in particular acute leukemia, FCM based cell counting is routinely
used for disease diagnosis and measurement of minimal residual disease
during follow-up [2]. An FCM capable of measuring six markers,
which is a typical setting in clinical applications, can produce over 3
million data points for one patient. The need to analyze data from cohorts
of patients together with the increasing numbers of FCM markers
calls for computationally efficient tools to manage, analyze, visualize
and integrate FCM data. |
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| The analysis workflow from the raw FCM data to interpretable
results useful for clinical decision making is complex and consists of
several steps most of which are currently done manually with various
software. One of the most time consuming step in the FCM data analysis
is arguably gating, i.e., the selection of cells of interest from the data.
Software such as FlowJo (TreeStar, Ashland, OR), FCS Express (De
Novo Software, Los Angeles, CA) and Cytobank [3] aim at easy-to-use
manual gating. As manual gating is not practical in the analysis of FCM
data from tens or hundreds or patients, several methods for automatic
gating, such as SamSPECTRAL [4] and flowMeans [5], have been suggested.
Gating, however, is only one step in FCM data processing and
current frameworks that allow integrated analysis, such as FLAME
[6], FIND [7] and flowCore [8] do not scale up to analyze millions of
data points that emerge from clinical applications. Furthermore, users
typically need to copy and paste results from one software to another,
which makes the manual process error-prone and tedious. |
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| We present a computational framework (FlowAnd) for comprehensive
analysis of high-throughput FCM data from large cohorts of
patients. FlowAnd integrates several individually published flow cytometry
analysis tools to herein developed novel ones within a unified
computational framework that supports parallel programming of the
computationally demanding methods as well as statistical methods for
downstream analyses. The FlowAnd software is thoroughly documented,
actively maintained and new versions are released periodically. |
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| Materials and Methods |
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| Data |
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| In order to demonstrate FlowAnd we used data from a previously
published experiment with intracellular phosphoprotein measurements
from six-color FCM from chronic myeloid leukemia patients
[9]. To test the functionality of FlowAnd and compare the implemented
methods we used data for one patient and for a test of large-scale
capabilities we used a large data set with 37 patient samples. All data are freely available at http://csbi.ltdk.helsinki.fi/flowand. Comparison
of the performance of the automatic gating methods to a golden standard
of manually gated data was quantified using the F-score and correlation
of the identified cell populations. The F-score is a measure of
accuracy for a classification test that accounts for both precision and
recall of a test: |
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 |
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| In the multiple patient dataset there were four sample groups:
healthy controls (n=7), patients at diagnosis (n=10), patients after imatinib
treatment (n=10) and patients after dasatinib treatment (n=10).
In order to study the differences in signaling, the intracellular signaling
of the cells was induced ex vivo with various cytokines and a control
PBS-stimulation. For baseline phosphoprotein studies fixed cells were
stored without cytokine or PBS stimulation. |
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| For each patient, due to the different stimulations, four different
panels of fluorescent antibodies were used. The panels were used so
that for the control stimulation all panels were measured, and for each
of the three different cytokine cocktails two of the panels were measured,
resulting in a total of 10 experiments and data files for each patient
(Table 1 as in [9]). For the baseline study done with samples with
no stimulations, the panels in Table 2 were used. Therefore when considering
each panel in addition to the forward scatter and side scatter
measurements, there are always a total of eight parameters measured
from each cell. Each individual file typically has hundreds of thousands
of cells resulting in a data set with roughly 112 million data points,
which render manual analysis tedious. Details of experimental protocols
can be found in [9]. |
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Table 1: Panels of fluorescence antibodies used for specific stimulation conditions.
