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MACHINE LEARNING-BASED GATING AUTOMATION
WHO ARE WE?
Working on different kinds of data: • Omics (genomics, transcriptomics, proteomics…) • Cytometry • Clinical/Medical
CRO in data analysis, founded in 2006 with a focus on: • Life Science Data analysis/mining in immunology, oncology, dermo-
cosmetology,… • Biological interpretation of results • Development of dedicated databases and proprietary tools for data
processing, analysis and interpretation
Transcriptomic (DNA chip, RNA-Seq)
Proteomic (LC-MS/MS)
Genomic/Epigenomic
(DNA chip, NGS)
Flow/Mass Cytometry
With the aim of: • Caracterizing new models, the effect of treatments at different doses • Identifying molecular mecanisms/markers of pathology • Following biological ways of interest (oxydative stress, immune response,…) • Studying the microbiote • Immunophenotyping
THE ALTRABIO’S WORKFLOW
Implementation of biostatistical and biomathematical methods
Analysis methods & results/Publication quality images/Oral presentation of the report
Comprehensive report
State-of-the-art methods/Customized statistical models/Classifier analyses
Generated DataFrom your own providers or from our partners
What we deliverWhat we need What we do
Biological question
Gating AutomataAvailables on a secured Web interface for raw data
deposit and results visualization and exploration
Gating automationCell-population-guided approachPatient-output-guided approach
Unbiased approaches
THE ALTRABIO’S TOOLBOX FOR CYTOMETRY DATA➡ Data preprocessing: « Transform raw data for better
identification of cell populations » ‣ Compensation ‣ Transformation (Logarithmic transformations; log-linear hybrid
transformations: logicle, Hyperlog, hyperbolic arcsine, biexponential, etc.) ‣ Normalization (Reduction of technical variance: statistical normalization,
peak alignment & registration methods, bead based normalisation, etc.) ‣ Debarcoding & « Pre-Gating » (Debarcoding, Beads Identification, Margin
Events / Doublets removal, Live Cells identification / DNA gating etc.)
➡ Data quality control & Visualization: « Remove artifacts & poor quality data » ‣ Detection of inconsistencies among individual samples (technical errors,
labelling errors, batch effect, sample effect, etc.): ‣ Summary statistics, statistical tests, outliers detection methods, probability
binning, fingerprinting, etc. ‣ Visualization to get insights ‣ Principal Component Analysis, t-SNE, UMAP, Minimum Spanning Tree
layouts, Multi Dimensional Scaling, etc.
➡ Automated unsupervised population identification: « Automatically identify cell populations in an unsupervised way » ‣ Clustering approaches (Proprietary approaches, topological/graph-based
approaches (e.g. SamSPECTRAL), density-based approaches (e.g. Flock), model-based approaches (e.g. immunoClust, FLAME, FlowClust, flowMerge), hybrid approaches (e.g. FlowSOM, Phenograph, FlowPeaks, FlowMeans, etc.), ensemble approaches, etc.)
‣ Gating by dimension reduction (Principal Component Analysis, Minimum Spanning Tree layouts (e.g. SPADE), Multi Dimensional Scaling, t-stochastic neighbor embeddings (e.g. ViSNE), UMAP etc.)
➡ Automated supervised population identification: « Automatically identify cell populations in a (partially) supervised way » —> Cell-populations-guided solution ‣ Partially supervised: Flow Density ‣ Supervised: Gating Automata (proprietary)
➡ Automated semi-supervised population identification: « Automatically identify cell populations while guiding the identification to find a clustering that fits the objective of the study thanks to knowledge embedding » —> Patient-output-guided solution
➡ Cross sample analysis: ‣ Statistical modeling
generalized linear models, mixed models (etc) for (1) differential analysis of abundance of cell populations or (2) differential analysis of marker expression stratified by cell population.
‣ Machine learning Supervised learning (e.g., Random Forest, Boosting, SVM, (sparse) PLS), correlation identification, etc.
‣ Specific task-dedicated algorithms CITRUS, RchyOptimyx, etc.
