Author: klecao

[Closed] Self-paced online course Oct 31st – Nov 27 2022

The next iteration of the course will be in September 2023 for a likely duration of 6-8 weeks (it will be advertised 3 months before opening the course). This course is online, but at your own pace, meaning that you need to dedicate enough time (5-8h per week) to fully benefit from the program.

Feedback from the 2022 iteration:

  • You can do it at your own time since the resources provided (Webinars and reading material) are very helpful. Due to working hours I had to watch/read on demand (at my own time)
  • Kim-Anh has done a very good job in the webinars and was generally approachable and helpful. Thank you! The online course material was very good and explained the basics of the program quite well. The integration with the mixOmics online material and sample cases is very helpful.
  • It had the option to attend live webinars (two offered times) or watch recordings. – The possibility to ask questions was available for both live webinars and stack. – The assignments are designed to enhance further learning allowing to use of either own data or provided data at different challenge skills.
  • Course organisers were very responsive to our questions in Slack. Modules flowed nicely and were well organised. Webinars were useful.

This is our second round of online course ‘mixOmics R Essentials for Biological Data Integration‘ that includes 4 weeks of asynchronous learning (with one live summary + Q&A per week), numerous chats on Slack and an additional 3 weeks to complete the assignment. Some feedback from our last round can be found here. Our last survey seem to suggest most learners spent between 5-8h per week on the program.

  • Teaching Period Dates, asynchronised:
    • Start – Monday, 31st October 2022
    • End – Sunday, 27th November 2022
    • (non marked) Assessment due Sunday, 9th December 2022
    • Peer-review of assessment due Sunday, 16th December 2022
  • Fees vary for
    • Research Higher Degree students enrolled at a University: $495 AUD (incl. GST)
    • Staff and members from Universities & Not-for-profit organisations: $825 (incl. GST)
    • Other industries: $1320 AUD (incl. GST)
    • discounts of 5% for a group of 3-9 learners and 10% for 10+ learners, however, this will require a single invoice per group.

(these funds go towards the support of a software developer to maintain the package)

Information about the course and registration: https://study.unimelb.edu.au/find/short-courses/mixomics-r-essentials-for-biological-data-integration/

The number of places is limited, so first come first serve (we aim to run this course twice a year).

What if I need an invoice? Contact Student Support at continuing-education[at]unimelb.edu.au

Prerequisites. A good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) is essential 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. The exercises and assignments are in R. Participants are encouraged to use RStudio and Rmarkdown (template and R code provided).

*Learners who are not proficient in R do not get the full benefit of the course (based on their own, honest, feedback!)

Our book is out!

We are excited to announce that our book is out, along with several case studies and R scripts available online. Check out this page.

It’s been a very (very) long term project, and a great collaboration with Zoe Welham whose dedication and patience helped shape this project into a readable whole! A huge thank you to Al Abadi, who tirelessly helped updating the package as we developed the content.

[Closed] Self paced online course Oct 11 – Nov 7 2021

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)
    • Other industries: $1320.00 AUD (incl. GST)

Information about the course and registration: https://study.unimelb.edu.au/find/short-courses/mixomics-r-essentials-for-biological-data-integration/

The number of places is limited, so first come first serve (we aim to run this course twice a year).

What if I need an invoice? Contact Student Support at continuing-education[at]unimelb.edu.au

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).

[closed] Online workshop (on-demand)

This workshop will only be run for a specific group of participants. Other online courses will be announced soon!

We will ask you to fill the internal survey so that we can tweak the course accordingly. Do not forget to lock in the dates already in your calendar!

Context. Advances in high-throughput technologies have transformed the way we examine molecular information. However, analytical tool development is critically trailing behind data generation, which hinders the analysis, understanding or integration of omics data. Data integration adopts a holistic, data-driven and hypothesis-free approach. This new approach is necessary to understand the role of biological systems and posit new hypotheses.

This online 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 and time-course data.

Each methodology will be illustrated on real biological studies during a short hands-on session in R. You can also bring your own data to analyse your data on the spot using the R scripts that we will provide. The workshop will cover general omics data integration concepts with appropriate case studies.

Instructors: A/Profs Sébastien Déjean (University of Toulouse, sessions 1-3) and Kim-Anh Lê Cao (University of Melbourne, sessions 4-5)

Material includes lecture notes, slides, R code, and data.

Bring your own data. Participants will be given the opportunity to analyse their own data using the R codes provided. We will give specific instructions on how to process and format the data. Participants can also work in a team. Some data sets will also be provided for those unable to bring their own data.

Dates for the five sessions (approx 2h per session):

  • Sept 21st, 23th, 28th 9-11am EST / 9-11pm Singapore (same day for all)
  • Sept 30th, Oct 5th 6-8pm EST / 6-8am Singapore (+1 day)

Contact: mixomics[ at] math.univ-toulouse.fr (for pre-requisite or content)

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

Each session is 2h length, roughly divided into 50min presentation, 10 min break and 1h hands-on with recap at the end of the session.