This table is adapted from [9]. |
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Table 2: Panels of fluorescence antibodies used for baseline study. |
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| Software implementation |
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| FlowAnd runs on the freely available Anduril framework [10]. Anduril
is a flexible data analysis framework that is intended for analysis
and integration of data from various data sources. The Anduril framework
is designed so that individual components in the system can be
created with different programming languages, currently covering Java,
R, Python, MATLAB and Perl. Anduril supports parallel programming
and thus computationally intensive jobs can be run in parallel. |
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| FlowAnd and user guide are freely available under GNU General
Public License at http://csbi.ltdk.helsinki.fi/flowand. The main modules
in FlowAnd are data import, preprocessing, cell gating, population identification and statistical analysis as shown in Figure 1. Each module
consists of several algorithms. For example, the data import module accepts
Flow Cytometry Standard (FCS) files. The data import functions
are mainly from the flowCore package. Preprocessing module contains
data transformation functions, such as logarithmic and arcsinh, as well
as tools to filter outlier data points that are likely debris. |
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|
Figure 1: FlowAnd overview: An overview figure of the steps in a full analysis
of flow cytometry data. First data are preprocessed by applying compensation
and a transformation to the data. Next the cells are filtered by clustering and
selecting debris clusters out from the data. The third step is to identify cell
populations from the data and this is done by first clustering the data and then
applying a rule based method to label the clusters into populations. Finally
once the correct populations have been identified, the data can be summarized
in a report. |
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| The cell gating module currently consists of three gating algorithms:
the FLAME algorithm of mixture modeling with a t-skew distribution
[6], SamSPECTRAL that combines spectral clustering with a FCM
tailored sampling procedure [4], and a variant of k-means clustering
(flowMeans) [5]. To assist with the automation of the gating procedure,
we have developed a component that allows the use of rules based on
biological knowledge. The component is given a priori rules about the
approximate locations of the cell populations as specific parameters
also enabling rules relating clusters to one another. For example, it is
known that the lymphocyte population has higher CD45 expression
than the granulocyte and monocyte populations but a low side scatter
value. Furthermore, in normal blood sample and bone marrow samples
granulocytes are known to have a large number of cells and a high side
scatter value with a high variance. This type of biological knowledge
can be translated into rules that can be used to identify the populations. |
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| These automatic tools do not always work perfectly and a user
may want to visually inspect the clustering and population labeling
manually. To allow manual intervention FlowAnd has a component
that produces a graphical interface where the user can see the results
of the clustering with the automatically identified cluster labels, manually
correct these identifications and use them in processing the final
results. The population identification can be run repeatedly for any
identified population to identify subpopulations. |
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| For final results, various statistical tests can be performed. For instance,
to compare the expression of a protein in various cell population
of two patient groups, or generate heatmaps for visualization of
the high-dimensional FCM data. |
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| Results |
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| Single patient case study |
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| We selected one patient to demonstrate a full analysis workflow and
compare performances of three gating algorithms (FLAME, flowMeans
and SamSPECTRAL) to manual gating results of three cell populations
(granulocytes, monocytes and lymphocytes). The first step of analysis
is to identify the cells from the debris, as this data contain a significant
amount of debris. This is due to the fact that the samples analyzed
were permeabilized cells which are required for enabling intracellular
staining. Debris filtering is important because clustering methods
perform much faster when only the relevant cells are used. For this
filtering, data were clustered based on the scatter channels and CDmarkers,
a total of five variables for each file (FSC, SSC, CD45, CD4,
and CD25/CD3 depending on the panel of antibodies used). The debris
was filtered out automatically by selecting the clusters that had lower median values for FSC, SSC and CD45 than the average cluster median
values, as is typically done when manually selecting the cell data from
the raw data. This filtering step reduced the data from hundreds of
thousands of cells (mean of 340,000 cells in raw data) to less than one
hundred thousand cells (mean of 70,000 cells after filtering). |
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| The smaller amount of debris-free data were used again with the
clustering algorithm to obtain more precise clustering results than
clustering with all of the raw data. The granulocyte, monocyte and
lymphocyte populations were identified from the clusters resulting
from all three methods and the results were compared to manually
gated data. Figure 2 shows a representative image of clustering results
by the different methods. With flowMeans and SamSPECTRAL
we get relatively similar clusters and the three main populations
(granulocytes, monocytes and lymphocytes) can be identified from the
clustering results. The FLAME method identified too many clusters that do not separate the three main cell populations. The distribution
of the cells is different with the three images because also cell and debris
identification is done with different clustering results, and it can be seen
that the FLAME method leaves more debris data. It is evident from
the results that FCM gating algorithms can result in widely dissimilar
results. Thus, the FlowAnd-type framework approach that includes
several methods allows the use of the best performing method in any
particular instances. |
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Figure 2: Comparison of gating algorithms: Comparison of a) manual gating to automatic gating with b) flowMeans, c) SamSPECTRAL and d) FLAME clustering.
