Our next round of online learning will be 31st October 2022!
Some feedback from this course:
Overall, this was a great course to take and I wish there would be more stats courses for biologists. Kim Anh is truly an amazing tutor. Approachable and knowledgeable. I am very happy that at the end of the course I can say that I am at a position when I feel confident to start applying the learn concepts. Definitely worth all my time and efforts! Thank you!
What were the best aspects of the program?
The practical tutorials and vignettes
I really enjoyed that the concept were well explained even for people not familiar with the methods. I liked the assignment part.
Self paced program, very focused content, easy to deep in it, very very useful the case studies provided, appropriate examples and detailed explanation; Curriculum design from easy to complicated
It has extremely useful online materials, Q&A session. I finally learnt a bit about mixomics and got some hands on practice
The content was great. I appreciate having access to content for a bit longer.
I enjoyed overall organization with: – online courses which enables us to learn at our own pace with theory and practice, – live Q&As beginning with a short summary – a slack channel for day to day questions The utilization of Slack to exchange through the program was really appreciated. I also really liked the weekly webinar.
After more than 230 days in lockdown in Melbourne, we are finally announcing our new self paced course ‘mixOmics R Essentials for Biological Data Integration‘!
Teaching Period Dates (AEST), asynchronised:
Start – Monday, 11th October 2021
End – Sunday, 7th November 2021
(non marked) Assessment due Sunday, 14th November 2021
Peer-review of assessment due Sunday, 21st November 2021
Live Q&A:
Thursday 14th October 5:30pm AEST
Thursday 21st October 5:30pm AEST
Thursday 28th October 5:30pm AEST
Thursday 4th November 5:30pm AEST
Fees vary for
Research Higher Degree students enrolled in a University: $495 AUD (incl. GST)
Staff and members from Universities & Not-for-profit organisations: $825 (incl. GST)
Prerequisites. A good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) will enable learners to fully benefit from the course. The course is divided into theory (50%) and hands-on practice, with the opportunity to analyse your own data. Participants are encouraged to use RStudio and Rmarkdown (template and R code provided).
Unfortunately the workshop planned face to face on 28-30 June 2021 will not go ahead because of a Melbourne lockdown that threatens the NZ bubble. Anticipated new dates are 26-29 October. For those who responded to our survey, we have saved your answers for later. For those interested to join in (preferably face to face), you can fill the survey.
For participants who had planned to join remotely, an online course is likely to be developed and open from October 2021. It might provide a better experience than a talking head on zoom! Information will be posted on our website.
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We are thrilled to announce that we are resuming our workshops! This time we will attempt to leverage the Australia-NZ bubble for a face to face workshop, hosted at Massey University, Palmerston North with co-organiser Dr Matthieu Vignes*.
Fill the survey so that you can register your interest and needs for this workshop. We can only allow a limited number of participants, so lock in those dates already in your calendar before we can confirm your participation! Priority will be given to postgraduate students and early career researchers. Results will be announced to the participants with details for registration on June 8.
*this workshop might be cancelled last minute due to travel restrictions AUS/NZ.
Context. Advances in high-throughput technologies have transformed the way we examine molecular information, including microbial communities. However, analytical tool development is critically trailing behind data generation, which hinders the analysis, understanding or integration of microbiome data with other types of molecular data. Data integration adopt a holistic, data-driven and hypothesis-free approach. This new approach is necessary to understand the role of biological systems and posit new hypotheses.
The workshop will introduce concepts of multivariate dimension methods developed in mixOmics for statistical analysis. Our methods make no distributional assumptions, are highly flexible for unsupervised (exploratory), supervised (classification) and integration analyses. Various analytical frameworks will be presented ranging from data exploration, selection of markers, integration with other omics datasets and introduction to time-course analysis. There will be an opportunity also to talk about the analysis of microbiome data.
Each methodology will be illustrated on real biological studies. The third day is ‘BYO data’ day where you can reinforce your learnings on your own study! The workshop will cover general omics data integration concepts with appropriate case studies.
Organized and hosted by: Massey University, Palmerston North
Indicative fees for 3 days are NZD490+GST for RHD students, NZD810+GST for Universities and CRIs/non-profit organisations, NZD1290+GST for other industries. We will seek sponsors to support the registration costs for 4 RHD students (application via our survey).