WHY AUTOMATE YOUR GATING STRATEGY? But, the gating is usually performed manually which:
• is highly time consuming and tedious
• requires experience/knowledge • is subject to errors and subjectivity,
reproducibility issuesGating is a fundamental step for data analysis
such as immunophenotyping
NEED FOR AUTOMATION
Data Generation Gating Cross-samples
analyses
Cells characterization
Cells populations
Samples - Individuals
TWO APPROACHES FOR AUTOMATED GATING DEPENDING ON NEEDS1. To identify discriminative cell populations without any (few) a priori
2. To identify predefined cell populations
Applications - Mainly for research projects: • Predictive modeling (e.g., definition of cell populations to
diagnose a disease and/or its evolution) • Discovery (e.g., identification & description of responding
populations for a therapy, identification of therapeutic targets)
Applications - Mainly for development & diagnostic projects: • Reproduce & standardize clinical trial data processing • Diagnostic, Minimal Residual Disease monitoring, …
ALTRABIO’S CELL-
POPULATIONS-GUIDED
SOLUTION
ALTRABIO’S
PATIENT-OUTPUT-GUIDED
SOLUTION
ALTRABIO’S PATIENT-OUTPUT-GUIDED SOLUTION
WORKFLOWS FOR AUTOMATED CYTOMETRY BASED BIOMARKERS IDENTIFICATION
Pros: Gating is performed automatically which:
• reduces time & cost (may render this task feasible) • no (less) experience/knowledge required • avoid errors and subjectivity, reproducibility issues
Cons: Identification do not take into account all previously accumulated knowledge: only based on data (=>risk of drowning into data) Understanding and Interpretation of Results are difficult
Automated Samples characterization without embedded knowledge
(Unbiased clustering)
Cytometry Data
Identification of relevant cellular populations
(BioMarkers)Predictive Modelling
Statistical Modeling / Machine Learning
Use of clustering algorithms to perform automated unbiased population identification
=> gating (done automatically) no use of Knowledge
classical automated ML based approach
based on Statistical Analyses
Pros: Gating is performed automatically which:
• reduces time & cost (may render this task feasible) • no (less) experience/knowledge required • avoid errors and subjectivity, reproducibility issues
Identification based on both data and all previously accumulated knowledge Understanding and Interpretation of Results are easier
WORKFLOWS FOR AUTOMATED CYTOMETRY BASED BIOMARKERS IDENTIFICATIONAutomated Samples characterization
without embedded knowledge (Unbiased clustering)
Cytometry Data
Identification of relevant cellular populations
(BioMarkers)Predictive Modelling
Statistical Modeling / Machine Learning
Automated Samples characterization with embedded knowledge (OUTPUT guided solution)
Use of a proprietary clustering algorithm to perform automated output
guided population identification
=> gating (done automatically) embedded Knowledge
ALTRABIO’s automated ML based approach based on Statistical Analyses
Use of clustering algorithms to perform automated unbiased population identification
=> gating (done automatically) no use of Knowledge
classical automated ML based approach
WHY?▸ Enhancement of the results ▸ « Automatic identification of the resolution » of the clustering ▸ Enhancement of the results adequacy with the problem to address
WHAT?▸ Tool to perform unbiased clustering while possibly « guiding » it (i.e. « guiding »
the kind of knowledge it should extract)
WHEN?▸ In any case ▸ Specially interesting when you need to describe samples in order to perform
comparisons/cross samples analyses
HOW?▸ Own clustering approach based on density
