cytoscape tutorial session 2 at ut-kbrin bioinformatics summit 2014 (4/11/2014)

Keiichiro Ono

Bioinformatics Summit 2014 4/11/2014

Cytoscape Tutorial 2: Advanced Topics

- Effective Visualization with Cytoscape

- Use Cytoscape with external data analysis tools

- Cytoscape and The Web

Part 2: Agenda

- This section is a bit conceptual rather than practical, but it is very important to understand before creating actual data visualizations

Part 2: Agenda

Effective Visualization

Now, you know…- Basic features of Cytoscape

- How to load network / table data - Basic Analysis / Filtering - Layout - Edit Visual Styles

- Ready to create great visualizations!

YPL201C YPL211W YML007WYPL131W YOR327CYDR171W YCL067C

YCL032W YGL208WYER074WYBL050W YLR134WYPL149W

YDR050C YMR311CYGL134WYER102W YBR112CYKL101W YNL199C

YPL222W

YLR264W

YPL089C

YNL098C

YLL028W

YBR072W

YOR326W

YJR066W

YOR039W YNL135C YPR041WYDR174W YIL074C YKL028WYOR362C

YIL162W

YNL189W

YOR212W

YPR080W

YPR145W

YLL019C

YLR284CYPL031C YFR037CYML074C YPL240CYPR048W YBR274W YBR050C YML032C

YJR022WYBR248C YDR382W YER081WYIR009W YDR244W YOL016C

YER103W YGR058WYLR256WYAL003W YOR355WYIL061C YER111C YMR309C

YPL248CYOL127W YBR019CYLR362W YGL035CYPR167C YML123C

YBL026WYLL021W YNL091W YOR178C YIL113WYLR321C YML064C YMR117C YDL194WYNR007C

YOL058WYBR045CYER065CYNL167CYNL047C YGL097WYHR071W YDL078C YDL081CYDR354W

YER145C YGR136WYDR311W YPR119WYER112W YLR214WYCR012W

YER143W YBR043CYKL204W

YGR019WYEL041W YER133W

YOL149W YBR118WYAL038W YDR167WYMR058WYER079W YMR291W

YKL012W YDL113CYDR299W YDL075W YDL236W YGL229CYLR377C YNL145W

YNL236W YJL030W

YOL156W

YGL013C

YHR171W

YBL021CYMR021C

YHR174W

YFL038C

YER090WYPR062W YAR007C YNL307C YGL237CYML024WYDR335W YLR075W

YNL050CYGR046W YAL040CYLR191W YMR138WYIL045W YHR005C YNL301C

YKL211CYLR452C YPL075WYML051W YOL123W YGR088WYHR198C YMR300C

YJR060W YMR043WYPR124WYLR081W YLR319CYKL074C YOR036WYKL001C

YDR100W

YDR395W

YDR009W

YDR309C

YPR102C

YAL030W

YHR084W

YLR345W

YBR170C

YJL089WYFL026W YBR018C YGL115W YHR179WYDL215CYGR009C YOL120C

YFL017C YDR429C

YIL052C YGL073W

YGR108WYPR035W

YJL190CYOL086C YHR055CYBL005WYKR026C

YBR155W

YOR264W YKL109W

YOR167C

YDR070CYEL015W

YIL133C

YGL166WYHR030CYGL008C

YMR146C YBR160W

YOL136C

YOL051W

YBR020W YBR190WYDR323CYLR197W YFR014CYKL161C

YML054C YKR099WYLR340WYGL106W YBR093CYCL040W

YLR044C

YCR086WYDL130W

YJL203W

YEL009CYBR135W

YOR361C YGR085C

YER056CA

YNL216WYMR005W

YBR109C

YLR229C YER124C

YJL157C YDR461WYNL154CYLR117C YKR097W YIL069CYMR186W YJR109CYIL015W

YER040WYDR103W YGR074WYER052C YIL160CYOR290C YLR249W

YGL153WYOR215CYGR254W YLR432WYCR084CYOR089CYGR218W YOR303W

YGL161C YLR293CYDL030WYNL036W YHR135CYER179W YDR277CYDR184CYNL312W YML114C YFL039C YOL059WYER054C YER110CYLR109W YLR116WYNL214W YBL069W

