Home

Welcome to the website of Kim-Anh Lê Cao’s lab group at Melbourne Integrative Genomics and the School of Mathematics and Statistics, University of Melbourne!

Our lab focuses on the development of computational methods, their applications in areas informed by biology, and the training of the new generation of computational biologists and data analysts. Our area of expertise is in the integration of biological ‘omics data (transcriptomics, proteomics, metabolomics etc., as well as microbiome metagenomics and lastly single cell transcriptomics) with multivariate and dimension reduction methodologies, selection of features of biomarkers in large biological data sets and R software development. Our group provides critical collaborative expertise to biologists, bioinformaticians, statisticians and clinicians and welcomes budding data analysts.

Our aim is to broadly enable scientific progress well beyond statistical development itself. We value creative thinking in statistical methodological development to address critical challenges arising from high throughput biological research.

More news about (workshops and updates): www.mixOmics.org

Lab head: Dr Kim-Anh Lê Cao
Snr Lecturer, NHMRC Career Development Fellow
Melbourne Integrative Genomics (MIG) & School of Mathematics and Statistics
Building 184 ground floor | University of Melbourne | Parkville VIC 3010

@: kimanh.lecao[ at ]unimelb.edu.au | twitter: mixOmics_team | Ph: +61 3 8344 3971

News

Click on this link to read all our News

Our lab specialises computational methods and software developments, as well as the application of our methods and tools to biological data sets generated by our collaborators.

Data integration methods using multivariate projection-based methodologies

Our dimension reduction methods are based on the Projection to Latent Structures algorithm (PLS, a term we prefer to Partial Least Squares regression, Wold et al. 2001) that are combined with LASSO regularization to identify important biological features or biomarkers in large-scale biological data sets. Our latest frameworks include DIABLO (Singh et al. 2019) to integration multiple data sets measured on the same N samples (N-integration); MINT (Rohart et al. 2017a) to integrate independent studies measured on the same P variables / genes (P-integration) and mixMC (Lê Cao et al. 2016) for the multivariate analysis of microbial communities, timeOmics (Bodein et al. 2019) to integrate microbiome and ‘omics time course data.

We are interested in developing new multivariate methodologies to

integrate multi-omics single cell data (scNM&T-seq in particular)
integration multi-omics time course data
integrate genotype (SNP) data

mixOmics R toolkit package (www.mixOmics.org)

mixOmics is one of the few R package dedicated to the integration of multiple ‘omics data (19 novel methodologies implemented so far, amongst which 13 were developed by our lab) and with an increasing uptake from the research community. The package has been downloaded > 67K times in 2019. Programming developments are on-going for interactive web interfaces, and efficient programming for large-scale studies. Check our our recent publication (Rohart et al. 2017b) and and 50-min webinar overview about this project. The mixOmics team run multiple day workshops for an introduction to multivariate projection-based methods for data integration using mixOmics, see our website www.mixOmics.org for news and tutorials.

Multivariate methods for microbiome studies

There are major statistical and computational challenges in analysing microbial communities that currently hinder the potential of microbiome research to substantially advance biomedical understanding. We are currently expanding mixMC to better characterise and understand important microbiome-host interactions. Some of our methods developments aim at addressing batch effects in microbiome experiments and analyse scarce temporal sampling in time course studies.

We analyse microbiome datasets from our collaborators for a wide range of studies, including investigating the role of gut and oral microbiome in spondyloarthropathy diseases, the development of intestinal or salivary microbiota in toddlers and infants, investigating the gut-brain crosstalk in Huntington’s disease.

Current members

Staff members

Aleksandar Dakic – senior postdoctoral fellow, genomics data analyst, in collaboration with A/Prof Jess Mar (AIBN, UQ) and Prof Christine Wells (Centre for Stem Cell Systems)
Al J Abadi – postdoctoral fellow, software developer and computational statistics
[we are looking for a senior postdoctoral fellow in computational statistics and genomics! contact us for more details]

Higher Degree Research students

Susie Ellul, PhD candidate ‘Multivariate methods for the analysis of longitudinal microbiome data‘, UoM
Isaac Virshup, PhD candidate ‘Finding patterns of biologically meaningful transcript expression by examining heterogenous sets of cells’, UoM with main supervisor Prof Christine Wells
Eva Yiwen Wang, PhD candidate ‘Development of multivariate and integrative statistical methods to improve microbiome research outputs‘, UoM
Sibi Xue, Msc Statistics by coursework, UoM
Yinghua Shen, Msc Statistics by coursework, UoM

We welcome any students and staff who are interested in statistical analysis of omics data and wish to attend our fortnight group meetings!

Alumni staff

Florian Rohart – now data analyst at NTI
Nicholas Matigian – now data analyst at QFAB Bioinformatics
Benoit Gautier – now teacher in mathematics in France.