▸ Clusterizes samples independently (allows to take into account samples specificities like marker shift, populations differences in abundance, etc.) + meta clustering
▸ Adopting a clustering approach with low assumptions on shapes, sizes of cellular populations, as well as imbalance between cellular populations
▸ Automatic identification of the « good resolution » for clustering ▸ Possible knowledge embedding in order to adopt a point of view on data
(clusterizing while taking into account that the extracted structure should help in adressing a given problem)
RESULT VISUALIZATIONSMulti Dimensional Scaling (MDS)
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Various t-SNE plots
HeatMap + Hier. Clustering
Sample Stratification Condition Stratification Cluster Stratification
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e
decisionConfirmed
Rejected
Synthetic
Undetermined
Cluster Importance for 1
●● ●
● ● ●
●
●● ●
●
●
●
●
●
●
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●
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●
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●
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0 10 20 30 40
0.0
0.2
0.4
0.6
0.8
1.0
Classification for 1
sample index
RF
votin
g
●●●
●
●●● ● ●●●
● ●● ●
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●
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●
● ●●
●
● ●●
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Wilcoxon, p = 8.1e−08
0
10
20
30
40
FALSE TRUEType
Clu
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Type●
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FALSE
TRUE
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Wilcoxon, p = 0.0045
0
10
20
30
40
FALSE TRUEType
Clu
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Type●
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FALSE
TRUE
●
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Wilcoxon, p = 0.05
0.00
0.05
0.10
0.15
FALSE TRUEType
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Type●
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FALSE
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●
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●●●● ●●● ● ● ●● ●●● ●● ●● ●●● ●● ● ●
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●
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●●●
● ●● ●● ●●● ●● ●●
●
●
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Wilcoxon, p = 0.095
0
20
40
60
FALSE TRUEType
Clu
ster
Type●
●
FALSE
TRUE
●
●
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0
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20
shad
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shad
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1X1
3X1
7 X2 X16
X18 X4
variable
valu
e
decisionConfirmed
Rejected
Synthetic
Undetermined
Cluster Importance for 2
● ● ● ● ● ●
●
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0 10 20 30 40
0.0
0.2
0.4
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0.8
1.0
Classification for 2
sample index
RF
votin
g
●●●
●
●●● ● ●●●
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Wilcoxon, p = 8.1e−08
0
10
20
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FALSE TRUEType
Clu
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FALSE
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Wilcoxon, p = 0.0045
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FALSE TRUEType
Clu
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Type●
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FALSE
TRUE
●
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Wilcoxon, p = 0.05
0.00
0.05
0.10
0.15
FALSE TRUEType
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Type●
