Pu#ng together a bioinforma1cs team: 2014 compared with 1997

Transcription

1 Pu#ng together a bioinforma1cs team: 2014 compared with 1997 BIG DATA and Healthcare Analy3cs Melbourne, Thursday 3 rd April 2014 Terry Speed, Walter & Eliza Hall Ins3tute of Medical Research 1

2 Overview Why am I here? Sta3s3cians and Big Data My experience 2

3 Why am I here? Why was I invited? Why did I accept? 3

4 Big Data and the US Na1onal Ins1tutes of Health 4

5 What is Big Data for the US NIH? The term 'Big Data' is meant to capture the opportuni3es and challenges facing all biomedical researchers in accessing, managing, analyzing, and integra3ng datasets of diverse data types [e.g., imaging, phenotypic, molecular (including various ' omics'), exposure, health, behavioral, and the many other types of biological and biomedical and behavioral data] that are increasingly larger, more diverse, and more complex, and that exceed the abili3es of currently used approaches to manage and analyze effec3vely. 5

6 What is Big Data for the US NIH? The term 'Big Data' is meant to capture the opportuni3es and challenges facing all biomedical researchers in accessing, managing, analyzing, and integra3ng datasets of diverse data types [e.g., imaging, phenotypic, molecular (including various ' omics'), exposure, health, behavioral, and the many other types of biological and biomedical and behavioral data] that are increasingly larger, more diverse, and more complex, and that exceed the abili3es of currently used approaches to manage and analyze This effec3vely. is exactly what I have been trying to do for the last 15 years 6

7 From NIH s BD2K: Major challenges in using biomedical Big Data include: Loca3ng data and soyware tools. GeZng access to the data and soyware tools. Standardizing data and metadata. Extending policies and prac3ces for data and soyware sharing. Organizing, managing, and processing biomedical Big Data. Developing new methods for analyzing & integra3ng biomedical data. Training researchers who can use biomedical Big Data effec3vely. 7

8 Early 1tles from this conference Big insights into pa3ent flow Iden3fying and predic3ng community care and health needs Enhancing diagnos3cs for invasive Aspergillosis using machine learning Big data from small sites: Crea3ng a primary care data warehouse Spa3o- temporal visualisa3on of disease incidence and respec3ve interven3on strategies 8

9 As I see it, Big Data is about data! 9

10 Sta1s1cians and Big Data 10

11 Big Data isn t new to sta1s1cians Part I Treatment of "Huge'' Data Sets COMPSTAT 1994 US NRC 1998 KDD

12 Buja A, Keller- McNulty S. Introduc3on to the special sec3on on massive datasets. J Comp Graph Sta-st 1999 The area of massive datasets, though currently of great interest to Computa3onal sta3s3cians and to many data analysts, has not yet become part of mainstream sta3s3cal science. However, we see it as an area of ferment that is likely to generate problems of interest to the en3re field and to contribute to the changes in the teaching of sta3s3cs. 2014: It hasn t happened yet! 12

13 The absence of sta1s1cians in Big Data ac1vi1es is striking (to a sta1s1cian) The US NSF convened a working group on Big Data in 2012 of 100 experts: guess how many sta3s3cians? The US NIH BD2K Execu3ve Commikee of 17 has how many sta3s3cians? Conferences and commikees are popping up around the world with no sta3s3cians involved (thanks for the invita3on to come here!) $Ms (and other currencies) are being thrown at Big Data experts (no, this is not envy, it s puzzlement) And so on 13

14 Why? Some first reac1ons Many problems in Big Data are poorly defined, and perhaps we tend to shy away from these, rather than help sharpen the problem Perhaps we prefer to be precisely wrong than approximately right Perhaps we are misunderstood, or, worse, understood We lack the IT (tempted to add: and the marke3ng ) skills Perhaps subject maker considera3ons put an unacceptably high cogni3ve burden on us Many Big Data projects involve large teams Your views please, now or later. 14

