visual compression of workflow visualizations with automated detection of macro motifs

Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs

Eamonn Maguire, Philippe Rocca-Serra, Susanna-Assunta Sansone, Jim Davies and Min Chen

University of Oxford e-Research CentreUniversity of Oxford Department of Computer Science

VIS 2013, 13th-18th October 2013

Some terminology

Motif

Macro

Commonly observed subgraphs

A single instruction that expands automatically in to a more complex set of instructions.

Workflow Literally a flow of work showing the processes enacted from start to finish in say business processes, software execution, analysis procedures, or in our case, biological experiments.

They are used to enable reproducibility.

e.g VisTrails in our VIS community - 40,000 downloads

Q

Q

D

E

Q

QE

D


Very commonly seen used in: biology - protein-protein interaction, transcription/regulation networks; chemistry; and even visualization (e.g. VisComplete)

Roadmap


Roadmap

WorkflowSubstitute motifs with

‘macros’


AutomaticallyDetect Motifs


Blockades


Blockades

Current Motif Detection Algorithm Limitations

No semantics

Limited motif sizes (Max 10)


Blockades


No semantics

Limited motif sizes (Max 10) Deciding what should

be a Macro

Macros in electronic circuit diagrams are the product of years of refinement.

Macros in biological workflows for instance is new...how do we determine what should be a macro?

Example case

Biology


Taxonomy-based Glyph Design

Maguire et al, 2012IEEE TVCG

Visualizing (ISA based) workflows of biological experiments

Extension on Previous Work


A Typical Biological Experiment

Hypothesis Experiment Results

&

Paper

Analysis


material protocol chemical dataKEY

Source name

Sampling Protocol

Sample name

Chemical Label

Labeling Protocol

Labeled Extract

Hybridisation Protocol

Assay Name

Scanning Protocol

Raw Data File

Feature Extraction Protocol

Processed Data File

Describe the flow of work from a

biological sample to the data file.

Workflow varies between technologies,

but there is a large commonality in steps.

For example, the labeling step is very

common in DNA microarray experiments.

Representing an Experiment - Workflows!

Reproducibility!


MOTIF EXTRACTION ALGORITHM

RANKING ALGORITHM

MACRO SELECTION VIA UI

BIOLOGICAL WORKFLOW REPOSITORY

MACROSELECTION

GLYPH DESIGN MACRO ANNOTATION

2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge

MACRO INSERTION IN GRAPH

MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT

Our Process


Workflow Repository

9,670 Biological Experiment Workflows

Why such a large number?We can statistically make suggestions to users about what motifs can be macros based on a number of metrics (detailed later)

+ we can robustly test our algorithm performance across a huge cross section of experiments...



RANKING ALGORITHM



MACROSELECTION


2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge


MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT



RANKING ALGORITHM



MACROSELECTION


2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge


MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Motif Extraction Algorithm


The Current Weaknesses

No semantics (edge or node)Small node limit normally <10


Imagine n-grams with no information other than topology

e.g. bi-grams of DNA ‘motifs’ where instead of A-T, T-C, T-G > x-x, x-x, x-x

FANMOD, mFinder etc.

The Problem...Current Motif Extraction Algorithms

Ah, and you can’t have macros without function...

Exactly!

We can’t infer function from these results

What’s up?

Unable to infer function Unable to produce a macro


SolutionA a normal state, with

s1

s0

A

B DB

EE

G

H

a holding state, with

a pseudo-ŵŽƟĨ

a ‘’legal’’ ŵŽƟĨ

s2

s3C

C

s4

F

E

H

a starƟŶŐ state

a trĂŶƐŝƟŽŶ that

generates a ŵŽƟĨC

E

generates a ŵŽƟĨ

a trĂŶƐŝƟŽŶ does not

generate a ŵŽƟĨ

A a normal state, with

s1

s0

A

B DB

EE

G

H


a pseudo-ŵŽƟĨ


s2

s3C

C

s4

F

E

H

a starƟŶŐ state



E



generate a ŵŽƟĨ


More detail about each individual case, A-H available in paper.

SolutionA a normal state, with

s1

s0

A

B DB

EE

G

H


a pseudo-ŵŽƟĨ


s2

s3C

C

s4

F

E

H

a starƟŶŐ state



E



generate a ŵŽƟĨ

A a normal state, with

s1

s0

A

B DB

EE

G

H


a pseudo-ŵŽƟĨ


s2

s3C

C

s4

F

E

H

a starƟŶŐ state



E



generate a ŵŽƟĨ

3


More detail about each individual case, A-H available in paper.

Resulting In... From our algorithm, running over 9,670 workflows, we retrieved ~12,000 motifs up to depth 12


Resulting In...