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●
●
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● ●●● ● ●●● ●● ●●
●
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Wilcoxon, p = 0.095
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20
40
60
FALSE TRUEType
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X17 X2 X16
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variable
valu
e
decisionConfirmed
Rejected
Synthetic
Undetermined
Cluster Importance for 3
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Classification for 3
sample index
RF
votin
g
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Wilcoxon, p = 8.1e−08
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FALSE TRUEType
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Wilcoxon, p = 0.0045
0
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Wilcoxon, p = 0.05
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0.05
0.10
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FALSE TRUEType
Clu
ster
Type●
●
FALSE
TRUE
ENHANCED RESULTS (CASE STUDY)
« ReAnalysis » of a dataset (AltraBio & a French hospital) • Residual disease context • Known « disease cellular population » • Might be a rare or a frequent population
(~1% to ~50% of a given subpopulation)
• Complex targeted phenotype based on a combination of 7 markers
• Goals: • Build model to classify samples • Automatically identify the « disease » population
• Results: • Identification of a predictive cell cluster with a
prediction 100% accuracy
Perfect prediction 100% accuracy by cross validation (only 75 % accuracy with CITRUS)
The most relevant automatically identified stratifying clusters is highly discriminative
REAL \ PREDICTED MRD - MRD +
MRD - 28 0MRD + 0 15
MRD -MRD + MRD +
MRD -
MRD +MRD -
CELL-POPULATIONS-GUIDED SOLUTION
WHY?▸ Reduce time/cost ▸ Address scalability/exhaustivity issues ▸ Address errors/subjectivity/
reproducibility issues ▸ « Real time » results delivery ▸ Concentrate needed experience/
knowledge
WHAT?▸ Tool to automate manual gating tasks
WHEN?▸ Large number of samples to process ▸ Need to accelerate processing time ▸ Clinical trials/immuno-monitoring ▸ Increased reproducibility needed ▸ On line (« real time ») processing &
reporting needed ▸ Embedding gating process into another
(medical) device
HOW?▸ Learns and applies your gating strategy (exactly mimics
what you would do) ▸ Uses the latest Machine Learning approaches with
emphasis on: - Handling variability - Addressing issues related to strongly imbalanced sizes of
cellular populations and rare populations
‣ Can embed additional knowledge (e.g. use of FMO, controls, copy of gates)
‣ Validated performances on numerous studies
FCS files
Train automaton
FCS Files + auto
gating
Learning data generation FCS
Files +manual gating
FCS Files
FCS File + auto gating
Batch processing Single file processing
Trained automaton
Learning stage
Application stage
1-4
wee
k(s)
5-10
min
/file
FCS File
THE CYTAUTOMATON WORKFLOW
What we deliverWhat we need What we do
Build a dedicated gating automaton
Upload your data and access to customisable population and outlier reports through a secure
web interface
Deploy and apply to new FCS files
AltraBio’s Tool Box & Experts
Iterative 3-step workflow: Build / Evaluate / Validate
Learning DataA few representative manually gated FCS files +
Complementary Information (e.g. embedded assumptions, controls, FMO) to transfer human expertise to the computer
LEARNING DATA
▸ Acquisition of human operator’s knowledge/experience with few manually gated examples
▸ Need representative examples