YHR141CYER116CYJL219W YPL111WYDL023C YGL202WYER062C YMR183CYFR034CYGL122C

YIL105C YDL088CYPR010C YJR048W YIL070C YEL039CYDR412WYMR108W

YOR204W YMR255W YLR175W YHR115CYNL164C YJL013C YDL063C YNL117W

YIL143CYOR315W YDR146CYLR310CYGR014WYBR217W

YNR053C

YJL036W

YNL116W

YOR120W

YDL014W

YJL194W

YDL013W

YDR032C

YOR310C YPR113W

YLR153C

YGR048W

YGR203W

YNL113WYOR202W

YNR050C

YCL030C

YJL159W

YHR053CYPR110C?YLR258W YBL079W

YNL069C YNL311CYDR142C YGL044CYMR044W

Great Visualization…?

Bad Visualizations

- Unfortunately, there are lots of bad data visualizations…

- Too many colors - Too many mappings - Lack of Story

YPL201C YPL211W YML007WYPL131W YOR327CYDR171W YCL067C

YCL032W YGL208WYER074WYBL050W YLR134WYPL149W

YDR050C YMR311CYGL134WYER102W YBR112CYKL101W YNL199C

YPL222W

YLR264W

YPL089C

YNL098C

YLL028W

YBR072W

YOR326W

YJR066W

YOR039W YNL135C YPR041WYDR174W YIL074C YKL028WYOR362C

YIL162W

YNL189W

YOR212W

YPR080W

YPR145W

YLL019C

YLR284CYPL031C YFR037CYML074C YPL240CYPR048W YBR274W YBR050C YML032C

YJR022WYBR248C YDR382W YER081WYIR009W YDR244W YOL016C

YER103W YGR058WYLR256WYAL003W YOR355WYIL061C YER111C YMR309C

YPL248CYOL127W YBR019CYLR362W YGL035CYPR167C YML123C

YBL026WYLL021W YNL091W YOR178C YIL113WYLR321C YML064C YMR117C YDL194WYNR007C

YOL058WYBR045CYER065CYNL167CYNL047C YGL097WYHR071W YDL078C YDL081CYDR354W

YER145C YGR136WYDR311W YPR119WYER112W YLR214WYCR012W

YER143W YBR043CYKL204W

YGR019WYEL041W YER133W

YOL149W YBR118WYAL038W YDR167WYMR058WYER079W YMR291W

YKL012W YDL113CYDR299W YDL075W YDL236W YGL229CYLR377C YNL145W

YNL236W YJL030W

YOL156W

YGL013C

YHR171W

YBL021CYMR021C

YHR174W

YFL038C

YER090WYPR062W YAR007C YNL307C YGL237CYML024WYDR335W YLR075W

YNL050CYGR046W YAL040CYLR191W YMR138WYIL045W YHR005C YNL301C

YKL211CYLR452C YPL075WYML051W YOL123W YGR088WYHR198C YMR300C

YJR060W YMR043WYPR124WYLR081W YLR319CYKL074C YOR036WYKL001C

YDR100W

YDR395W

YDR009W

YDR309C

YPR102C

YAL030W

YHR084W

YLR345W

YBR170C

YJL089WYFL026W YBR018C YGL115W YHR179WYDL215CYGR009C YOL120C

YFL017C YDR429C

YIL052C YGL073W

YGR108WYPR035W

YJL190CYOL086C YHR055CYBL005WYKR026C

YBR155W

YOR264W YKL109W

YOR167C

YDR070CYEL015W

YIL133C

YGL166WYHR030CYGL008C

YMR146C YBR160W

YOL136C

YOL051W

YBR020W YBR190WYDR323CYLR197W YFR014CYKL161C

YML054C YKR099WYLR340WYGL106W YBR093CYCL040W

YLR044C

YCR086WYDL130W

YJL203W

YEL009CYBR135W

YOR361C YGR085C

YER056CA

YNL216WYMR005W

YBR109C

YLR229C YER124C

YJL157C YDR461WYNL154CYLR117C YKR097W YIL069CYMR186W YJR109CYIL015W

YER040WYDR103W YGR074WYER052C YIL160CYOR290C YLR249W

YGL153WYOR215CYGR254W YLR432WYCR084CYOR089CYGR218W YOR303W

YGL161C YLR293CYDL030WYNL036W YHR135CYER179W YDR277CYDR184CYNL312W YML114C YFL039C YOL059WYER054C YER110CYLR109W YLR116WYNL214W YBL069W