Semantically awareLimited by depth, not node count - we have motifs with > 80 nodes

From our algorithm, running over 9,670 workflows, we retrieved ~12,000 motifs up to depth 12


Essentially, more complicated topologically sensitive n-grams


RANKING ALGORITHM



MACROSELECTION


2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge


MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT



RANKING ALGORITHM



MACROSELECTION


2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge


MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

Ranking Algorithm...because 12,000 is just too much.


Ranking Algorithm

M1 - Occurrences in data

repository

1,043

M2 -Workflow Presence

640...

M3 -Compression Potention


Ranking Algorithm

M1 - Occurrences in data

repository

1,043

M2 -Workflow Presence

640...

M3 -Compression Potention

For At, Aw and Ac, we map it to a fixed range [−1, 1] using a linear mapping based on the min-max range of each indicator, yielding three normalized metrics M1 , M2 and M3


No algorithm would be complete without a weighting element. So each metric can be weighted. We use a default weight of 1.

Ranking Algorithm

3 Normalized metrics Motif subgraph 3 Glyph representations

Filter by pattern presenceLinear, branching and merging

Filter by min/max depth

Motifs arranged by depth

Depth 6 motifs with magnified view in B and detailed popup of selected motif in D


Ranking Algorithm






Occurrences Workflow

presence

Score Compression

Potential


Ranking Algorithm







presence

Score Compression

Potential

Downgrade Icon

Adjusted Score


Ranking Algorithm







presence

Score Compression

Potential

Downgrade Icon

Adjusted Score


1000

Ranking Algorithm







presence

Score Compression

Potential

Downgrade Icon

Adjusted Score


Subset of

1000

1200

Ranking Algorithm







presence

Score Compression

Potential

Downgrade Icon

Adjusted Score


Subset of

1000

1200 200


RANKING ALGORITHM



MACROSELECTION


2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge


MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT



RANKING ALGORITHM



MACROSELECTION


2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge


MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT

Glyph Design


Glyph Design

Topology/structure within a macro

Node type

Density

Annotation

Things we’d like to see...


Glyph Design

annotation

annotation

Node typecolour/shape


Length

Topologyarrangement

Breadth

Topologyarrangement

Breadth

Topologyoverall

Node typecolour

Length

Breadth

Length

annotation


annotation

annotation

STATE-TRANSITION MODEL EXAMPLES



Length

Topologyarrangement

Breadth

Topologyarrangement

Breadth

Topologyoverall

Node typecolour

Length

Breadth

Length

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4

F

E

H

C

E

s1

s0

AA s

1ss0 s

3

Bs1

s3C A s

1

Es4

s1

s4

F s1

s4

G

annotation


RANKING ALGORITHM



MACROSELECTION


2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge


MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT



RANKING ALGORITHM



MACROSELECTION


2.87

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

2.4

OCCURRENCE

600

WORKFLOWS

240

COMPRESSION

2400

SELECTED MACROS

DOMAIN EXPERT

Branch & Merge

Branch & Merge

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

Branch & Merge

Branch & Merge


MOTIFS

...

2.871

OCCURRENCE

1092

WORKFLOWS

476

COMPRESSION

3276

-2.43n

OCCURRENCE

20

WORKFLOWS

10

COMPRESSION

200

...

DOMAIN EXPERT

Branch & Merge

Branch & Merge

Macro Insertion for Workflow Compression



A



A

B



A

B

C



A

B

C D


Evaluation


User Testing Performance

Evaluation


Community Dissemination


A B

Dissemination of macros to community

Automacron API available as an OSGi plugin for ISAcreator


Roadmap

WorkflowSubstitute motifs with

‘macros’AutomaticallyDetect Motifs



Overcoming the blockades


No semantics


be a Macro






No semantics


be a Macro



New semantically enabled algorithm




No semantics


be a Macro



New semantically enabled algorithm

Statistically informed selection fro

m

analysis of a large corpus of w

orkflows


Summary

New semantically enabled motif discovery algorithm

Statistically informed selection of macro candidates for use in biological workflow visualizations

Automated macro image generation from inferred from algorithm states

Integration of final selections and utility to compress in ISAcreator tool for curators and biologists alike

Open source - we want you to extend!

F

A

s1

s0

A

B DB

EE

G

H

s2

s3C

C

s4E

H

C

E

github.com/isa-tools/automacron

Philippe Rocca-SerraSusanna-Assunta SansoneJim DaviesMin Chen

Co-authors

AlsoAlejandra Gonzalez Beltran for many useful discussions

Bye.

You can download this software now!

And yes.

It is open source!


visual compression of workflow visualizations with automated detection of macro motifs

Technology

biological workflows

biological sample

workflow repository9

domain expert2

experiment workflows

limited motif sizes

as0 1as0as1bes2bs3cd

sampling protocol sample