Registrations fees include coffee breaks, lunch, lecture notes and electronic material (slides, R code, data).
Registration Fill the survey and lock the dates in your calendar! As we have a limited number of participants (30), priority will be given to postgraduate students and early career researchers. Results will be announced to the participants with details for registration on June 8. Online attendance is also available for a limited number of participants (but with reduced opportunities for interactions).
Prerequisite and requirements. We require from the trainees a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to fully benefit from the workshop. Participants are requested to bring their own laptop, having installed the software RStudio http://www.rstudio.com/and the R package mixOmics (instructions will be provided prior to the training).
Outline
Day 1 & 2: methods and hands-on. The following broad topics will be covered.
A. Key methodologies in mixOmics and their variants:
Basic processing of count data (scaling, how to handle compositional data)
Exploration of one data set and how to estimate missing values
Identification of a microbial signature to discriminate different treatment groups
Integration of two data sets and identification of microbial markers
Introduction to repeated measurements or longitudinal studies analysis
How to deal with batch effects
Integration of more than two data sets to identify multi omics signatures (if sufficient interest)
Integration of independent but related studies (optional)
B. Review on the graphical outputs implemented in mixOmics
Sample plot representation
Variable plot representation for data integration
Other useful graphical outputs
C. Case studies and applications
Several omics studies (and microbiome if there is some interest) will be analysed using the methods presented above.
Day 3: bring your own data. Participants will be given the opportunity to analyse their own data under the guidance and the advice of the three instructors. Participants can also work in a team. Some data sets will also be provided for those unable to bring their own data.
The following statistical concepts will be introduced: covariance and correlation, multiple linear regression, classification and prediction, cross-validation, selection of markers, penalised regressions. Each methodology will be illustrated on a case study (theory and application will alternate).
Target group The course is intended for microbiologists working in the fields of bioinformatics, computational biology and applied statistics with some statistical knowledge and a good working knowledge in R. It will be particularly useful to those interested in:
Exploring data sets.
Selecting molecular / microbial features with methods implementing LASSO-based penalisations.
Using graphical techniques to better visualise data.
Understanding and/or applying multivariate projection methodologies to large data sets.
Anticipated learning outcomes After completion of this workshop, participants will be able to
Understand fundamental principles of multivariate projection-based dimension reduction technique.
Perform statistical integration and feature selection using recently developed multivariate methodologies.
Apply those methods to high throughput microbiome studies, including their own studies.
The latest version includes some recent updates (also covered in the other webinars in more details – check them out!)
The slides are opened to the community, but don’t forget to acknowledge the presenter if you are re-using the slides.
Multi-omics data (eg. transcriptomics, proteomics) collected from the same set of biospecimens or individuals is a powerful way to understand the underlying molecular mechanisms of a biological system.
mixOmics, a popular R package, integrates omics data from a wide range of sources into a single, unified view making it easier to explore and reveal interactions between omics layers. It overcomes many of the challenges of multi-omic data integration arising from data that are complex and large, with few samples (<50) and many molecules (>10,000), and generated using different technologies.
Prof Kim-Anh Lê Cao, head of the mixOmics team, is delivering this webinar to outline the different methods implemented in mixOmics and how statistical data integration is defined in this context. She will demonstrate how these approaches are applied to analysis of different multi-omics studies and outline the latest methodological developments in this area. From a study of human newborns, to multi-omics microbiomes, and multi-omics in single cells, these examples illustrate how mixOmics is used to perform variable selection and identify a signature of omics markers that characterise a specific phenotype or disease status.
Who the webinar is for:This webinar is for life scientists, bioinformaticians and anyone with an interest in exploration and integration of multiomics biological datasets.
Topics covered: omics data statistical integration, introduction to matrix factorisation techniques, applications of DIABLO and MINT frameworks for bulk or single cell assays, extensions.
The slides are opened to the community, but don’t forget to acknowledge the presenter if you are re-using the slides.
The following workshops will be advertised soon. Some are open to any scientists from any institution. This page will be updated as time evolves (we usually advertise our workshops ~ 4 months in advance).
3 – 5 February 2020, beginner workshop, UWA, Perth, Australia
12-13 March 2020, advanced workshop, L’Oreal, Aulnay sous Bois, France
We had a great group of participants at UWA and were hosted in the beautiful Forrest Hall facility from the Forrest Research Foundation.
