The power of graphs to analyze biological data

the power of graphs for analyzing biological datasets

Davy Suvee

Janssen Pharmaceutica

about me

➡ working as an it lead / software architect @ janssen pharmaceutica• dealing with big scientific data sets

• hands-on expertise in big data and NoSQL technologies

who am i ...

Davy Suvee@DSUVEE

➡ founder of datablend• provide big data and NoSQL consultancy

• share practical knowledge and big data use cases via blog

outline

➡ getting visual insights into big data sets

➡ fluxgraph, a time machine for you graphs ...

★ gene expression clustering (mongodb, Neo4j, Gephi)★ Mutation prevalence (cassandra, Neo4j, Gephi)

insights in big data

➡ typical approach through warehousing★ star schema with fact tables and dimension tables

insights in big data

➡ typical approach through warehousing★ star schema with fact tables and dimension tables

insights in big data

★ real-time visualization★ filtering★ metrics★ layouting★ modular 1, 2

1. http://gephi.org/plugins/neo4j-graph-database-support/ 2. http://github.com/datablend/gephi-blueprints-plugin

gene expression clustering

★ 4.800 samples★ 27.000 genes

➡ oncology data set:

➡ Question:★ for a particular subset of samples, which genes are co-expressed?

mongodb for storing gene expressions{ "_id" : { "$oid" : "4f1fb64a1695629dd9d916e3"} ,  "sample_name" : "122551hp133a21.cel" ,  "genomics_id" : 122551 ,  "sample_id" : 343981 ,  "donor_id" : 143981 ,  "sample_type" : "Tissue" ,  "sample_site" : "Ascending colon" ,  "pathology_category" : "MALIGNANT" ,  "pathology_morphology" : "Adenocarcinoma" ,  "pathology_type" : "Primary malignant neoplasm of colon" ,  "primary_site" : "Colon" ,  "expressions" : [ { "gene" : "X1_at" , "expression" : 5.54217719084415} ,                    { "gene" : "X10_at" , "expression" : 3.92335121981739} ,                    { "gene" : "X100_at" , "expression" : 7.81638155662255} ,                    { "gene" : "X1000_at" , "expression" : 5.44318512260619} ,                     … ]}

pearson correlation through map-reduce

pearson correlation

x y

43 99

21 65

25 79

42 75

57 87

59 81

0,52

co-expression graph

➡ create a node for each gene➡ if correlation between two genes >= 0.8, draw an edge between both nodes

co-expression graph

graphs and time ...

➡ fluxgraph: a blueprints-compatible graph on top of Datomic

➡ make FluxGraph fully time-aware ★ travel your graph through time★ time-scoped iteration of vertices and edges★ temporal graph comparison

➡ towards a time-aware graph ...

➡ reproducible graph state

travel through time

FluxGraph fg = new FluxGraph();

travel through time

FluxGraph fg = new FluxGraph();

Vertex davy = fg.addVertex();davy.setProperty(“name”,”Davy”);

Davy

travel through time

FluxGraph fg = new FluxGraph();

Vertex davy = fg.addVertex();davy.setProperty(“name”,”Davy”);

Davy

Peter

Vertex peter = ...

travel through time

FluxGraph fg = new FluxGraph();

Vertex davy = fg.addVertex();davy.setProperty(“name”,”Davy”);

Michael

Davy

Peter

Vertex peter = ...Vertex michael = ...

travel through time

FluxGraph fg = new FluxGraph();

Vertex davy = fg.addVertex();davy.setProperty(“name”,”Davy”);

Michael

Davy

Peter

Vertex peter = ...Vertex michael = ...

Edge e1 = fg.addEdge(davy, peter,“knows”);

knows

travel through time

Date checkpoint = new Date();

Michael

Davy

Peter

knows

travel through time

Date checkpoint = new Date();

davy.setProperty(“name”,”David”);

Michael

Davy

Peter

knows

travel through time

Date checkpoint = new Date();

davy.setProperty(“name”,”David”);

Michael

Peter

knows

David

travel through time

Date checkpoint = new Date();

davy.setProperty(“name”,”David”);

Michael

Peter

Edge e2 = fg.addEdge(davy, michael,“knows”);

knows

David

knows

travel through time

Michael

Davy

Peter

DavidDavy

Peter

knows

knows

Michael

knows

checkpoint

currenttime

by default

travel through time

Michael

Davy

Peter

DavidDavy

Peter

knows

knows

Michael

knows

checkpoint

currenttime

fg.setCheckpointTime(checkpoint);

tcurrrentt3t2

time-scoped iteration

change change change

Davy’’’Davy’ Davy’’

t1

Davy

➡ how to find the version of the vertex you are interested in?

