machine learning-based gating automation · tree layouts (e.g. spade), multi dimensional scaling,...

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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0.6

0.8

1.0


sample index

RF

votin

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●●●

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0

10

20

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FALSE TRUEType

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●

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0

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Wilcoxon, p = 0.095

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20

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● ● ● ● ● ●

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0 10 20 30 40

0.0

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sample index

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●●●

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●

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0 10 20 30 40

0.0

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sample index

RF

votin

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●●●

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● ●●● ● ●●

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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


#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



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


REPRODUCIBLE, ROBUST & ACCURATE RESULTS (CASE STUDY #1)

Consensus vs Expert#1



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



#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 [email protected]

FOR MORE INFORMATION…

mailto:[email protected]?subject=

mailto:[email protected]?subject=

machine learning-based gating automation · tree layouts (e.g. spade), multi dimensional scaling,...

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