Some feedback from the participants:
‘I liked that simple concepts were explained, to facilitate understanding of the complex ones. It is always nice to recap on the basics’
‘[It] Provided me with novel insights for my research and how to approach and interpret the data from an integrated statistical/computational and biological point of view.’
‘I liked you took some time to explain the concept behind the statistical tool, useful examples to clarify the concepts. After this workshop, it is going to be much easier to make better decisions about future experimental designs (or recommendations for other experiments) and the application of specific statistical tool as MixOmics. I will definitely apply mixMC to the data I am preparing for a publication. I appreciate you granted me the bursary. This workshop has uplifted my microbiome-applied statistical skills.’
‘This was a great workshop and it exceed my expectations’
I could complete all the exercises based on the material provided (which was great, with very clear instructions!). Most importantly, I acquired a better understanding of how I should approach my data in the future when my new project is in a more advanced stage. In other words, I now have a better idea of the packages and tools available and what to focus on in terms of skills development. I am also feeling a lot more confident to interpret complex plots generated in these multivariate analyses and make further decisions based on them and my research question.
Context. Advances in high-throughput technologies have transformed the way we examine molecular information, including microbial communities. However, analytical tool development is critically trailing behind data generation, which hinders the analysis, understanding or integration of microbiome data with other types of molecular data. Data integration adopt a holistic, data-driven and hypothesis-free approach. This new approach is necessary to understand the role of biological systems and posit new hypotheses.
The workshop will introduce concepts of multivariate dimension methods developed in mixOmics for statistical analysis. Our methods make no distributional assumptions, are highly flexible for unsupervised (exploratory), supervised (classification) and integration analyses. Various analytical frameworks will be presented ranging from data exploration, selection of markers, integration with other omics datasets and introduction to time-course analysis.
Each methodology will be illustrated on real biological studies. The third day is ‘BYO data’ day where you can reinforce your learnings on your own study! The workshop is not limited to microbiome data only, as we will cover general omics data integration concepts with appropriate case studies if the need arise.
Organized and hosted by: West Australian Heath Translational Network (WAHTN) and WA Human Microbiome Collaborating Centre (WAHMCC), Curtin University.
Fees for 3 days are AUD450+GST for RHD students, AUD750+GST for research non-profit organisations (Universities and CSIRO) and AUD1200+GST for industry. The West Australian Heath Translational Network generously sponsors registration bursaries ($225 to support 50% of the registration costs) to 4 RHD students. Apply at the EOI survey link below.
Registrations fees include coffee breaks, lunch, lecture notes and electronic material (slides, R code, data).
Registration Express your interest at this survey link. As we have a limited number of participants (30), priority will be given to postgraduate students and early career researchers. EOI for bursaries closes on November 4 2019 5pm AEST, but there are still spots for non bursaries. Results announced to the participants with details for registration.
Location: Forrest Hall, 35 Stirling Highway, Crawley WA 6009, Australia. Google map.
Accommodation: short stay can be booked at Forrest Hall ($120/night)
Prerequisite and requirements We require from the trainees a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to fully benefit from the workshop. Participants are requested to bring their own laptop, having installed the software RStudio http://www.rstudio.com/and the R package mixOmics (instructions will be provided prior to the training).
Outline
Day 1 & 2: methods and hands-on. The following broad topics will be covered.
A. Key methodologies in mixOmics and their variants:
Basic processing of count data (scaling, how to handle compositional data)
Exploration of one data set and how to estimate missing values
Identification of a microbial signature to discriminate different treatment groups
Integration of two data sets and identification of microbial markers
Introduction to repeated measurements or longitudinal studies analysis
How to deal with batch effects
Integration of more than two data sets to identify multi omics signatures (if sufficient interest)
Integration of independent but related studies (optional)
B. Review on the graphical outputs implemented in mixOmics
Sample plot representation
Variable plot representation for data integration
Other useful graphical outputs
C. Case studies and applications
Several microbiome and omics studies will be analysed using the methods presented above.
Day 3: bring your own data. Participants will be given the opportunity to analyse their own data under the guidance and the advice of the three instructors. Participants can also work in a team. Some data sets will also be provided for those unable to bring their own data.
