Download your name - The Open Microscopy Environment

Large Scale Biological Data Handling and
Analysis Using Open Source Alternatives
Dr Karol Kozak
ETH Zurich
May 2009, Paris OME
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Large Scale Experiments and
Informatics
Instrument management
Data acquisition
Data flow
Image processing
Normalization, QC
STORAGE
Data storage
Archiving
Data mining
Bioinformatics
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Detection points
Data Automation:
- Library Handling
- Robotics
- Microscopy
- Image processing
- Cell reliability
- Hit Definition
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Database architecture (LIMS)
Database architecture
WEB
USER
INTERFACE
Command
Line
Client
DB SCHEME
Import / Export
Read/Write
Search
WEBAPPLICATION
MySQL/ PostgreSQL
Oracle/DB/SQL Server
External Bioinformatics Databases :
NCBI, PubMed, SCOPE, PDB, Harvester, …
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STORAGE
Link to microscope
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STORAGE
Link to microscope
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STORAGE
Link to microscope
Olympus SCAN R
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Data archiving
Tape
~100MB
Microscope –format
BD
ThermoFisher
Zeiss
Leica
Olympus
MD
Image processing
- tiff
- lsm
- lei
Convert to jpeg
50 x smaller
In Screening
Process
JPEG JPEG
Results Database
DB
Data Management Tool
After
Screen
JPEG
+ numeric results
plots
~2MB
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*.tiff
In Screening
Process
ETH NAS
Users Tape
LMC interface- user
Buffer server
2x25TB
LMC (6 weeks)
JPEG JPEG
Results Database
DB
Data Management Tool
After
Screen
JPEG
ETH NAS
User storage
+ numeric results
plots
~0.5MB
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Dataflow architecture
siRNA
Inhibit.
Library
Pre-processing
Screening
process
Library checker
Booking system
Library DB
Protocol
Equipment manager
Plates with compounds
Microscope
images
DB
We needScreen
software
(Library Handling, QC, Data
Mining, Visualization,
Export, Flexibility)
HitBase
Screen results
Image
analysis
Published screens
IMAGE SERVER
Data mining
Bioinformatics
Post-processing
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HC/DC
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Read data from database
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Link with image storage
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Data mining
STORAGE
LIMS
Microscope
File system
Statistics
-One/two parameters
-Normalization
-Correlation
-Compare distribution
-Ex. Statistical tests, Zscore, Z’, etc
Pattern recognition –
Machine Learning
-Multi-parameters
-Clustering
-Dimensionality reduction
-Check parameter
importance
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Classification
- Unsupervised learning (Cluster analysis,
Clustering) seeks to discover the classes
?
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Classification problem
?
- Supervised learning (Classification)
assumes classes are known
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2 class problem
Positive control
Negative control
?
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Train data for
supervised learning
5 people
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Train data for
supervised learning
WEKA/R-Project
nodes (KNIME)
Niels Landwehr, Mark Hall, Eibe Frank
(2005). Logistic Model Trees.
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92% Accuracy
HC/DC
Type I
Input data
1 or 2 Input(s)
and
1 or 2 Output(s)
Input data
Output data
Output data
(mostly data readers)
1 Input
(mostly for visualization)
Type III
1 Output
Type II
Each node
has submenu
(right mouse button)
Settings:
Parameters
Rules
Algorithms rules
Filters
Settings
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HC/DC KNIME nodes
HC/DC nodes
WEKA/R-Project
nodes (KNIME)
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Next-Generation sequencing nodes
Roche 454
Type I
Input
data
1 or 2
Input(s)
and
1 or 2
Output(s)
Output
data
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Next-Generation sequencing nodes
SOLEXA
Illumina
Type I
Input
data
1 or 2
Input(s)
and
1 or 2
Output(s)
Output
data
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Proteomics
Type I
Input
data
1 or 2
Input(s)
and
1 or 2
Output(s)
Output
data
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Drag-and-drop
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Pioneering study
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Plot image parameters/descriptors
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Compound screening
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Menu HC/DC
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Library Handling
Data Automation:
- Volume
- Concentration
- Dilutio
- Splitting
- Take liquid
- Barcode
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Workflows for HCS
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Workflows for HCS
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Workflows for HCS
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Workflows for HCS
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Link to OME/OMERO
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Acknowledgements & Partners:
Contribution in development:
ETH Zurich
MPI-CBG, Dresden
Eberhard Krausz (former) & team LMC-RISC: Gabor Csucs & team
SystemX Adrian Honegger & team
Eugenio Fava & team
Marc Bickle & team
MPI-IB, Berlin
Nikolaus Machuy &
team
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WebPage
HCDC: http://hcdc.ethz.ch
Workshops - Webinar:
04.06.2009
2-4pm
Workshops HCDC+ KNIME:
16-17.10.2009
2 days ETH Zurich
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