▸ Complementary information: details on how manual gating was done (e.g. embedded assumptions, controls, FMO)
▸ Collection made easier thanks to interfaces with FlowJO / Kaluza / DIVA
▸ Iterative 3-step workflow: Build / Evaluate / Validate
▸ A loop to enhance performance of the learning if required
▸ Validate the learning performance before its deployment
▸ Go/No Go decision to deploy the Gating Automaton based on an objective predefined criterion
▸ For example, tests can allow to check that: • the GA handles variability arising from instrument
calibration (since GAs learnt on data coming from different days of acquisition were applied on data acquired another day)
• the GA is able to handle natural biological variations (the series were including different donors and different sample stimulations)
get some more learning data
get learning data
build Automaton
a priori evaluation of learning
performance
Validated Gating Automaton for deployment
learning performance
validation
No
Yes
enhance
BUILD A DEDICATED GATING AUTOMATON - THE DESIGN WORKFLOW
DEPLOY AND APPLY TO NEW FCS FILES▸ Batch or single FCS file processing
▸ Web interface to limit the exchange of files
▸ Deliverables: • Customised « per file » reports and tables
(e.g. #events, % total, % parent, MFIs)
• Customised « per study » reports and tables (e.g. Files (samples) comparison, outliers detection)
• Optionally: - Automation of additional biological and technical quality controls - Automation of cross-samples analyses
CONFIDENTIAL 19 of 54
Panel_1_32160214_DRFZ_CANTO2_21APR2016_21APR2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32160463_CHP_NAVIOS_12SEP2016_12SEP2016.LMD_intra.fcs_comp.fcs / #mfi2_CD14+_MONOCYTES_FSC-A
Panel_1_32170435_CHP_NAVIOS_18JUL2017_18JUL2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32151817_UBO_NAVIOS_22MAR2016_22MAR2016.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-A
Panel_1_32151953_IRCCS_CANTOII_27JUN2016_27JUN2016.fcs_intra.fcs_comp.fcs / #mfi2_CD14+_MONOCYTES_FSC-APanel_1_32160221_DRFZ_CANTO2_11MAY2016_11MAY2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32170454_CHP_NAVIOS_07AUG2017_07AUG2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32170449_CHP_NAVIOS_24JUL2017_24JUL2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-A
Panel_1_32160219_DRFZ_CANTO2_22APR2016_22APR2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32160215_DRFZ_CANTO2_24MAY2016_24MAY2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32160552_DRFZ_CANTO2_01NOV2016_01NOV2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32160548_DRFZ_CANTO2_16SEP2016_16SEP2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32152235_DRFZ_CANTO2_12SEP2016_12SEP2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32152237_DRFZ_CANTO2_05SEP2016_05SEP2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32160551_DRFZ_CANTO2_21OCT2016_21OCT2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32170080_CHP_NAVIOS_06JUL2017_06JUL2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32170451_CHP_NAVIOS_14SEP2017_14SEP2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-A
Panel_1_32160106_UCL_CANTO2_16JUN2016_16JUN2016.fcs_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32160546_DRFZ_CANTO2_27SEP2016_27SEP2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32161029_DRFZ_CANTO2_07MAR2017_07MAR2017.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32151817_UBO_NAVIOS_22MAR2016_22MAR2016.LMD_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32170039_UCL_CANTO2_25SEP2017_25SEP2017.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32160087_UCL_CANTO2_15DEC2016_15DEC2016.fcs_intra.fcs_comp.fcs / #mfi2_PMN_SSC-A
