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Wim de Leeuw, Swammerdam Institute for Life Sciences, Amsterdam Pernette Verschure, Swammerdam Institute for Life Sciences, Amsterdam Robert van Liere, Center for Mathematics and Computer Science, Amsterdam

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slide1

Wim de Leeuw, Swammerdam Institute for Life Sciences, Amsterdam

Pernette Verschure, Swammerdam Institute for Life Sciences, Amsterdam

Robert van Liere, Center for Mathematics and Computer Science, Amsterdam

Visualization and analysis of large data collections: a case study applied to confocal microscopy data
motivation 1
Motivation (1):

Context: cell biology experiments

Phenomenon captured using digital microscopy

Experiment characteristics:

Biological diversity

Not all biological parameters can be controlled

Many measurements needed

motivation 2
Motivation (2):

Visualization and analysis of collections of data sets

High variability

Non-trivial information extraction (eg segmentation)

Noise

Visualization Modes: Interactive vs Batch

Interactive control+feedback vs static settings of parameters

Time consuming vs multiple data sets processed simultaneously

Aim: combine advantages of Interactive and Batch Visualization

agenda
Agenda

Biological Problem

Chromatin structure and gene control

Visualization Problem

Data collection description

Analysis with visual summaries

chromatin structure and gene control
Chromatin Structure and Gene Control

Chromatin Structure

Low level : DNA, nucleosomes, 30 nm fiber

High level: fiber folding

Gene control

Regulation of gene activity

Biological research question:

Relation chromatin structure and gene control

Is there, what is, when, etc....

experiment
Experiment

Question: influence of Hetrochromatin protein 1 on chromatin structure?

Approach:

Prepare collection of cells with a specific region

Control group: target GFP to the region

HP1 group : target GFP/HP1 to the region

Observe regions with confocal microscope

Data analysis question:

Identify and quantify the differences between control and HP1 group

collection of data sets
Collection of data sets

60 data sets (30 control group, 30 HP1 group)

Each data set: 512 x 512 x 32

Sample images:

Control group (left)

HP1 group (right)

Data analysis questions:

Accurately detect region of interest

Quantify region attributes (size, roughness, roundness, etc)

What are the attribute differences in the control and HP1 groups ?

interactive visualization of collection
Interactive Visualization of Collection

Advantages

Control over visualization tools and parameters

Segmentation

Attribute computations

Direct feedback

Disadvantages

Laborious

Error prone

batch processing of collection
Batch processing of collection

Advantage

All sets are processed automatically

A-priori parameter settings

Disadvantage

No feedback on the process

visual summaries
Visual Summaries

Definition: a user defined compact visual representation of the data during (batch) processing

Governing idea: the visual summary is used to visualize the steps in batch process

Examples:

General strategy:

Interactive setup (determine parameters, attributes, etc)

Batch processing using setup

Information visualization with visual summaries

discriminating groups
Discriminating groups
  • Red: HP1 sets, Green: control
  • Region granularity vs number of spots in region
  • Granularity attribute
    • Average intensity gradient of region
  • Plot tells us:
    • Large variation, some outliers
    • HP1 and control seem different
large variation some outliers
Large variation, some outliers
  • Brush / link outliers
    • Investigate visual summary
  • Problems with data set
    • Corrupt data
hp1 and control seem different
HP1 and control seem different
  • Further analysis
    • Histograms
    • Box plots
  • Statistical tests
    • Wilcoxon
  • Wilcoxon tells us that there is indeed a significant difference
lessons learned
Lessons learned

Showing a significant difference in granularity vs number of spots tells us that the HP1 effects the structure of chromatin. The effect is that chromatin is condensed in a number of compact regions.

Biological significant result. Two papers published

Strategy for analysis of collections of confocal data sets

Interactive visualization and batch processing are both needed

Information visualization is used for the analysis of batch output

Visual summaries are used to link back to original data set or previous steps in batch process

Strategy has been implemented as the Argos system

generality
Generality

Argos has been used for the analysis of an experiment consisting of 2500+ confocal data sets

Argos has been used for the analysis of micro array data