The following statistical concepts will be introduced: covariance and correlation, multiple linear regression, classification and prediction, cross-validation, selection of markers, penalised regressions. Each methodology will be illustrated on a case study (theory and application will alternate).
Target group The course is intended for microbiologists working in the fields of bioinformatics, computational biology and applied statistics with some statistical knowledge and a good working knowledge in R. It will be particularly useful to those interested in:
Exploring data sets.
Selecting molecular / microbial features with methods implementing LASSO-based penalisations.
Using graphical techniques to better visualise data.
Understanding and/or applying multivariate projection methodologies to large data sets.
Anticipated learning outcomes After completion of this workshop, participants will be able to
Understand fundamental principles of multivariate projection-based dimension reduction technique.
Perform statistical integration and feature selection using recently developed multivariate methodologies.
Apply those methods to high throughput microbiome studies, including their own studies.
Lê Cao team and collaborators from University of British Columbia (Vancouver, Canada) have published their first method to integrate multiple omics data from the same set of biospecimens or individuals (e.g. transcriptomics, proteomics). Their method adopts a systems biology holistic approach by statistically integrating data from multiple biological compartments. Such approach provides improved biological insights compared with traditional single omics analyses, as it allows to take into account interactions between omics layers and extract multi-omics molecular networks.
DIABLO is a multivariate dimension reduction method and is hypothesis-free. The method constructs combinations of variables (e.g. cytokines, transcripts, proteins, metabolites) that are maximally correlated across data types to identify a minimal subset of markers – a multi-omics signature. This signature can highlight novel findings but is also the starting point to network modelling.
While the computational researchers where busy developing their method, they also analysed the data from the #SmallBig study (small sample, big data) with the EPIC (Expanded Program on Immunization) Consortium. EPIC comprises researchers from the Boston Children’s Hospital, University of British Columbia, Medical Research Council Unit The Gambia, Université libre de Bruxelles, Telethon Kids Institute and University of Western Australia, the Papua New Guinea Institute for Medical Research, to answer the question: What can less than 1mL of blood tell us about a newborn’s health?
Sample processing of the #SmallBig study (adapted from Lee et al. 2019)
In this study recently published in Nature Communications, the team has developed a technique to collect extremely small volumes of blood samples (< 1mL) to comprehensively characterise how biological molecules evolve in newborns. Using cutting-edge computational and statistical methods including DIABLO, they show that to the contrary to biology in adults that has a relatively steady-state, the first week of human life is highly dynamic and undergoes dramatic changes. Their results were consistently observed in vastly different areas of the world, West Africa (The Gambia) and Australasian (Papua New Guinea) and suggest a purposeful rather than random developmental path.
If you are interested in the potential of DIABLO to integrate microbiome and omics from the host, here is another study we published. We integrated the microbiome, proteome and meta-proteomics in T1D individuals.
Design of the multi-omics microbiome study
Identification of multi-omics signature from Gavin et al 2018.
The mixOmics team will present at the Nordic Precision Medicine Forum in Stockholm, 18-19 march 2019. Sébastien Déjean will give a talk about Data integration: Examining Statistical Methods for the Exploration and Integration of Heterogeneous Biological Data Sets.
Nordic Precision Medicine Forum brings together those at the very forefront of precision medicine from biologists, physicians and technology developers to data scientists, patient groups, governments and more.
Our participants for our first microbiome-dedicated workshop
Feedback from the workshop: This time we included several new case studies specifically focused on microbiome applications. We presented new material, including the problem of compositional data, how to detect and assess existing methods for batch effects (Ms Yiwen (Eva) Wang, PhD student) and our first timeOmics pipeline (Dr Olivier Chapleur).
‘I was especially pleased with the pace of the workshop. There was time to ask questions during lectures and practice. The pracs were designed to be relevant to our actual research questions.’
The event was sponsored by AFRAN and we obtained 50% bursaries from EMRI UoM for 5 PhD students.
‘Good contextualisation of methods before application of them, lots of depth on the background to methods which was important even when concepts were very complex. ‘I think the case studies were really helpful. The R code is written in such a clear and digestible way that it was easy to apply to my own data’
‘The pace and depth was good. All topics covered were highly relevant, and techniques were directly applicable. The ‘mood’ of the workshop was very friendly.’
