• Save
Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and Management in High-Throughput Screening Experiments
Upcoming SlideShare
Loading in...5
×
 

Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and Management in High-Throughput Screening Experiments

on

  • 567 views

The presentation was delivered during the 1st International Conference on Health Information Science (HIS 2012) on April 9th, 2012 in Beijing, China. ...

The presentation was delivered during the 1st International Conference on Health Information Science (HIS 2012) on April 9th, 2012 in Beijing, China.

Abstract:
In cytomics bookkeeping of the data generated during lab experiments is crucial. The current approach in cytomics is to conduct High-Throughput Screening (HTS) experiments so that cells can be tested under many different experimental conditions. Given the large amount of different conditions and the readout of the conditions through images, it is clear that the HTS approach requires a proper data management system to reduce the time needed for experiments and the chance of man-made errors. As different types of data exist, the experimental conditions need to be linked to the images produced by the HTS experiments with their metadata and the results of further analysis. Moreover, HTS experiments never stand by themselves, as more experiments are lined up, the amount of data and computations needed to analyze these increases rapidly. To that end cytomic experiments call for automated and systematic solutions that provide convenient and robust features for scientists to manage and analyze their data. In this paper, we propose a platform for managing and analyzing HTS images resulting from cytomics screens taking the automated HTS workflow as a starting point. This platform seamlessly integrates the whole HTS workflow into a single system. The platform relies on a modern relational database system to store user data and process user requests, while providing a convenient web interface to end-users. By implementing this platform, the overall workload of HTS experiments, from experiment design to data analysis, is reduced significantly. Additionally, the platform provides the potential for data integration to accomplish genotype-to-phenotype modeling studies.

Statistics

Views

Total Views
567
Views on SlideShare
543
Embed Views
24

Actions

Likes
0
Downloads
0
Comments
0

2 Embeds 24

http://planet-data.eu 18
http://www.planet-data.eu 6

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and Management in High-Throughput Screening Experiments Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and Management in High-Throughput Screening Experiments Presentation Transcript

  • Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and Management in High- Throughput Screening Experiments HIS 2012 : The 1st. International Conference on Health Information ScienceEnrique Larios, Kuan Yan, Fons J. Verbeek (LIACS, Leiden University, The Netherlands)Ying Zhang, Fabian Groffen (CWI, Amsterdam, The Netherlands)Zi Di, Sylvia LeDévédec (Department of Toxicology, Leiden University, The Netherlands) Centrum Wiskunde & Informatica Leiden University. The university to discover.
  • Introduction§  The current approach in Time Lapse Static Sequence cytomics is to conduct Images Images High Throughput Screening (HTS) experiments so that cells 2D 2D + T can be tested under different experimental conditions.§  In HTS experiments, as 3D 3D + T more experiments are lined up, the amount of data and Cytomics computation needed to analyze these increases rapidly. 2 Leiden University. The university to discover.
  • Workflow in HTS experiments Experiment Plate design planningScientist Scientist 3 Leiden University. The university to discover.
  • Workflow in HTS experiments HTS process Storage Setting up the tiff files microscope BD Pathway ics & ids files tiff files Nikon 1 nd2 files ics & idsScientist files tiff files File Server Nikon 2 nd2 files tiff files nd2 files Nikon 3 4 Leiden University. The university to discover.
  • Workflow in HTS experiments Image Analysis Cell masks and motion trajectories Bioinformaticians High-throughput image analysis provides an automated quantification of dynamic cell behavior in both cellular level and structural level. Data AnalysisScientist By employing pattern recognition theorem, the system provides objective Data Map statistical conclusions to support biological hypothesizes. 5 Leiden University. The university to discover.
  • Problems identified Software Component Duration of Data tools used s used in the (images and are not the experiments metadata) is suitable for experiment can take not linked the work are not months. properly. performed. integrated. There is no platform that can facilitate Scientists to learn from the experience. Lack of a Knowledge Discovery System. 6 Leiden University. The university to discover.
  • Objectives Develop an integrated platform to automate data Design a management and database to image analysis of store almost all cytomic HTS data produced experiments. Establish an and used in automated the HTS workflow experiments. system of the HTS experiments. 7 Leiden University. The university to discover.
  • Workflow of the HTS System 8 Leiden University. The university to discover.
  • Which data should be stored inthe database? Experiment details Users Plates & Wells HTS Database Results of Data Results of Image Analysis Analysis Raw images 9 Leiden University. The university to discover.
  • Description of the SystemArchitectureGUI layer HTS Analysis GUI Plate Design Image Analysis Pattern recognition tools API API API WebServices layer Glassfish - IIS Datastorage / Scientific SuperProcessin g layer Computer 10 Leiden University. The university to discover.
  • }  + easy to add/modify a record }  + only need to read in relevant data}  - might read in unnecessary }  - tuple writes require multiple data. accesses. }  Suitable for read-mostly, read-intensive, large data repositories. }  MonetDB is a open-source database system for high- performance applications in data mining, OLAP, GIS, XML Query, text and multimedia retrieval. MonetDB often achieves a 10-fold raw speed improvement for SQL and XQuery over competitor RDBMSs. by Peter Boncz (CWI) 11 Leiden University. The university to discover.
  • ROW STORAGE COLUMN STORAGE STRIPE STRIPE by Peter Boncz (CWI) 12 Leiden University. The university to discover.
  • HTS System Database Schema 13 Leiden University. The university to discover.
  • How data is organizedin the schema? Users Experiment details 14 Leiden University. The university to discover.
  • How data is organizedin the schema? Plates & Wells 15 Leiden University. The university to discover.
  • How data is organized in theschema? Raw images 16 Leiden University. The university to discover.
  • How data is organized in theschema? Results of Image Analysis Results of Data Analysis 17 Leiden University. The university to discover.
  • How the platform works? Authentication Decision New idea Web User making interface (GUI) UsersUser Roles System §  Audit, maintenance of Administrator users, roles, conditions. §  Create Projects, Administrator Experiments, Plates, Upload the images from the microscope, and Plate layout design (GUI) perform data and image §  analysis. images from Upload the §  Every user need to log in in the Expert User platform and is administrator of their the microscope, and perform data and image own Projects-Experiments. §  analysis.data and image Perform §  A user can also grant to other users a Analyst User analysis and link the specific role (Administrator, Expert results to the experiment. User or Analyst user) and create a collaborative environment. 18 Leiden University. The university to discover.
  • How the platform works?Web Userinterface (GUI) Administration option: •  Create / Edit / Delete users •  Assign Roles to a user 19 Leiden University. The university to discover.
  • How the platform works?Web Userinterface (GUI) Project option: •  Create, Edit, Delete Projects •  Visualize Project’s metadata 20 Leiden University. The university to discover.
  • How the platform works?Web Userinterface (GUI) Experiments option: •  Create, Edit, Delete Experiments •  Visualize Experiment’s metadata 21 Leiden University. The university to discover.
  • How the platform works?Web Userinterface (GUI) Conditions option: •  Create, Edit, Delete, Import Coating parameters, Cell line tissues, Compounds, siRNA, and Antibodies/reagents. 22 Leiden University. The university to discover.
  • How the platform works?Web Userinterface (GUI) Plates option: •  Create, Edit, Delete Plates •  Visualize Plate’s metadata. 23 Leiden University. The university to discover.
  • How the platform works?Web Userinterface (GUI) Reports option: •  Perform custom queries through different datasets. •  Visualize predefined reports about Projects/ Experiments/ Plates/ Well metadata. 24 Leiden University. The university to discover.
  • How the platform works?Web Userinterface (GUI) Analysis option: •  Invoke the Data and Image Analysis APIs . •  Visualize the results of the data and image analysis. 25 Leiden University. The university to discover.
  • How the platform works?Steps in the new Workflow System §  Create a Project §  Create an Experiment §  Design the layout of a culture plate (4x6 wells, 6x8 wells , 8x12 wells, etc.). §  Assign the experimental conditions applied to the wells (drag and drop). §  Allow access to your project to other users assigning them a specific Wet lab experiment role. using the plate design Time-Lapse Image Sequence / HTS Static Images 26 Leiden University. The university to discover.
  • How the platform works? Upload HTS Images HTS Time-Lapse §  The files generated by the Image Sequence / microscope have a standard Static Images named convention. §  Through the GUI, the images are uploaded to the platform. Raw Images §  The platform links the imported images to the experiment and theq  2D (XY): [1] Frame [1] Image [1..n] Channels plate designed previously. §  The platform also reads from theq  2D+T (XY+T): [1] Video [1..n] header of the files information Frames [1] Image [1..n] Channels associated to the microscopeq  3D (XYZ): [1] Frame [1..n] Sections settings. [1] Image [1..n] Channels §  According to the microscope used,q  3D+T (XYZ+T): [1] Video [1..n] the image’s metadata has a Frame [1..n] Sections [1] Image particular structure that is also [1..n] Channel stored in the database. 27 Leiden University. The university to discover.
  • How the platform works? Image Analysis Imagesuploaded §  Through the GUI it is possible to invoke the API for the image analysis process. §  As a result of the image analysis, auxiliary images are generated: binary masks or trajectories. §  These auxiliary images are linked to the plates – wells and raw images in the GUI. Auxiliary images Binary mask Trajectories 28 Leiden University. The university to discover.
  • How the platform works? Data AnalysisImagesAnalysis §  Measurements extracted from the image analysis are further analyzed using Patter recognition tools. §  Through the GUI it is possible to invoke the Binary mask Trajectories API for the data analysis process. §  As a result, it is generated CSV files which are stored in the database in order to have later graphical representations. Example Results Cell migration analysis Structure dynamic analysis 29 Leiden University. The university to discover.
  • Conclusions}  Using this platform for image analysis and management in HTS it is possible to avoid typical man-made errors in the experiments.}  Using this system the time invested in post experiment analysis has been reduced considerably. Now takes less than a week to accomplish the data analysis that previously easily took more than a month with commercial software, or a year by manual observation.}  The platform allows end-users to perform high-profile cytomics with a minimum level of a prior experience on image analysis and machine learning.}  The system uses web services, therefore, the framework is very flexible as it allows the connection to other web services.}  The platform can eventually evolve into a sophisticated interdisciplinary platform for cytomics.}  Having the HTS information comprehensively organized in a sophisticated and scalable database is a fertile ground for knowledge discovery. 30 Leiden University. The university to discover.
  • Questions ?Sponsors: 31 Leiden University. The university to discover.