YHR141CYER116CYJL219W YPL111WYDL023C YGL202WYER062C YMR183CYFR034CYGL122C

YIL105C YDL088CYPR010C YJR048W YIL070C YEL039CYDR412WYMR108W

YOR204W YMR255W YLR175W YHR115CYNL164C YJL013C YDL063C YNL117W

YIL143CYOR315W YDR146CYLR310CYGR014WYBR217W

YNR053C

YJL036W

YNL116W

YOR120W

YDL014W

YJL194W

YDL013W

YDR032C

YOR310C YPR113W

YLR153C

YGR048W

YGR203W

YNL113WYOR202W

YNR050C

YCL030C

YJL159W

YHR053CYPR110C?YLR258W YBL079W

YNL069C YNL311CYDR142C YGL044CYMR044W You need to learn how to avoid this and need to make something like…

MSL5

TEM1

PRP40

MUD2

HAP4HAP2

CYC1

GCY1

HAP3

YHR198C

ECI1

YEL015W

GAL1

GAL7

GAL80

GAL3

GAL11

GAL4

GAL2

MLS1

SIP4

FBP1

GAL10

SWI5

SUC2

MIG1

ADH1PGK1

CDC19

GCR1

CBF1ENO1

ENO2

MCK1

CYC7

HAP1

CTT1

NCE103

SSL2

TFB1YNL091W

TRP4

ARG1

GCN4

SKO1

HIS3

ADE4 ILV2

TIF35

TIF5 NIP1

GNA1

PRE10

PRT1

YDR070C

GPD2

RPS17A

BAS1

HIS7

RPS24B

MSL1

HIS4

PDC5

PHO84

PHO4

YNL047C YIL105C

MET16

RPL11BRPS8B

RPL10

RPL11A

CKS1

RPL31A

PHO13

PDC1

SXM1RPL34B

RPL16B

ATC1

CAR1

FCY1

RFA2

ICL1SRP1

TPI1RPL18B

RPL25

PHO5

RPS24ARPL18A

DMC1 RAP1

RPL16A

HSP42

I’m not a designer…

- But learning basic principles of design and data visualization is not so hard

- Creating 10/10 visualization is difficult, but 8/10 is the goal for us

- Let’s avoid pitfalls!

What is BAD Visualization?

- Lack of story - What’s the point?

- Hard to understand - Too many or too few

visual mappings - Ugly

Story (or Goal)- Example:

- I want to show the changing levels of gene expression for three time points

- Assign gene expression profile to the primary visual property in your visualization

MSL5

TEM1

PRP40

MUD2

HAP4HAP2

CYC1

GCY1

HAP3

YHR198C

ECI1

YEL015W

GAL1

GAL7

GAL80

GAL3

GAL11

GAL4

GAL2

MLS1

SIP4

FBP1

GAL10

SWI5

SUC2

MIG1

ADH1PGK1

CDC19

GCR1

CBF1ENO1

ENO2

MCK1

CYC7

HAP1

CTT1

NCE103

SSL2

TFB1YNL091W

TRP4

ARG1

GCN4

SKO1

HIS3

ADE4 ILV2

TIF35

TIF5 NIP1

GNA1

PRE10

PRT1

YDR070C

GPD2

RPS17A

BAS1

HIS7

RPS24B

MSL1

HIS4

PDC5

PHO84

PHO4

YNL047C YIL105C

MET16

RPL11BRPS8B

RPL10

RPL11A

CKS1

RPL31A

PHO13

PDC1

SXM1RPL34B

RPL16B

ATC1

CAR1

FCY1

RFA2

ICL1SRP1

TPI1RPL18B

RPL25

PHO5

RPS24ARPL18A

DMC1 RAP1

RPL16A

HSP42

Map gene expression values to color

Avoid using more colors in other components (edge/label)

If necessary, map other data into non-overlapping visual properties

(edge score to width)

“Cool” does not always mean “Effective”

- This is what I’ve learned from my past experiences…

Case Study: 3D Visualization

- Background: - In late 90’s, 3D graphics card was

cheap enough for entry-level workstations

- Many researchers made tons of 3D graphics applications for data visualization

3D Network View by igraph

Carpendale et al. 96. Distortion Viewing Techniques for 3-D Data

What is the Advantage?