Complex microbial networks have a central role in the provision and regulation of ecosystems. Multiple microbial biotechnology applications are contributing to global efforts to achieve sustainability – through purification of wastewater, waste valorisation, bioenergy production, or to understand the role of microbiome in human disease and healthy states.
Statistical analysis of microbiome data is challenging due to the inherent characteristics of the data, such as high sparsity and compositional structure. Our workshop will introduce major concepts including multivariate dimension methods developed in mixOmics. Our methods make no distributional assumptions, are highly flexible for unsupervised (exploratory), supervised (classification) and integration analyses.
This hands-on course will cover basic processing and inherent characteristics of microbiome data (compositionality, batch effects), various analytical frameworks ranging from data exploration, selection of microbial markers, integration with other omics datasets and introduction to time-course analysis. Each methodology introduced in the workshop will be illustrated on real biological studies. The third day is ‘BYO data’ day where you can reinforce your learnings on your own study!
Instructor: Dr Kim-Anh Lê Cao and Dr Olivier Chapleur; Tutor: Ms Laetitia Cardonna. The travels of Olivier and Laetitia is proudly sponsored by AFRAN, the Australian-French Association for Research and Innovation.
Fees for 3 days are AUD500 for RHD students, AUD900 for research non-profit organisations and AUD1500 for industry / government. The Environmental Microbiology Research Initiative EMRI (University of Melbourne) proudly sponsors registration bursaries ($225 to support some of the registration costs) to 5 RHD students enrolled at UoM. Apply at the EOI survey link below.
Registrations fees include coffee breaks, lunch, lecture notes and electronic material (slides, R code, data).
Registration Express your interest at this survey link. As we have a limited number of participants (30), priority will be given to postgraduate students and early career researchers. EOI closes on March 11.
Prerequisite and requirements We require from the trainees a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to fully benefit from the workshop. Participants are requested to bring their own laptop, having installed the software RStudio http://www.rstudio.com/and the R package mixOmics (instructions will be provided prior to the training).
Outline
Day 1 & 2: methods and hands-on. The following broad topics will be covered.
A. Key methodologies in mixOmics and their variants:
Basic processing of count data (scaling, how to handle compositional data)
Exploration of one data set and how to estimate missing values
Identification of a microbial signature to discriminate different treatment groups
Integration of two data sets and identification of microbial markers
Introduction to repeated measurements or longitudinal studies analysis
How to deal with batch effects
Integration of more than two data sets to identify multi omics signatures (if applicable)
Integration of independent but related studies (if applicable)
B. Review on the graphical outputs implemented in mixOmics
Sample plot representation
Variable plot representation for data integration
Other useful graphical outputs
C. Case studies and applications
Several microbiome studies will be analysed using the methods presented above.
Day 3: bring your own data. Participants will be given the opportunity to analyse their own data under the guidance and the advice of the three instructors. Participants can also work in a team. Some data sets will also be provided for those unable to bring their own data.
The following statistical concepts will be introduced: covariance and correlation, multiple linear regression, classification and prediction, cross-validation, selection of microbial markers, penalised regressions. Each methodology will be illustrated on a case study (theory and application will alternate).
Target group The course is intended for microbiologists working in the fields of bioinformatics, computational biology and applied statistics with some statistical knowledge and a good working knowledge in R. It will be particularly useful to those interested in:
Exploring microbiome data sets.
Selecting microbial features with methods implementing LASSO-based penalisations.
Using graphical techniques to better visualise data.
Understanding and/or applying multivariate projection methodologies to large data sets.
Anticipated learning outcomes After completion of this workshop, participants will be able to
Understand fundamental principles of multivariate projection-based dimension reduction technique.
Perform statistical integration and feature selection using recently developed multivariate methodologies.
Apply those methods to high throughput microbiome studies, including their own studies.
Registration and registration fees: before 17 may 2019, using this form. Fees are: academic (500€), private (1000€), see more details below in the link provided.
Language: French or English depending on the attendees
Prerequisite and requirements We require from the trainees a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to fully benefit from the workshop. Participants are requested to bring their own laptop, having installed the software RStudio http://www.rstudio.com/and the R package mixOmics (instructions will be provided prior to the training).