Panel_1_32152084_UCL_CANTO2_19JAN2017_19JAN2017.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32161023_DRFZ_CANTO2_25JAN2017_25JAN2017.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32170632_CHP_NAVIOS_09OCT2017_09OCT2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32161022_DRFZ_CANTO2_24JAN2017_24JAN2017.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32170071_CHP_NAVIOS_10JUL2017_10JUL2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32152238_DRFZ_CANTO2_13SEP2016_13SEP2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32151213_UCL_CANTO2_15JAN2016_15JAN2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32151954_IRCCS_CANTOII_19JAN2016_19JAN2016.fcs_intra.fcs_comp.fcs / #mfi2_CD14+_MONOCYTES_FSC-A
Panel_1_32152234_DRFZ_CANTO2_19JUL2016_19JUL2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32161019_DRFZ_CANTO2_16JAN2017_16JAN2017.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32152089_UCL_CANTO2_01DEC2016_01DEC2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32150934_IRCCS_CANTOII_13OCT2015_13OCT2015.fcs_intra.fcs_comp.fcs / #mfi2_CD14+_MONOCYTES_FSC-APanel_1_32170455_CHP_NAVIOS_16AUG2017_16AUG2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-A
Panel_1_32170439_CHP_NAVIOS_19JUL2017_19JUL2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32150933_IRCCS_CANTOII_10SEP2015_10SEP2015.fcs_intra.fcs_comp.fcs / #mfi2_CD14+_MONOCYTES_FSC-A
Panel_1_32160683_UBO_NAVIOS_23SEP2016_23SEP2016.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32151984_UBO_NAVIOS_01MAR2016_01MAR2016.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-A
Panel_1_32152089_UCL_CANTO2_01DEC2016_01DEC2016.fcs_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32151952_IRCCS_CANTOII_20APR2016_20APR2016.fcs_intra.fcs_comp.fcs / #mfi2_CD14+_MONOCYTES_FSC-A
Panel_1_32170441_CHP_NAVIOS_09AUG2017_09AUG2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32160683_UBO_NAVIOS_23SEP2016_23SEP2016.LMD_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-A
Panel_1_32161415_UCL_CANTO2_23FEB2017_23FEB2017.fcs_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32170614_CHP_NAVIOS_10OCT2017_10OCT2017.LMD_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-A
Panel_1_32151466_UCL_CANTO2_29JAN2016_29JAN2016.fcs_intra.fcs_comp.fcs / #mfi2_LYMPHOCYTES_SSC-APanel_1_32151466_UCL_CANTO2_29JAN2016_29JAN2016.fcs_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-APanel_1_32152084_UCL_CANTO2_19JAN2017_19JAN2017.fcs_intra.fcs_comp.fcs / #mfi2_PBMC_SSC-A
Panel_1_32151466_UCL_CANTO2_29JAN2016_29JAN2016.fcs_intra.fcs_comp.fcs / #mfi2_MONOCYTES_SSC-A
deviation from closest hinge in number of interquartile ranges (black -, grey+)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Figure 2: Univariate iden fica on of poten al outliers (Plot 1) based onMFI per popula on forMorphologicalParameters
———————————————————————————————————————————————–Automaton: PRECISEADS Panel1 / Project: PRECISEADS Panel1Outlier Iden fica on Report generated with CytAutomaton by AltraBio. h ps://www.altrabio.com
CONFIDENTIAL 23 of 54
#mfi2_BEADS_SSC-A
#mfi2_NOT BEADS_SSC-A#mfi1_S2_SSC-A
#mfi2_S2_SSC-W
#mfi1_S1_FSC-A
#mfi2_S1_FSC-W
#mfi1_S3_FSC-A
#mfi1_S4_SSC-A
#mfi2_MONOCYTES_SSC-A
#mfi2_S3_FSC-H
-8
-4
0
4
-5 0 5Dim1 (47.7%)
Dim
2 (2
3%)
GroupsCHP
DRFZ
IRCCS
MHH
UBO
UCL
Figure 6: Principal Component Analysis(2): MFI for all popula ons for Morphological Parameters. Only the10 most contribu ve factors are drawn
———————————————————————————————————————————————–Automaton: PRECISEADS Panel1 / Project: PRECISEADS Panel1Outlier Iden fica on Report generated with CytAutomaton by AltraBio. h ps://www.altrabio.com
CONFIDENTIAL 29 of 54
