The power of graphs to analyze biological data

the power of graphs for analyzing biological datasets Davy Suvee Janssen Pharmaceutica

about me who am i ... ➡ working as an it lead / software architect @ janssen pharmaceutica • dealing with big scientific data sets • hands-on expertise in big data and NoSQL technologies ➡ founder of datablend • provide big data and NoSQL consultancy Davy Suvee • share practical knowledge and big data use cases via blog @DSUVEE

outline➡ getting visual insights into big data sets ★ gene expression clustering (mongodb, Neo4j, Gephi) ★ Mutation prevalence (cassandra, Neo4j, Gephi)➡ fluxgraph, a time machine for you graphs ...

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 1, 2 ★ modular1. http://gephi.org/plugins/neo4j-graph-database-support/ 2. http://github.com/datablend/gephi-blueprints-plugin

gene expression clustering ➡ oncology data set: ★ 4.800 samples ★ 27.000 genes ➡ 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 x ypearson correlation 43 99 21 65 25 79 0,52 42 75 57 87 59 81

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 ...➡ reproducible graph state➡ towards a time-aware graph ...➡ 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

travel through timeFluxGraph fg = new FluxGraph();

travel through timeFluxGraph fg = new FluxGraph(); DavyVertex davy = fg.addVertex();davy.setProperty(“name”,”Davy”);

travel through timeFluxGraph fg = new FluxGraph(); DavyVertex davy = fg.addVertex();davy.setProperty(“name”,”Davy”); PeterVertex peter = ...

travel through timeFluxGraph fg = new FluxGraph(); DavyVertex davy = fg.addVertex();davy.setProperty(“name”,”Davy”); PeterVertex peter = ...Vertex michael = ... Michael

travel through timeFluxGraph fg = new FluxGraph(); Davy kn owVertex davy = fg.addVertex(); sdavy.setProperty(“name”,”Davy”); PeterVertex peter = ...Vertex michael = ...Edge e1 = Michael fg.addEdge(davy, peter,“knows”);

travel through time DavyDate checkpoint = new Date(); kn ow s Peter Michael

travel through time DavyDate checkpoint = new Date(); kn ow sdavy.setProperty(“name”,”David”); Peter Michael

travel through time DavidDate checkpoint = new Date(); kn ow sdavy.setProperty(“name”,”David”); Peter Michael

travel through time DavidDate checkpoint = new Date(); kn ow sdavy.setProperty(“name”,”David”); Peter knEdge e2 = ow fg.addEdge(davy, michael,“knows”); s Michael

travel through time by defaulttime kn Davy ow David Davy s kn ow checkpoint s current Peter Peter kn ow s Michael Michael

travel through timetime kn Davy ow David Davy s kn ow checkpoint s current Peter Peter kn ow s Michael Michael fg.setCheckpointTime(checkpoint);

time-scoped iteration t1 t2 t3 tcurrrent change change change Davy Davy’ Davy’’ Davy’’’ ➡ how to find the version of the vertex you are interested in?

time-scoped iteration t1 t2 t3 tcurrrent next next next Davy Davy’ Davy’’ Davy’’’ previous previous previous

time-scoped iteration t1 t2 t3 tcurrrent next next next Davy Davy’ Davy’’ Davy’’’ previous previous previousVertex previousDavy = davy.getPreviousVersion();

time-scoped iteration t1 t2 t3 tcurrrent next next next Davy Davy’ Davy’’ Davy’’’ previous previous previous Vertex previousDavy = davy.getPreviousVersion();Iterable<Vertex> allDavy = davy.getNextVersions();

time-scoped iteration t1 t2 t3 tcurrrent next next next Davy Davy’ Davy’’ Davy’’’ previous previous previous Vertex previousDavy = davy.getPreviousVersion(); Iterable<Vertex> allDavy = davy.getNextVersions();Iterable<Vertex> selDavy = davy.getPreviousVersions(filter);

time-scoped iteration t1 t2 t3 tcurrrent next next next Davy Davy’ Davy’’ Davy’’’ previous previous previous Vertex previousDavy = davy.getPreviousVersion(); Iterable<Vertex> allDavy = davy.getNextVersions();Iterable<Vertex> selDavy = davy.getPreviousVersions(filter); Interval valid = davy.getTimerInterval();

time-scoped iteration➡ When does an element change?➡ vertex: ★ setting or removing a property ★ add or remove it from an edge ★ being removed

time-scoped iteration➡ When does an element change?➡ vertex: ➡ edge: ★ setting or removing a property ★ setting or removing a property ★ add or remove it from an edge ★ being removed ★ being removed

time-scoped iteration➡ When does an element change?➡ vertex: ➡ edge: ★ setting or removing a property ★ setting or removing a property ★ add or remove it from an edge ★ being removed ★ being removed➡ ... and each element is time-scoped!

temporal graph comparisonDavidDavy Davy kn kn ow ow s s Peter what changed? Peterkn ow s Michael Michael current checkpoint

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! David difference ( , )= kn ow s

use case: longitudinal patient data t1 t2 t3 t4 t5 smoking smoking deathpatient patient patient patient patient cancer cancer

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

use case: longitudinal patient data➡ get all male non-smokers in 2005fg.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 2005fg.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➡ which patients were smoking before 2005?boolean smokingBefore2005 = ((FluxVertex)p2005).getPreviousVersions(new TimeAwareFilter() { public TimeAwareElement filter(TimeAwareVertex element) { return element.getEdges(OUT, "smokingStatus").iterator().hasNext() ? element : null; } }).iterator().hasNext();

use case: longitudinal patient data➡ which patients have cancer in 2010 working set of smokers Graph g = fg.difference(smokerws, time2010.toDate(), time2005.toDate());

use case: longitudinal patient data➡ which patients have cancer in 2010 working set of smokers Graph g = fg.difference(smokerws, time2010.toDate(), time2005.toDate());➡ extract the patients that have an edge to the cancer node

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The power of graphs to analyze biological data

by datablend on Nov 12, 2012

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