…And My Mistakes

Experimental 3D renderer for Cytoscape

Technology Oriented, Lack of Story!

What was the problem?

… It would be more accurate to say that visual space has 2.05 dimensions.

Lessons Learned…- Introduce additional dimension / complexity to the

visualization only when it is necessary - Animation, 3D, charts on nodes, etc.

- Use minimal set of visual channels to make the visualization understandable

- Define story (or goal) before creating actual visualization

- Understand human perception

Goal of Scientific Data Visualization

- Help scientists to understand their data sets

- Tell a STORY

- Just follow some simple principles - Info-Graphics != Data Visualization

- Art/Design : Science - Infographics 8:2 - Scientific Visualization 1:9

You Don’t Have to be a Professional Designer

What is Good Visualization?

http://www.visualcomplexity.com/vc/

http://www.visualcomplexity.com/vc/

- One of the unfortunate trends in data-driven life sciences is that they increasingly use programmers to abstract data so that mundane information looks visually appealing - this is motivated by the desire to appear on the cover of the glossy life sciences journals.

- Comment from Wired Magazine article “Circle of Life: The Beautiful New Way to Visualize Biological Data”

http://www.wired.com/wiredscience/2013/11/wired-data-life-martin-krzywinski/

http://www.wired.com/wiredscience/2013/11/wired-data-life-martin-krzywinski/

An Extreme Example (I’m not saying this is bad, but…)

http://youtu.be/WTHtYZcH6fk

http://youtu.be/WTHtYZcH6fk

Don’t be Too Cool!- Cool visualizations are sometime useless for

scientists - But still good for journal cover page…

- Balance coolness and effectiveness - Think about audience (or users if it is

interactive)

Visualizing Heterogeneous Data In a Diagram is HARD

- Visualization itself is a research area - You should learn about commonly used

techniques and principles from experts

Human Interactome data from BioGRID visualized by Cytoscape

Large Scale Visualizations are Pointless in Many Cases

Good Large-Scale Visualizations

Ultimately, you want…

SDHA

Tyrosine metabolismFH

Arginine and proline metabolism

C00149

C00122

K00239...

SUCLG2

C15973Valine

SUCLG2C00091

DLD

DLST C16254C00042

C16255DLAT C05125C00024 PDHA1

K01643...

C00417C00158 ACO1ACLY

DLD

MDH1 C00036

C15972C15973

CS

IDH1

C00022

ACO1

PC

PDHA1

C00311

Alanine

Fatty acid degradation

Fatty acid biosynthesis

Valine

TITLE:Citrate cycle (TCA cycle)

Glyoxylate and dicarboxylate metabolism

Fatty acid elongation

D-Glutamine and D-glutamate metabolism

K17753

IDH1

Ascorbate and aldarate metabolism

C00026

IDH3A...C05379

Alanine

C05381

C15972

K00174...

C00068

OGDHOGDH

C00074PCK1

C00068

Glycolysis / Gluconeogenesis

K00169...

K01610

But this is still useful

Costanzo et al.

Targeting the Audience

- Even meaningless (but cool) visualization is useful as a eye-catcher or journal cover page

- When you need figures for your publication, minimize the noise in your visualization and keep it simple

Data Visualization Tools

http://selection.datavisualization.ch/

http://selection.datavisualization.ch/

Effective Visualization for Non-Designers

- Excellent resource for data visualization Tamara Munzner’s Web Sitehttp://www.cs.ubc.ca/~tmm/

http://www.cs.ubc.ca/~tmm/

Resources

- Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information.ACM Trans. Graph. 5, 2 (April 1986), 110-141.