Panel_1_32151466_UCL_CANTO2_29JAN2016_29JAN2016.fcs_intra.fcs_comp.fcs / #mfi2_CD8+_TCELLS_KO.APanel_1_32170608_CHP_NAVIOS_10OCT2017_10OCT2017.LMD_intra.fcs_comp.fcs / #mfi2_CD19+_BCELLS_APC.A
Panel_1_32170069_CHP_NAVIOS_13JUL2017_13JUL2017.LMD_intra.fcs_comp.fcs / #mfi2_CD19+_BCELLS_APC.APanel_1_32151976_UBO_NAVIOS_14MAR2016_15MAR2016.LMD_intra.fcs_comp.fcs / #mfi2_CD8+_TCELLS_KO.A
Panel_1_32161423_UCL_CANTO2_24AUG2017_24AUG2017.fcs_intra.fcs_comp.fcs / #mfi2_CD19+_BCELLS_APC.APanel_1_32151991_UBO_NAVIOS_26APR2016_26APR2016.LMD_intra.fcs_comp.fcs / #mfi2_CD8+_TCELLS_KO.A
Panel_1_32161417_UCL_CANTO2_17AUG2017_17AUG2017.fcs_intra.fcs_comp.fcs / #mfi2_CD19+_BCELLS_APC.APanel_1_32151466_UCL_CANTO2_29JAN2016_29JAN2016.fcs_intra.fcs_comp.fcs / #mfi1_CD4+_TCELLS_PB.A
anel_1_32161028_DRFZ_CANTO2_09MAR2017_09MAR2017.fcs_intra.fcs_comp.fcs / #mfi2_CD14LOWCD16POS_NONCLASSICALMONOCYTES_FITC.APanel_1_32151466_UCL_CANTO2_29JAN2016_29JAN2016.fcs_intra.fcs_comp.fcs / #mfi1_CD15LOWCD16HIGH_NEUTROPHILS_FITC.A
Panel_1_32170445_CHP_NAVIOS_27JUL2017_27JUL2017.LMD_intra.fcs_comp.fcs / #mfi1_CD8-_CD4-_TCELLS_PB.APanel_1_32160683_UBO_NAVIOS_23SEP2016_23SEP2016.LMD_intra.fcs_comp.fcs / #mfi2_CD56LOW_CD16HIGH_PC5.5.A
Panel_1_32151130_UBO_NAVIOS_12OCT2017_12OCT2017.LMD_intra.fcs_comp.fcs / #mfi2_CD19+_BCELLS_APC.APanel_1_32150704_UCL_CANTO2_23APR2015_23APR2015.fcs_intra.fcs_comp.fcs / #mfi1_CD15LOWCD16HIGH_NEUTROPHILS_FITC.A
Panel_1_32170036_UCL_CANTO2_04SEP2017_04SEP2017.fcs_intra.fcs_comp.fcs / #mfi1_CD3+_TCELLS_APC.AF750.APanel_1_32150695_UCL_CANTO2_20APR2015_20APR2015.fcs_intra.fcs_comp.fcs / #mfi2_CD8+_TCELLS_KO.A
Panel_1_32170448_CHP_NAVIOS_01AUG2017_01AUG2017.LMD_intra.fcs_comp.fcs / #mfi2_CD19+_BCELLS_APC.APanel_1_32151444_CHP_NAVIOS_02DEC2015_02DEC2015.LMD_intra.fcs_comp.fcs / #mfi2_CD56LOW_CD16HIGH_PC5.5.A
Panel_1_32161424_UCL_CANTO2_19JUN2017_19JUN2017.fcs_intra.fcs_comp.fcs / #mfi1_CD56HIGH_CD16LOW_FITC.APanel_1_32170355_UBO_NAVIOS_21NOV2017_21NOV2017.LMD_intra.fcs_comp.fcs / #mfi2_CD19+_BCELLS_APC.A
Panel_1_32151438_CHP_NAVIOS_12OCT2015_12OCT2015.LMD_intra.fcs_comp.fcs / #mfi1_CD15HIGHCD16NEG_EOSINOPHILS_FITC.APanel_1_32150698_UCL_CANTO2_23APR2015_23APR2015.fcs_intra.fcs_comp.fcs / #mfi1_CD15LOWCD16HIGH_NEUTROPHILS_FITC.A
Panel_1_32151465_UCL_CANTO2_01FEB2016_01FEB2016.fcs_intra.fcs_comp.fcs / #mfi2_CD3-CD56+_NKCELLS_PC5.5.APanel_1_32150696_UCL_CANTO2_23APR2015_23APR2015.fcs_intra.fcs_comp.fcs / #mfi2_CD8+_TCELLS_KO.APanel_1_32150698_UCL_CANTO2_23APR2015_23APR2015.fcs_intra.fcs_comp.fcs / #mfi2_CD8+_TCELLS_KO.A
Panel_1_32160074_UCL_CANTO2_11AUG2016_11AUG2016.fcs_intra.fcs_comp.fcs / #mfi2_CD3-CD56+_NKCELLS_PC5.5.APanel_1_32170448_CHP_NAVIOS_01AUG2017_01AUG2017.LMD_intra.fcs_comp.fcs / #mfi2_CD56LOW_CD16HIGH_PC5.5.A
Panel_1_32151472_UCL_CANTO2_04FEB2016_04FEB2016.fcs_intra.fcs_comp.fcs / #mfi1_CD56LOW_CD16HIGH_FITC.APanel_1_32170069_CHP_NAVIOS_13JUL2017_13JUL2017.LMD_intra.fcs_comp.fcs / #mfi2_CD3-CD56+_NKCELLS_PC5.5.A
Panel_1_32160222_DRFZ_CANTO2_07JUN2016_07JUN2016.fcs_intra.fcs_comp.fcs / #mfi2_CD3+_TCELLS_APC.APanel_1_32170039_UCL_CANTO2_25SEP2017_25SEP2017.fcs_intra.fcs_comp.fcs / #mfi1_CD14LOWCD16POS_NONCLASSICALMONOCYTES_PC7.A
Panel_1_32150704_UCL_CANTO2_23APR2015_23APR2015.fcs_intra.fcs_comp.fcs / #mfi1_CD15HIGHCD16NEG_EOSINOPHILS_FITC.APanel_1_32150704_UCL_CANTO2_23APR2015_23APR2015.fcs_intra.fcs_comp.fcs / #mfi2_CD8+_TCELLS_KO.A
Panel_1_32170354_UBO_NAVIOS_04JAN2018_04JAN2018.LMD_intra.fcs_comp.fcs / #mfi1_CD8-_CD4-_TCELLS_PB.APanel_1_32152087_UCL_CANTO2_08DEC2016_08DEC2016.fcs_intra.fcs_comp.fcs / #mfi1_CD3+_TCELLS_APC.AF750.A