Session 1: PCA and sparse PCA 101

  • We will start with the basics that are necessary to understand the more complicated concepts!

Session 2: PLS-Discriminant Analysis

  • We will move on to discriminant analysis, to separate sample groups and identify molecular signatures. The hands-on session can include your own data. (*PLS = Projection to Latent Structures / Partial Least Squares)

Session 3: integration of two data sets with PLS and CCA

  • This session will also introduce useful graphics to visualise the results of those methods. BYO data welcome. (CCA = Canonical Correlation Analysis)

Session 4: multi-omics data integration with block PLS (DIABLO)

  • Building up on the previous sessions, we will cover multiblock PLS-DA with additional numerical and graphical outputs. You will anlyse BYO data (if you have already analysed your data with the previous methods) or data provided in the package.

Session 5: various methodological extensions

  • This more theoretical session will cover recent methodological developments ‘around’ (but not necessarily ‘in’) mixOmics, from compositional data analysis (for microbiome studies), batch effect management to P-integration and time-course omics data exploration (topics chosen according to your needs). This session will not include hands-on on session but relevant R code / vignettes will be hand out.

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:

  1. Exploring data sets.
  2. Selecting molecular / microbial features with methods implementing LASSO-based penalisations.
  3. Using graphical techniques to better visualise data.
  4. Understanding and/or applying multivariate projection methodologies to large data sets.

Anticipated learning outcomes After completion of this workshop, participants will be able to

  1. Understand fundamental principles of multivariate projection-based dimension reduction technique.
  2. Perform statistical integration and feature selection using recently developed multivariate methodologies.
  3. Apply those methods to high throughput microbiome studies, including their own studies.

[cancelled] 26-29 Oct 2021, Palmerston North, NZ (beginner)

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.

———-

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.

Instructor: A/Prof Kim-Anh Lê Cao;Tutors: TBA

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).

Location: TBA, Massey University

Contact: mixomics[ at] math.univ-toulouse.fr (for pre-requisite or content)

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:

  1. Exploring data sets.
  2. Selecting molecular / microbial features with methods implementing LASSO-based penalisations.
  3. Using graphical techniques to better visualise data.
  4. Understanding and/or applying multivariate projection methodologies to large data sets.

Anticipated learning outcomes After completion of this workshop, participants will be able to

  1. Understand fundamental principles of multivariate projection-based dimension reduction technique.
  2. Perform statistical integration and feature selection using recently developed multivariate methodologies.
  3. Apply those methods to high throughput microbiome studies, including their own studies.

Webinar: mixOmics in 50 minutes

This latest seminar was hosted by Australian BioCommons / EMBL-ABR / ARDC  in March 2024.

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.

Any mixOmics related question can be send to  https://mixomics-users.discourse.group (you will need to login but there is not mail traffic associated)

Workshops for 2020

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

18 – 23 May 2020, selected PhD students from http://www.phd4manna.eu/ , 2 days, Slovakia

25 – 29 May 2020, Roscoff ‘Ecole Thematique’ PAMOMICS, more details will be updated on line.

22 – 24 June 2020, Toulouse, France, 3-day workshop. See here.

For more details, contact us at mixomics[@]math.univ-toulouse.fr

Feb 3-5 2020, Perth, AUS (beginner, omics and microbiome)

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.

Instructor: Dr Kim-Anh Lê Cao;Tutor: TBA

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)

Contact: mixomics[ at] math.univ-toulouse.fr (for pre-requisite or content)

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:

  1. Exploring data sets.
  2. Selecting molecular / microbial features with methods implementing LASSO-based penalisations.
  3. Using graphical techniques to better visualise data.
  4. Understanding and/or applying multivariate projection methodologies to large data sets.

Anticipated learning outcomes After completion of this workshop, participants will be able to

  1. Understand fundamental principles of multivariate projection-based dimension reduction technique.
  2. Perform statistical integration and feature selection using recently developed multivariate methodologies.
  3. Apply those methods to high throughput microbiome studies, including their own studies.

Multi-omics data integration: method and showcase applications

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.

More information about DIABLO, implemented in the mixOmics R package: Amrit Singh, Casey P Shannon, Benoît Gautier, Florian Rohart, Michaël Vacher, Scott J Tebbutt and Kim-Anh Lê Cao (2019) DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays, Bioinformatics. You can also find some technical information in the mixOmics paper (particularly in the Supp!) and also in our tutorials here.

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.

More information about the SmallBig study: Amy H. Lee, Casey P. Shannon, […]Tobias R. Kollmann (2019). Dynamic molecular changes during the first week of human life follow a robust developmental trajectory Nature Communications volume 10, Article number: 1092.

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.

More details about the study: Gavin PG, […], and Hamilton-Williams EE (2018). Intestinal metaproteomics reveals host-microbiota interactions in subjects at risk for type 1 diabetes Diabetes care 41: 10. We used DIABLO to integrate microbiome, proteomics and meta-proteomics.