Effectiveness Principle

Encode most important attributes with highest ranked channels [Mackinlay 86]

Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information.ACM Trans. Graph. 5, 2 (April 1986), 110-141.

Channels are NOT Equal!

- Understand human perception - Use proper channel for proper data

Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information.ACM Trans. Graph. 5, 2 (April 1986), 110-141.

In Cytoscape…1. Position: Node Position 2. Length: Edge Length 3. Area: Node Size, Edge Width 4. Color: Node/Edge/Label Color 5. Density: Node/Edge/Label

Transparency

1. Position

Power of Layout

C16255C00074 C00026 C16254 C00068 C05125

Alanine

MDH1

Valine


C00024

C00036


ACLY


C00022 C00068 DLST

DLDPDHA1

SDHA


FH

C00149

Tyrosine metabolism

C15973

DLD

DLAT

C00042


OGDH C00417


ACO1

Alanine C00311

C15972

PDHA1C15973

SUCLG2

C00091 Valine

SUCLG2C00122

ACO1

C00158

CS


C15972C05381

OGDH

PC

PCK1


IDH1

IDH3A...C05379

IDH1

TITLE:Citrate cycle (TCA cycle) K00239...K00174...K01610 K01643...K00169...K17753

SDHA



C00149

C00122

K00239...

SUCLG2

C15973Valine

SUCLG2C00091

DLD

DLST C16254C00042

C16255DLAT C05125C00024 PDHA1

K01643...


DLD

MDH1 C00036

C15972C15973

CS

IDH1

C00022

ACO1

PC

PDHA1

C00311

Alanine



Valine





K17753

IDH1


C00026

IDH3A...C05379

Alanine

C05381

C15972

K00174...

C00068

OGDHOGDH

C00074PCK1

C00068


K00169...

K01610

Layouts

- Some cases, manual editing is necessary - Start from tweaked automatic layout, and

then use techniques discussed later

Group Similar Nodes

Cytoscape Function for This

- Apply layout to selected nodes only

Use Case

- Show group of nodes in same cellular location

- Same functional groups

Tweak Layout Parameters

- Layout - Settings to tweak parameters


Stacking & Grouping

- Manual Layout


2. Length

In Cytoscape

- Edge Length - Can be used for the similarity of

the connected nodes

- Long = less related

- Short = closely related

Scaling

3. Area

In Cytoscape- Node Size / Edge Width

- Two strongest visual channels for mapping your data

- Use these two for your important data

- Automatic layout algorithms can be applied only to selected group of nodes

Edge Weight to Width

SDHA



C00149

C00122

K00239...

SUCLG2

C15973Valine

SUCLG2C00091

DLD

DLST C16254C00042

C16255DLAT C05125C00024 PDHA1

K01643...


DLD

MDH1 C00036

C15972C15973

CS

IDH1

C00022

ACO1

PC

PDHA1

C00311

Alanine



Valine





K17753

IDH1


C00026

IDH3A...C05379

Alanine

C05381

C15972

K00174...

C00068

OGDHOGDH

C00074PCK1

C00068


K00169...

K01610

C00122

SDHA

FHTyrosine metabolism


K00239...

Valine

SUCLG2

C15973

DLSTSUCLG2 C00091

DLD

C16254C00042

C05125

DLD

DLAT

PC

C00022C16255C00024PDHA1 PDHA1

C15973 C15972

C00158 C00311

IDH1

ACO1

C00417

ACO1

K00169...

C00074

PCK1

C00068

Glycolysis / GluconeogenesisK01610

Alanine


Valine





C05379 K17753IDH3A...

C00026Ascorbate and aldarate metabolism


IDH1

AlanineK00174...

OGDH

C00068

C15972

C05381OGDH

C00149

K01643...

MDH1ACLYC00036

CS

4. Color

In Cytoscape

- Node/Edge/Label Color - Less accurate, but still useful

especially when you map to continuous values

- Automatic layout algorithms can be applied only to selected group of nodes

Expression Values To Node Colors

Common Pitfall: Use Too Many Colors

- Simply awful

- Hard to understand

- Doesn’t tell anything!