Alumni students (PhD)

Aimee Hanson, PhD candidate ‘Lymphocyte receptors: Genomic structure and role in immune- mediated arthritis’ with main supervisor Prof Matt Brown (QUT) and Diamantina Institute, Faculty of Medicine, University of Queensland
Farah Syeda Zahir, PhD candidate ‘Obesity paradox: Exploring the relationship between adiposity and mortality in persons with Cardiovascular Disease and/or Type 2 Diabetes Mellitus’, co-supervised with Dr Ahmed Medi (Diamantina Institute), School of public Health, University of Queensland.
Jasmin Straube ‘Development of statistical tools for integrating time course ‘omics’ data’ with co-supervisors Dr Emma Huang and Dr Anne Bernard, QFAB and University of Queensland.
Ralph Patrick ‘Molecular interaction motifs in a system-wide network context: Computationally charting transient kinase-substrate phosphorylation events’ with main supervisor A/Prof Mikael Boden, University of Queensland.
Amrit Singh ‘Blood biomarker panels of the late phase asthmatic response’ with main supervisor Prof Scott Tebbutt, University of British Columbia, Vancouver, Canada.
Chao Liu ‘Computational analysis of DNA repair pathways in breast cancer’ with main supervisor Prof Mark Ragan, Institute for Molecular Bioscience, University of Queensland

Alumni students (Honours and Msc)

Alana Butler, Master of Science (Bioinformatics), UoM, now research assistant at Monash University.
Nicholas d’Arcy, Nicholas Mueller, University of Queensland
Solange Pruilh, Zoe Welham, Vanessa Lakis, Priscilla Montfalet, Thom Cuddihy, Mourad Larbi, Jeff Coquery, Pierre Monget who did a research placement in our lab.

Conventional publications

Publications are listed here. We are fervent advocates of open science and open data, with some manuscripts hosted in bioRxiv, and all R codes and scripts on our mixOmics page.

Fun publications

(refereed by editorial board)

Huang BE, Clifford D and Lê Cao K-A (2014). The surprising benefit of passive-aggressive behaviour at Christmas parties: being crowned king of the crackers. Medical Journal of Australia 201(11):694-6 (Christmas issue, awarded first prize, radio interview from ABC Darwin, mentioned in the podcast from Two Shrink Pod (episode 21, Dec 2017).

Clifford D, Lê Cao K-A and Huang BE (2014). The statistician’s guide to a cracking good Christmas party. Significance 11(5):44-7 (Christmas issue, doi: 10.1111/j.1740- 9713.2014.00784.x).

Awards and fellowships

2019 – 2022 Career Development Fellowship (CDF2) from the National Health and Medical Council Research (NHMRC) ‘Microbiome biomarkers of human disease: novel computational methods to facilitate therapeutic developments’, $483K.

2019 Moran medal from the Australian Academy of Science for contribution in the past 10 years in Statistical sciences in Australia (early-career, biennial)

2015 – 2019 Career Development Fellowship (CDF1) from the National Health and Medical Council Research (NHMRC) ‘Development of statistical methodologies and application to clinical cancer studies’, $419K.

2009 Laurent-Duhamel triennial prize from the French Statistical Society for PhD thesis in Applied Statistics, Bordeaux, France.

Current funding (UoM)

2018 – 2019 Silicon Valley Community Foundation, HCA2-A-1708-02277, Multivariate computational methods for data integration of single cell assays. Role: CIA. $132K

2018 UoM Computational Biology Research Initiative seed funding. Towards the understanding of gut-brain crosstalk in Huntington’s disease. Role: CIA. $20K.

2018 – 2021 NHMRC Project Grant, GNT1142456. Enhancing host defence mechanisms in severe bacterial infections. Dr A Blumenthal, Prof B Venkatesh, Prof D Evans, Dr K-A Lê Cao, Prof G Ulett, A/Prof J Cohen. Role: CID. $837K

2018 – 2021 NHMRC Project Grant. GNT1144941. Understanding how azithromycin prevents exacerbations in severe asthma. Prof J Upham, Prof J Simpson, Dr K Baines, Dr K- A Lê Cao. Role: CID. $698K

2018 – 2019 ARC Special Research Initiative in Stem cells Centre of Excellence, Stem Cells Australia led by Prof M Little (UoM). Role: co-CI. $3M, 1 research fellow in Lê Cao group.

Patents

The application of our methods and software has directly resulted in four biomedical patents.

Gandhi M, Keane C, Lê Cao K-A, Vari F (2015). A method of assessing prognosis of lymphoma. WO/2016/134416. Priority 23/02/2016
Thomas R, Mehdi A, Lê Cao K-A (2014). Kits and methods for the diagnosis, treatment, prevention and monitoring of diabetes. PCT/AU2014/050415. Priority 18/06/2015
Hill M, Shah A, Lê Cao K-A (2014). Blood Test for Throat Cancer. WO/2016/077881. Priority 17/11/2015
Musso O, Desert R, Rohart F, Lê Cao K-A. Method for predicting the survival time of a patient suffering from hepatocellular Carcinoma. EP17305436.2. Priority 12/04/2017

Past funding (University of Queensland)

2016 Translational Research Institute SPORE grant, Obesity-induced Barrett’s oesophagus and associated cancer: mechanisms and diagnostic tools. A/Prof M. Hill, Dr A. Barbour, Dr K-A. Lê Cao (CIC). $100K