15 A 2014 ini1a1ve in Australia Centre of Excellence for Fron-ers in Mathema-cs and Sta-s-cs: Big Data, Big Models, New Insights Centre Director: Professor Peter Hall From the 350 character descrip3on This Centre will create innova3ve mathema3cal and sta3s3cal models for analysing big data sets. The insight gained from analysing these models 15

16 What skills are needed for Big Data? Interpersonal and leadership : access to important problems (team work) Computa3onal (gateway to accessing data, and analysis tools) Theore3cal (analyses under idealized models give insight) Ability to make sense of data: cri3cal thinking and common sense 16

17 Should sta1s1cians have a place at the Big Data table? I think the answer is unequivocally yes! (but then I m a sta3s3cian, and so I would say that) I do think I could argue cogently for that, and may do so a likle panel in the discussion However, we sta3s3cians need to change! See next slide for the 1999 perspec3ve. 17

18 Buja A, Keller- McNulty S. Introduc3on to the special sec3on on massive datasets. J Comp Graph Sta-st 1999 In addi-on, we see two par-cular messages emerging from the following ar-cles: (1) the interdisciplinary nature of problem solu-ons that integrate sta-s-cs, databases and subject maier issues; and (2) the increased computa-onal literacy required of future sta-s-cians. Both points should be noted by young researchers who may do well to educate themselves in new computa-onal tools, as well as in sciences and ins-tu-ons that generate these massive datasets. 18

19 McKinsey Report (2011) Big data: The next fron1er for innova1on, compe11on, and produc1vity. 6. There will be a shortage of talent necessary for organiza3ons to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analy3cal skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effec3ve decisions. What should they learn? Who will teach them? 19

20 Who are the data scien1sts? Sta1s1cians are, and Many others as well, from machine learning, computer science, signal processing, physics, applied mathema3cs, domain sciences and social sciences, ci3zen science, 20

21 My experience building up Bioinforma3cs at the Walter & Eliza Hall Ins3tute of Medical Research 21

22 WEHI Bioinforma1cs research staff : Ini3ally me (who was I?). First student: N, u/g stats and gene3cs; First hire: G, MD PhD 1998 : M, PhD stats/th pop gen; M, PhD mol biol; L, PhD chem; students from comp sci, sci, 1999 : A, PhD gene3cs; 2001 : K, B, C: physics PhDs (all ex- DSTO) 2002 : G, PhD stats; T: PhD bioinf (Germany); J, PhD app maths 2003 : R, PhD stats/th pop gen; T, PhD astrophys; A: PhD astron 2004 : A, MB BS PhD gene3cs 22

23 2005 : S, PhD mol biol; Con1nuing 2006 : A, PhD bioinf (US); T, PhD stat gen US 2007 : S, PhD bioinf (Mon); A, PhD bioinf (Mel); M, PhD gene3cs; M, PhD stats; M, PhD appl maths 2008 : Z, PhD bioinf (China), N, PhD WEHI bioinf 2009 : H, PhD math gen; M, PhD stats 2010 : T, PhD stat gen 2011 : A, PhD app maths 2012 :I, PhD stats 2013 : A, PhD bioinf (US); S, PhD stat gen 23

24 Summary: in our bioinforma1cs division Several each of sta3s3cians, applied mathema3cians, physicists (including astrophysics and astronomy), gene3cists, computer scien3sts, bioinforma3cians (Aus made only ayer 2007) Also, medical graduates (2), molecular biologists (2), chemist A mix, not atypical of interdisciplinary research This is what one might expect of Big Data analysts, with the addi3on of people from the social sciences (esp econ) 24

25 Two final thoughts This is a Golden age of sta3s3cs but not necessarily for sta3s3cians Gerry Hahn Those who ignore sta3s3cs are condemned to re- invent it. Brad Efron Many thanks to Bin Yu of UC Berkeley for sharing her views and material with me. 25