Colors for Categorical Data

- Again, limitation of our perception — Use up to 6~7 Colors

- Preferably, 3-4

- Less is better!

5. Density

In Cytoscape

- Node/Edge/Label Transparency - Use to emphasize important region

of the network - Density of connections

- Use edge bundling for dense network

YNL036WYDR312WYNL121C

YNL183C

YMR260C

YNL213C

YNL212W

YAL054C

YJL176C

YML012W

YJL174W

YKR082W

YHR051WYDR295CYFL048C

YNL258CYHR115C

YOR205CYNR038W

YMR197CYKR059W

YNL189W

YDL032W

YOR207CYPL217C

YBL039C

YBR078W

YBR030W

YNL068C

YJL063C

YGL120C

YLL008W

YER111C

YIR023W

YPL204W

YDL056W

YEL009C

YOR372CYGR162W

YMR012WYJL138C

YOR117WYNL085W

YOR116C

YBR011C

YDL145C

YCR053W

YIL131C YAL023CYOR272W

YDL213C

YDR207CYOR206W

YLR182WYOR039W

YKL172W

YDL014W

YJL109C

YKR081C

YPL012W

YGL228WYOL004W YBL038WYDL035C

YGL229C

YBR247CYER006W

YNL132W

YOL139C

YLR175WYKL143W

YJR105W

YNL117W

YHR090C YBR146W

YDR283C

YBR029C

YGR059W

YKL144C

YOR261CYHR200W

YDL106C

YLR025W

YOL108C

YPR187W

YOR310CYHL029C YKL016C

YMR079W

YMR198W

YMR093W

YOR210W

YMR078C

YKL014C

YGR231C

YGR232WYLL033W

YFR004W YLL034C

YLR222C

YLR129W

YLR399C

YGR145WYBR077C

YIL035C

YOR145C

YKL015WYPL126W

YDR208W

YDR384CYJL191W

YDR385W

YLR337C

YDR448W

YLR264W

YGR090W

YMR172W YOR061W

YDL116WYGL019W

YMR229C

YML069W

YDL060W

YOL116W

YDR449C

YKR060W

YKR095W

YKR057W

YPL131W

YDL075W

YPL249C-A

YPL086C

YIL133C

YER130CYKL057C

YOL040CYLL043W

YER082C

YGR253C

YBL007C

YLR409CYCR057C

YBR190WYDR447CYML026CYOR095C

YDL133WYGR214W

YNL163C YER056C-AYEL054CYDL076C

YBR181C

YLR047CYJR123W

YNL096C

YHR021C

YBR117C

YDL130W

YNL069C

YDL136WYOL127W

YBR084C-A

YDR418W

YGR213C

YOR355WYFR032C-A

YLR048W

YDL082W

YKR094C

YMR142C

YOR369C

YNL067WYKL006W

YPL080C

YMR230WYLR388W

YPR080W YHR141CYGR027C

YDR470CYKL156W YJL136C

YJL190CYOR235W

YIL069CYLR387C

YPL199CYGL147C YOR293W

YFL034W

YOR292CYLR441CYOR312C

YDR471W

YMR194WYML024W YOL121C

YJL177W

YGL124CYER074WYGR034W

YBL093CYFL034C-A

YLR183C

YLR184WYDL061C

YBR118WYKL006C-A

YDR064W

YLR326W

YBR189WYPR132WYLR447C

YHR142WYBR085WYDL083C

YLR325C

YNR037C

YMR128W

YNL306W

YNR035C

YGL195W

YDL029W

YDR363W-A YDR280W

YIL032C

YJR116W

YML013C-A

YHR116W

YKL020C

YDR194C YPL037CYDR422C YPR178W

YNL322C

YPL228W

YDR296W

YHL027W

YEL037CYHR078W

YKL196C

YAL053W

YDR339C

YCR003W

YOR309C

YGR186WYBR101C YIR010W

YOL036W

YCL001W-A

YCR001WYOR322C

YER117WYGL136C

YLR208W

YBL087C

YER116C

YNL178WYPL184CYHR204W

YPL143WYER101C

YGL031CYLR030W

YGL189CYDL184C

YEL008WYER131WYGL030WYDL133C-A

YOR182CYPR042C

YGR118W