Panel_1_32150813_DRFZ_CANTO2_08SEP2015_08SEP2015.fcs_intra.fcs_comp.fcs / #mfi1_CD15HIGHCD16NEG_EOSINOPHILS_FITC.APanel_1_32151819_UBO_NAVIOS_09MAY2016_10MAY2016.LMD_intra.fcs_comp.fcs / #mfi1_CD15POSCD14LOW_LDGS_PC7.A
Panel_1_32170447_CHP_NAVIOS_14SEP2017_14SEP2017.LMD_intra.fcs_comp.fcs / #mfi2_CD3+CD56+_NKLIKETCELLS_PC5.5.APanel_1_32152228_DRFZ_CANTO2_18FEB2016_18FEB2016.fcs_intra.fcs_comp.fcs / #mfi1_CD15HIGHCD16NEG_EOSINOPHILS_FITC.A
Panel_1_32150426_CHP_NAVIOS_20APR2015_20APR2015.LMD_intra.fcs_comp.fcs / #mfi2_CD14LOWCD16POS_NONCLASSICALMONOCYTES_FITC.APanel_1_32150426_CHP_NAVIOS_20APR2015_20APR2015.LMD_intra.fcs_comp.fcs / #mfi2_CD14HIGHCD16NEG_CLASSICALMONOCYTES_FITC.A
Panel_1_32161137_UBO_NAVIOS_14DEC2016_14DEC2016.LMD_intra.fcs_comp.fcs / #mfi1_CD4+_TCELLS_PB.APanel_1_32170432_CHP_NAVIOS_04OCT2017_04OCT2017.LMD_intra.fcs_comp.fcs / #mfi2_CD19+_BCELLS_APC.A
Panel_1_32160071_UCL_CANTO2_16JUN2016_16JUN2016.fcs_intra.fcs_comp.fcs / #mfi1_CD15HIGHCD16NEG_EOSINOPHILS_FITC.APanel_1_32150933_IRCCS_CANTOII_10SEP2015_10SEP2015.fcs_intra.fcs_comp.fcs / #mfi1_CD8+_TCELLS_PB.A
4 6 8 10 12
Figure 11: Univariate iden fica on of poten al outliers (Plot 2) based on MFI per popula on for Non Mor-phological Parameters
———————————————————————————————————————————————–Automaton: PRECISEADS Panel1 / Project: PRECISEADS Panel1Outlier Iden fica on Report generated with CytAutomaton by AltraBio. h ps://www.altrabio.com
REPRODUCIBLE, ROBUST & ACCURATE RESULTS (CASE STUDY #1)
Consensus vs Expert#1
Consensus vs Expert#4
Consensus vs Expert#3
Consensus vs Automaton
Human Variability vs Automaton robustness
Comparison of gating performances for several human experts & gating automaton (AltraBio & CIRI: J. Marvel, INSERM, Lyon) • Mice, immunological data • Longitudinal data (several time points)
• 5 different human experts
• Consensus gating calculated on experts’ gatings
Consensus vs Expert#2
Consensus vs Expert#5
#1 #2 #3
#4 #5 Auto
Consensus Consensus Consensus
Consensus Consensus Consensus
#cells manual
#cell
s au
to
R=0.999 p value<2x10 -16
SIMILAR RESULTS BUT FASTER (CASE STUDY #2)
manual gating automated gating
• Few days • No subjectivity/reproducibility issues
Auto
mat
ed
Gat
ing
Man
ual
Gat
ing • ~ 1 year of work
• Subjectivity/reproducibility issues
Profiling/Immuno: Monitoring Study on Human data (AltraBio & CRCL: C. Caux, INSERM, Lyon) • ~200 patients (8 samples per patient) • Huge number of gates 519 gates per patient • Some gates with high complexity
(continuum, unobvious boundaries, very rare populations)
ACCURACY: AUTOMATON BENEFITS FROM MULTI DIMENSIONALITY (CASE STUDY #3) Manual gating Automated gating
The automaton takes into account all markers simultaneously, thus
enabling optimized discrimination of cellular
populations
Inaccurately assigned cells
Correctly assigned cells
Profiling/Immuno: Monitoring Study on Mouse data (AltraBio, IMPC & Ciphe: H. Luche, INSERM, Marseille)
USABLE IN CLINICAL TRIAL CONTEXT (CASE STUDY #4)Assessment of the gating automaton performances in a clinical trial context (AltraBio & a French Big Pharma)
• Human donors • Longitudinal study • Different antigen stimulations • Signatures of polyfunctionality
The Automaton handles biological and technical variabilities
Gating automation of a huge immuno-phenotyping study (AltraBio & a US Big Pharma)
• More than 150 000 files to process
But also:
Gating automation of a complex multicentric study (AltraBio & an IMI project)
• 11 centres with different cytometers • Meta-automaton generation
And others….
http://www.altrabio.com 30 rue Pré-Gaudry, 69007 Lyon, France
+33 (0)4 26 84 69 63 julien.nourikyan@altrabio.com
FOR MORE INFORMATION…
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