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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
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Single cell benchmark manuscript on bioRxiv
The CellBench data are out and we benchmarked a wide variety of methods for single cell transcriptomics data analysis. More details at this link. This work is in close collaboration with Luyi Tian and Dr Matt Ritchie at WEHI. What a great resource of data for the computational biology community in single cell!
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mixOmics workshop Nov 6, Vancouver, Canada
Our first mixOmics workshop with a special focus on microbiome data analysis will take place in Vancouver. The event is organised by the Microbiome Research Network, University of British Columbia, as part of their symposium 'Exploring the Microcosmos '. More details here.
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Masterclass, School of Mathematics and Statistics, Oct 10, 12-1pm (Evan Williams) , UoM
'Introduction to visualization for small and big data' by Kim-Anh Le Cao, School of Mathematics and Statistics, Oct 10 12-1pm (Evan Williams), University of Melbourne, Melbourne, Australia. ‘A picture is worth a thousand words’. Graphical displays are powerful ways of conveying a message or a story about the data we wish to explore or analyse. In this masterclass, I will give some …
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Latrobe University, Oct 5, 12-1pm
'Multivariate data integration of omics data', by Kim-Anh Lê Cao, Department of Mathematics and Statistics,Oct 5 2018, Melbourne, Australia.
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Kim-Anh selected for the Homeward Bound project 4
Kim-Anh Lê Cao is amongst the seven University of Melbourne staff members and associates have been selected to participate in the Homeward Bound global leadership initiative for women in STEMM (Science, Technology, Engineering, Mathematics and Medicine), more details here and more news soon about this inspiring initiative for women in STEMM!
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Murdoch Children’s Research Institute
'Navigating through ‘omics data: a multivariate perspective', by Kim-Anh Lê Cao LifeCourse seminar series, Sept 20 2018 Murdoch Children's Research Institute, Melbourne, Australia
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2nd Postdoctoral Methods Symposium at WEHI
'Combining independent single cell RNA-seq studies using a component-based approach' by Al Abadi (poster), 2nd Postdoctoral Methods Symposium, The Walter and Eliza Hall Institute, Melbourne Australia.
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.
Development of 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. in prep) 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.
Specifically, we are currently developing novel multivariate methodologies to
- integrate multiple ‘omics time course data
- integrate genotype (SNP) data
Development of the 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 > 29K times in 2017, (R CRAN package download logs). 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 a poster that gives an overview of this large 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.
Development and application of 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
- Dr Zitong Li – senior postdoctoral fellow, statistical genomics.
Higher Degree Research students
- José Antonio Férez Rubio, PhD candidate ‘Computational analysis of gut microbiota-host interactions‘. University of San Antonio Catholic University with main supervisor Dr Vicente Navarro Lopez
- 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
- 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.
- Alana Butler, Master of Science (Bioinformatics), UoM
Visiting members who participate in our lab meeting (anyone welcome)
- Dr Amy Loughman, Research Fellow, Deakin University
- Ms Geraldine Kong, PhD student, Hannan Lab (Florey institute)
Alumni staff
- Florian Rohart – postdoctoral fellow, applied statistician, University of Queensland
- Nicholas Matigian – data analyst, University of Queensland
- Benoit Gautier – statistician, University of Queensland
Alumni students (PhD)
- 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)
- Nicholas d’Arcy, Nicholas Mueller, University of Queensland
- Solange Pruilh, Zoe Welham, Vanessa Lakis, Priscilla Montfalet, Thom Cuddihy, Mourad Larbi, Jeff Coquery, Pierre Monget
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.
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.
Future
We are currently preparing 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).
Current
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. 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.
@: 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 to give us a buzz.