YML073CYOL120C

YBR116CYOR183W

YLL045C

YPL034W YMR164CYLR439W

YAL022CYGR250C

YBL080CYHR161C YPL242C

YLL011WYNL313C

YLR056WYDR130C

YKR029CYOR056CYLR400W

YDR405W

YGR183C

YGR270WYGR128C

YMR296C

YCR082W

YNL037C

YNL267W

YNL116WYGL106W

YDR234WYPL232WYNL059C YJR104CYNL057W

YMR033WYKL112W YNL119W

YGR129W

YML081W

YDL130W-A

YDL190C

YDL209C

YMR005WYDL160C

YHR162WYHL028W

YLR055CYNL312WYOL077C

YCL004WYGL122C

YKL028WYGR056W

YGL107CYGL222C

YPL159CYKL029C

YJL183WYGR268C

YJL062W

YJL111WYNL118CYPR018W

YDL012C

YEL017C-AYKL135CYDR285W

YAL043CYLR293C

YOR209C

YDR326C

YIL036WYDL105W

YMR092CYCL016C

YGL192W

YGL092W

YDR501W YNL112W

YMR116CYJR042W

YGL207WYDR500C

YPR103WYLR367WYLR344W YLR029C

YOR234CYPR043W

YGL103WYLR333C YML064CYFR031C-A YMR257CYBR188C

YGR117C

YHL015W

YLR407WYGR148CYJL135W

YJL134WYDR174W

YDR024W

YGL123W YML063W

YHL001WYPR104CYGL100WYOR151C

YLR061WYDL191WYMR143W

YHL033C

YOR208WYMR129WYDR284C

YBL079WYNL321W

YGR185C

YFL018C

YDR463W

YIL031WYNR046WYBR211C

YDL193W

YPR176CYDR233CYDL208W

YDL159WYDR195WYDR329C

YDR245W

YEL017WYLR024C

YHR165CYGR252W YOL076WYOR057WYIL048WYKL190WYBR283CYGL194C YDR361C

YKL060CYKL195WYBR212W

YKL179CYCL001W

YCL011C

YLR096W

YFL017W-AYKL019W

YDR404CYPL036W

YLR095CYAL043C-A

YMR061WYPR186C

YFL047W

YCL017C

YCL005W

YDR330W

YPR129W

YMR060C

YKL177W

YHR199C

YNL307C

YLR229C

YMR297W

YML081C-AYBR284W

YJL008CYCL031C

YOL068CYNL149CYJR138WYIL135C

YNL255C

YOR147WYHR064CYKR056W

YAL041WYGR119C

YKL005CYOR262W

YDL189W

YDL122W YDL010W

YDR340W

YLR287C-A

YGL135WYJR147WYDR450WYBL072C

YJR145C

YLR167W

YGL104CYGR149W YPR065W

YLR438C-AYJR146W YLR074C

YLR403WYJL206CYNR018W

YML025C

YPL183W-A

YNL241C

YER079W

YML100W

YFR017C

YOR028C YGL114W

YEL045C

YOR138C

YDR259C YJL101CYMR261C

YLR131C

YJL067W

YLR166C

YOR032C

YDR039C

YLR075W

YBR126C

YKL062W

YLR340W

YIL149C YLR438W YEL044W

YIL148W YLR105C

YOR140W

YEL046C

YKL180WYLR448W

YNL302CYLR185W

YER102WYGR033C

YJL178C

YPL090CYMR242C

YOL039WYPL079W

YPR131C

YHR021W-A YNL162WYOR096W

YJL192C

YBL092W

YHR203C YJL189W

YGR085C YDR025W

YHL016C YIL018WYOL128C

YPR102C

YCR093WYIL094C

YDR394WYKL145W

YNL262W

YNL113WYOR323C

YNL287W

YIL076W

YKR067WYGL245W

YFR051CYOR150WYPR119W

YDR261CYKL008C

YGR234W

YJL158CYNL284C

YHR206WYGL137WYDR238C

YJR137C

YJL148WYJR110W

YBR218CYDL225W

YMR188CYFR040W

YAL036CYKL104CYNR016C

YBR025C

YDL226C

YER078C YOR110WYDR146CYKL007W

YLR174W

YJR109C

YHR018C

YML028W

YGL062W

YBR079C

YMR309C

YER155C

YJR064W

Other Tips

Avoid Data Overload

- Mapping too many attributes makes your visualization awful!