next next next

previouspreviousprevious

tcurrrentt3t2

time-scoped iteration

Davy’’’Davy’ Davy’’

t1

Davy

next next next

previouspreviousprevious

tcurrrentt3t2

time-scoped iteration

Davy’’’Davy’ Davy’’

t1

Davy

Vertex previousDavy = davy.getPreviousVersion();

next next next

previouspreviousprevious

tcurrrentt3t2

time-scoped iteration

Davy’’’Davy’ Davy’’

t1

Davy

Vertex previousDavy = davy.getPreviousVersion();Iterable<Vertex> allDavy = davy.getNextVersions();

next next next

previouspreviousprevious

tcurrrentt3t2

time-scoped iteration

Davy’’’Davy’ Davy’’

t1

Davy

Vertex previousDavy = davy.getPreviousVersion();Iterable<Vertex> allDavy = davy.getNextVersions();

Iterable<Vertex> selDavy = davy.getPreviousVersions(filter);

next next next

previouspreviousprevious

tcurrrentt3t2

time-scoped iteration

Davy’’’Davy’ Davy’’

t1

Davy

Vertex previousDavy = davy.getPreviousVersion();Iterable<Vertex> allDavy = davy.getNextVersions();

Iterable<Vertex> selDavy = davy.getPreviousVersions(filter);Interval valid = davy.getTimerInterval();

time-scoped iteration

➡ vertex:★ setting or removing a property ★ add or remove it from an edge★ being removed

➡ When does an element change?

time-scoped iteration

➡ vertex:★ setting or removing a property ★ add or remove it from an edge★ being removed

➡ When does an element change?

➡ edge:★ setting or removing a property ★ being removed

time-scoped iteration

➡ vertex:★ setting or removing a property ★ add or remove it from an edge★ being removed

➡ When does an element change?

➡ edge:★ setting or removing a property ★ being removed

➡ ... and each element is time-scoped!

MichaelMichael

Davy

Peter

David Davy

Peter

temporal graph comparison

knows

knows

knows

current checkpoint

what changed?

temporal graph comparison

➡ difference (A , B) = union (A , B) - B

➡ ... as a (immutable) graph!

temporal graph comparison

➡ difference (A , B) = union (A , B) - B

➡ ... as a (immutable) graph!

difference ( , ) =

David

knows

t3t2t1

use case: longitudinal patient data

patient patient

smoking

patient

smoking

t4

patient

cancer

t5

patient

cancer

death

use case: longitudinal patient data

➡ historical data for 15.000 patients over a period of 10 years (2001- 2010)

use case: longitudinal patient data

➡ historical data for 15.000 patients over a period of 10 years (2001- 2010)

➡ example analysis: ★ if a male patient is no longer smoking in 2005★ what are the chances of getting lung cancer in 2010, comparing

patients that smoked before 2005

patients that never smoked

use case: longitudinal patient data

➡ get all male non-smokers in 2005

fg.setCheckpointTime(new DateTime(2005,12,31).toDate());

use case: longitudinal patient data

➡ get all male non-smokers in 2005

fg.setCheckpointTime(new DateTime(2005,12,31).toDate());

Iterator<Vertex> males = fg.getVertices("gender", "male").iterator()

use case: longitudinal patient data

➡ get all male non-smokers in 2005

fg.setCheckpointTime(new DateTime(2005,12,31).toDate());

Iterator<Vertex> males = fg.getVertices("gender", "male").iterator()

while (males.hasNext()) { Vertex p2005 = males.next(); boolean smoking2005 = p2005.getEdges(OUT,"smokingStatus").iterator().hasNext();}

use case: longitudinal patient data

boolean smokingBefore2005 = ((FluxVertex)p2005).getPreviousVersions(new TimeAwareFilter() { public TimeAwareElement filter(TimeAwareVertex element) { return element.getEdges(OUT, "smokingStatus").iterator().hasNext() ? element : null; }

}).iterator().hasNext();

➡ which patients were smoking before 2005?

use case: longitudinal patient data

Graph g = fg.difference(smokerws, time2010.toDate(), time2005.toDate());

➡ which patients have cancer in 2010

working set of smokers

use case: longitudinal patient data

Graph g = fg.difference(smokerws, time2010.toDate(), time2005.toDate());

➡ which patients have cancer in 2010

working set of smokers

➡ extract the patients that have an edge to the cancer node

Questions?