These two recordings were part of a presentation to WEHI for their postgraduate lecture series for a diverse audience.
In the PCA presentation (18 min), we explain the concept of linear combination of variables (components) and useful graphical outputs such as correlation circle plots and biplots.
In the PLS-DA presentation (7 min), we talk about the concept of multivariate signature.
If you want to know more about the actual algorithm under the hood, you can watch this webinar on PLS.
Unfortunately we had to cancel the workshop as we did not receive a sufficient number of participants to justify running the workshop at this time. These workshops involve peer review and a cohort feel to provide the best experience to our learners.
Register your EOI here and we will let you know when the registration page is up. Our next intake is scheduled for February 2025.
Feedback from a previous iteration can be found here.
Key summary
(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 (this course runs once or 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 benenefit of the course (based on their own, honest, feedback!)
Our latest method based on PLS to infer microbial networks across time is now in preprint!
Microbial network inference for longitudinal microbiome studies with LUPINE. Saritha Kodikara, Kim-Anh Lê Cao. bioRxiv 2024.05.08.593086; accepted in Microbiome.
View this 50min video of Dr Saritha Kodikara presenting her method LUPINE:
We also have a second video presented by Prof Kim-Anh Lê Cao who sets LUPINE in the context of microbiome longitudinal data analysis, elaborating more on the types of analytical objects covered in Kodikara et al. (2022) Statistical challenges in longitudinal microbiome data analysis, Briefings in Bioinformatics.
Below you will also find the most common questions related to LUPINE.
FAQ:
Q: Do you build up the network from the covariance matrix or from the inverse covariance matrix? And what are you doing linear regression on?
A: The network is built on the partial correlation so it would be similar to the inverse covariance matrix. But instead of estimating the inverse covariance matrix, we calculate partial correlations through linear regression. To estimate the partial correlation between taxa a and taxa b, we regress their counts on the low dimensional representation of other taxa (excluding taxa a and b). This is then repeated for all pairs (we have an efficient way to do this computationally).
Q: You reduce the dimension of the data into one dimension. How much variance can be explained by the 1st component in your computation?
A: It depends on the data, but in the data we analysed, and if consider the single time point scheme only with PCA, the first component explained about 25% of the total variance. We could add more components into the regression but that may overfit the regression model. This is why we only select the first component, which explains much of the variance (for PCA, single time point) or covariance (for PLS, multiple time points).
Q: Do you think that this approach would work on single cell data trying to look at gene co expression in sort of longitudinal data in across time points?
A: It will not work with the present single cell technologies, because in LUPINE we need the same individuals/samples/cells across time to infer the association.
Q: When you do the linear regression, do you regress directly on the counts with all the zeros and the sparsity that you mentioned?
A: Yes, the method was originally developed for count data. We regress on the count data, but we also include library size as an offset to account for different library sizes. The method also works with center log ratio values, which I used to analyse the third case study.
Q: Do you apply your method for the two groups combined or separately?
A: I model each group separately as we assume that each group has a unique network.
Q: You’re building the networks building based on the partial correlations. What about the actual network for representation, do you actually binarize it?
A: Yes, I binarize the network based on a correlation test.
The LUPINE package
LUPINE is currently not directly available from the mixOmics package, instead it is a separate R package that can be installed from Github.
This workshop is now closed. Fill in this short survey to register your interest. A new iteration of the course might be run between Sept – Nov if there is sufficient interest!
Key summary
Feedback from the 2022 iteration can be found here.
(these funds go towards the support of a software developer to maintain the package)
The number of places is limited, so first come first serve (this course runs once or 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!)
If you’ve missed out, our next iteration will run from 19th Feb – 5th April 2024. You can fill up this short survey to be notified when we open our next course.
Summary
Feedback from the 2022 iteration can be found here.
(these funds go towards the support of a software developer to maintain the package)
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!)
We will be running a 2-day workshop at Frazer Institute, University of Queendland. The workshop will cover 1.5 days of lectures and hands-on, and an additional 0.5 day for discussions and opportunities to analyse your own data (assuming the data are already processed and normalised).
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 in your calendar before we 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 17th February.
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 omics 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 analyse your own data.
Each method will be illustrated on real biological studies. The last afternoon is ‘BYO data’ where you can reinforce your learnings on your own study!
Instructor: A/Prof Kim-Anh Lê Cao;Tutor: Nick Matigian (QCIF)
Organized and hosted by: Frazer institute, University of Queensland
There are no registration fees for this workshop. We do expect your attendance as the number of places is limited. The workshop is fully catered. Slides, R code and data will be provided.
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 after the survey’s deadline. Online attendance is also available for a limited number of participants (but with reduced opportunities for interactions).
Location: TBA, Translational Research Institute
Contact: kimanh.lecao[ at] unimelb.edu.au (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).
The following broad topics will be covered during these two days:
Several omics studies (and microbiome if there is some interest) will be analysed using the methods presented above.
Day 2: bring your own data. Participants will be given the opportunity to analyse their own data under the guidance and the advice of the instructors. Participants can also work in a team. Your data need to be processed and normalised beforehand.
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 molecular biologists 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:
Anticipated learning outcomes After completion of this workshop, participants will be able to
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:
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.
(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!)
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.
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‘!
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).
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):
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
Session 2: PLS-Discriminant Analysis
Session 3: integration of two data sets with PLS and CCA
Session 4: multi-omics data integration with block PLS (DIABLO)
Session 5: various methodological extensions
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:
Anticipated learning outcomes After completion of this workshop, participants will be able to
![[closed] 13-14 March 2023, Brisbane, Aus](https://mixomics.org/wp-content/uploads/2023/01/TRI-940x198.jpeg)