- It is hard to see the overall trend if too many channels are used in a image

Move Label Position

# of Visual Properties is Limited

- Use them effectively - Don’t use too much

in the same view

PRNPHNRNPUL1EDF1

USP11

STMN1

HSP90AB1

SMARCD3

SMARCA4

CCDC88A

SMARCD3TUBB

EP300

CHD8

HTT

OPTN

CLU

MIF4GD

PPARG

PSMD1

MAP4K4

ATP6V1C1

MARCH6

Start from Scratch

- If you are not sure you need the decoration or not, remove it

- Example: Node border, edge arrow

- Even labels are not always required!

“Great Artists Steal…”

Summary

- Learn basic principles of data visualization

- Write a story before creating visualization - What do you want to tell by the diagram?

External Tools

External Tools

- Biological data analysis is not simple! - There is no such thing: one-size-fits-all - Need to understand de-facto standard

tools to save your time

Network Data Analysis

Analysis

Graph Analysis

NetworkX

igraph

Cytoscape

Python

Pandas

NumPy

SciPy

Excel

Visualization

Desktop

Gephi

Cytoscape

matplotlib

Web

Cytoscape.js

sigma.js

d3

NDV3

d3.chart

Google Charts

Data Storage

Graph

Neo4j

GraphXDocument

MongoDB

Relational

MySQL

IPython

3rd Party Apps

NetworkAnalyzer

Data Analysis ToolsAnalysis VisualizationData Preparation

Data Analysis Tools- Languages / Platforms

- R + Bioconductor - Python + Pandas - MATLAB - Excel

- Graph analysis library - igraph - NetworkX

Data Visualization Tools

- Data visualization on web browsers are getting more and more important… - Cytoscape.js - sigma.js - D3.js

- Need more analysis functions - Cytoscape can perform network

analysis interactively, but does not have complete suite of network data analysis tools

- These days, cutting-edge methods and algorithms are implemented in Python

- Easy to implement, yet fast (because of NumPy/SciPy)

- Batch analysis - Visualize in web browsers

Why Multiple Tools?

- Avoid reinventing the wheel - igraph and NetworkX have a lots of

network analysis functions. Why should we repeat it again?

- Collaboration rather than competition - General policy for our project

Why Multiple Tools?

Glue for Applications

- There are two ways to use external tools with Cytoscape

- Common file formats - RESTful API for programatic

access (Ongoing)

- Use popular, standard, widely-used data formats !

- GraphML (Recommended) - CSV/TSV

- Not a format, but easy to process in scripting languages and spreadsheet

File-Base Data Exchange

Realistic Example

- Prepare data in Python - Load data from Bioconductor - Calculate network statistics with

igraph - Export networks and tables in

GraphML format

- Visualize it in Cytoscape

Coming Soon…

- Programatic access to Cytoscape objects and functions via REST - /networks/ID/nodes/NODEID - /apply/layout?network=ID

- We need your opinion!

Communication Bus

NDEx (DB)

Browser

Cytoscape Desktop

Web and Cytoscape

- Prepare/integrate/analyze data with R/Python or traditional desktop applications

- Visualize & publish it as web apps

Trends in Data Visualization

Into The Web…

- Cytoscape is a Java desktop application - Need glue modules to use existing

Cytoscape features from web browsers

New in Cytoscape 3.1.0: Export Networks and Visual Styles to Cytoscape.js Format

JS

Integration to Cytoscape

Cytoscape.js is NOT

- Complete web application - Compatible with Cytoscape Apps - Replacement for Cytoscape

Export to Cytoscape.js Demo

Open Q&A Session

2014 Keiichiro Ono [email protected]

mailto:[email protected]