2016 Translational Research Institute SPORE grant, Towards biomarkers for patient stratification in sepsis, Dr A. Blumenthal, Prof B. Venkatesh, A/Prof J Cohen, Dr K-A. Lê Cao, Dr D. Vagenas, Prof I. Frazer (CID). $80K

2014 – 2015 The Juvenile Diabetes Research Foundation (JDRF), 2-SRA-2015-306-Q-R, A genetic link between gut microbial flora and T1D susceptibility. Dr D. Zipris (University of Colorado) and co-CI from UQDI: Dr E. Hamilton-Williams, Dr J. Mullaney, A/Prof M. Hill, Dr K-A. Lê Cao (PI). $500K

2014 The Juvenile Diabetes Research Foundation (JDRF), 1-PNF-2014-153-A-V, Risk of diabetes progression in at-risk subjects with metabolic and inflammatory signatures. Prof R. Thomas (UQDI), K-A. Lê Cao et al. (PI). $110K

2014 UQ Major Equipment and Infrastructure, 2014000102, High throughput gene expression of patient samples via the Nanostring nCounter system.  Prof M. Gandhi and 9 co-CI from UQDI, K-A. Lê Cao (CIJ). $169K

2014 – 2016 NHMRC Project Grants Funding, APP1058993, Blood biomarkers in Hodgkin Lymphoma. Prof M. Gandhi, Prof M. Fulham, A/Prof J. Trotman, Dr K-A. Lê Cao, Dr L. Berkahn. (CID). $513K

2013 – 2015 ARC Discovery Project, DP130100777. The Stemformatics gene expression compendium: development of multivariate statistical approaches for cross platform analyses. A/Prof C. Wells, Dr K-A. Lê Cao (CIB). $269K, shared postdoctoral fellow.

Our lab aims to inspire younger generations of budding statisticians, data analysts and computational biologists to advance the field of computational biostatistics.

All our members use GitHub and thrive for reproducible research, see:

https://github.com/ajabadi

https://github.com/EvaYiwenWang

https://github.com/abodein/timeOmics

https://github.com/ivirshup

https://github.com/mixOmicsTeam

Future

We are preparing a handbook about multivariate projection-based methods and how to apply them (using mixOmics!) to integrate biological data. Stay tuned!

Current

2019 – We have developed a 16-week online course opened for University of Melbourne students called ‘Data fundamentals’ with Dr Sue Finch (Statistical Consulting Centre, School of Mathematics and Statistics). The course is opened every trimester. Have a look at this page if you wish to register, it is a fun course to learn how to work with data.

Since 2014 – We teach specialised workshops to introduce key concepts in multivariate statistics, with applications using the R software mixOmics. Our mixOmics web page provides numerous tutorials to apply the different multivariate integrative methods implemented in mixOmics.

Past

We taught introductory statistics ‘Statistics for frightened bioresearchers’ lecture materials can be found here.

Below is a list of opportunities in our lab, including undergraduate and postgraduate research projects and scientific visits.

Positions

We are looking for self-motivated candidates in the field of computational statistics applied to high-throughput biological data, as data analyst and software developer. We are also looking for a long term (2 + 2years) senior postdoc with opportunities for teaching as well as statistical methods development. Contact us!

Students

We welcome undergraduate, hons/Msc and PhD students willing to be part of the group to apply our methods to specific biological problems, or develop innovative computational methods at the forefront of ‘omics and microbiome data integration. There are plenty of projects to choose from our research themes and cross-discipline projects. Some are listed in here.

Visiting scientists

We welcome wet-lab researchers and assist them in acquiring the necessary skillsets to analyse their own data with our tools, and dry-lab researchers to collaborate on our many exciting projects.

Dr Olivier Chapleur and Ms Laetitia Cardona stayed for 10 and 3 weeks with us in 2017. Here is a brief description of the work they undertook with us, and their feedbacks.

Dr Sébastien Déjean stayed for 5 weeks with us in July 2018 and helped run a mixOmics workshop.

Prof Malu Calle Rosingana visited us for 4 weeks in January 2019.

Stijn Hawinkel, PhD candidate in Prof Olivier Thas (Ghent University) visited us for 3 months (March – May 2019).

Dr Olivier Chapleur and Ms Laetitia Cardona were back for 4 and 6 weeks from April 2019! they gave us a hand for our upcoming mixOmics workshop focusing on microbiome data analysis.

Mr Attila Csala, PhD candidate in in Prof Aeilko Zwinderman (University of Amsterdam) will visit us for 4 months (Nov 2019 – March 2020)

@: kimanh.lecao[ at ]unimelb.edu.au

Ph: +61 3 8344 3971

We are located at:

Melbourne Integrative Genomics | Old microbiology building 184 ground floor | The University of Melbourne

Entrance is through Royal Parade, approximately at 30 Royal Parade (see map), next to the Kenneth Meyer building (Tram Route 19, Stop no. 11 from the city centre).

There is a phone in the reception area, with the contact numbers. Give us a buzz then (and if we don’t answer, call Andrew or Bobbie).

Lê Cao Lab