FlowMeans and SamSPECTRAL identify the three populations relatively well, while the FLAME mixture modeling with t skew distribution identifies too many clusters. |
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| For a more detailed comparison of clustering methods we used the
F-score and correlation. The most accurate method with this data was
SamSPECTRAL with an F-score of 0.97 followed by flowMeans (F-score
0.91), whereas FLAME failed to give reasonable clusters (F-score 0.63,
Figure 2). The correlation between manual gating and SamSPECTRAL
was 0.99, whereas flowMeans and FLAME achieved 0.69 and 0.42,
respectively. flowMeans was the fastest method (1.5 hours wall clock
time using five parallel processes) followed by SamSPECTRAL (6h)
and FLAME (40h). These results demonstrate that there are differences
between the performance of gating methods, and similar results were
obtained with other patient samples (data not shown). It is thus an
advantage to be able to use several methods in parallel and choose
the best performing one. Furthermore, FlowAnd allows the use of
faster methods, such as flowMeans, for computationally demanding
clustering that do not require high accuracy and of more accurate but
slower methods, such as SamSPECTRAL, for identifying detailed cell
populations after coarse gating. |
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| Multiple patient case study |
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| To demonstrate the performance of FlowAnd in the analysis of a
large number of samples, we reanalyzed the data from 37 patient samples
with two experiments for each patient for a total of 74 FCS experiments
[9]. The aim of this study was to replicate the previous finding
of a decrease in the expression of pSTAT3 in the lymphocytes of
dasatinib-treated patients in comparison to healthy controls. For this,
the lymphocyte populations of all patients needed to be identified and
the median pSTAT3 expression of these cells calculated. |
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| We used flowMeans for clustering due to its high speed and relatively good accuracy and also included a second round of clustering in
case the automatic parametrization did not identify all of the desired
cell populations. The full analysis of the data with 10 parallel processes
took 8.5 hours (wall clock time), which included only 20 minutes
of manual work for checking the population labels of the clustering
results. Compared to manual analysis time for identification of three
populations from 74 FCM-experiments, this is a significant decrease in
time. The FLAME webservice or standalone versions were not able to
handle these data. |
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| We compared the expression of phosphoproteins obtained using
FlowAnd in comparison to manually gated lymphocytes in [9]. We
used median values of pSTAT3 expression in lymphocytes from manually
gated and computationally gated data. Both data sets were analyzed
with a Kruskall-Wallis test with a null hypothesis of equal population
medians for the four patient groups: healthy controls (n=7), patients at
diagnosis (n=10), patients after imatinib treatment (n=10) and patients
after dasatinib treatment (n=10). A p-value of less than or equal to 0.05
was considered significant. The manually gated lymphocyte data are
plotted in Figure 3A and the FlowAnd gated data in Figure 3B and both
methods gave similar results showing that there is a difference in the
expression of pSTAT3 and that the difference was between the control
and dasatinib treated individuals. Similar analyses were done with
FlowAnd for other populations and markers (data not shown). |
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|
Figure 3: Visualization of results of statistical testing: A. The results of manual gating for STAT3 in the lymphocyte populations of 37 individuals from four patient
groups, healthy controls, patients at diagnosis, patients after imatinib treatment and patients after dasatinib treatment. B. The replicated experiment using FlowAnd and
the semi-automated analysis pipeline. |
|
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| Discussion |
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| FlowAnd is designed to allow the analysis of large-scale FCM
experiments with tens to hundreds of patients and multiple FCM
experiments for each patient. Our objective was to create a framework
that is scalable, enable the use of different clustering algorithms, and
provide an environment where analysis is straightforward, repeatable,
and rapid in comparison to manual analysis. These features were
demonstrated with two case studies comprising of one and 37 leukemia
patients. When using Cytobank or FlowJo, the manual gating is a time
consuming process and for all downstream analysis, the values must
be copied from the original software to another statistical software.
FlowAnd is implemented in Anduril, which allows taking advantage
of tools for multivariate statistics, such as Weka, MATLAB and R, in a unified framework. |
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| As complexity and numbers of FCM analyses are increasing in
research and diagnostic laboratories, there is a need for computational
frameworks, such as FlowAnd, that allow accurate, fast and well
documented analysis of multidimensional FCM experiments. The
results of these case studies demonstrate that FlowAnd is able to
efficiently process large-scale FCM data as well as integrate analysis
tools into a coherent framework. FlowAnd can be easily modified to
comply with various marker panels and parameter settings. |
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| Acknowledgements |
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| Funding: Academy of Finland (projects 125826, 136181), Sigrid Jusélius
Foundation, Finnish Cancer Associations, Helsinki Biomedical Graduate School
and Biocentrum Helsinki, K.A. Johanssen Foundation and Finnish Association of
Haematology. |
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