Development of an RShiny App for Longitudinal Protein Expression Data Analysis
ABOUT THE INDUSTRY PARTNER
CSL is a global plasma biotechnology company with a diverse drug portfolio, headquartered in Melbourne. The Biostatistics team within CSL Research works on providing innovative solutions to diverse research problems pertaining to the company’s drug development pipeline.
WHAT’S IN IT FOR YOU?
- Gain real-world experience in R&D Biostatistics in the biopharmaceutical industry.
- Learn about the statistical concepts, methods, and strategies for pre-processing and analysis of longitudinal protein expression data (Olink proteomics)
- Be a part of a team having real impact across a wide range of human diseases and therapeutic areas.
- Opportunities for students to develop professional network with industry and to find potential career mentors.
RESEARCH TO BE CONDUCTED
Longitudinal protein expression studies which track protein expression levels over time in the same subjects are invaluable in drug discovery. While these studies generate valuable data, there’s a significant gap: most available data analysis tools either lack sophistication or require extensive R programming expertise, creating barriers for many researchers .
CSL are seeking a talented intern to help develop a user-friendly, web-based platform for analysing longitudinal proteomics data. This innovative tool will enable a more robust analytical workflow tailored to longitudinal studies, making advanced statistical analysis accessible to researchers of all skill levels.
This project will commence with a comprehensive survey/literature review of statistical best practices in analysing proteomics data with longitudinal data structure. The identified recommendations/gaps will then be leveraged to develop a more robust workflow for longitudinal proteomics data analysis. The intern will also systematically identify and document any limitations and areas for potential enhancement in the available statistical tools for this analysis. The intern will then develop a web-based tool in RShiny that will provide a comprehensive platform for proteomic data analysis, specifically those with longitudinal data structure. By identifying and extending any missing features, this will create a more flexible and powerful tool that caters to a wide range of users, from beginners to advanced researchers. This app will enable users to perform sophisticated analyses with ease, enhancing their ability to draw meaningful biological insights from their data.
SKILLS WISH LIST
If you’re a postgraduate research student and meet some or all the below we want to hear from you. We strongly encourage women, indigenous and disadvantaged candidates to apply:
- Bachelors, Honours or Master’s degree in Statistics, Biostatistics or Bioinformatics, or a PhD candidate in Statistics, Biostatistics or Bioinformatics.
- Solid understanding of statistical principles and methods for longitudinal proteomics data analysis (Olink).
- Experience in R programming
- Knowledge of RShiny (preferred).
- High level of verbal and written communication skills.
RESEARCH OUTCOMES
- An initial report outlining literature review results on best available statistical methodologies for the analysis of longitudinal protein expression data.
- Validated analysis pipeline.
- User-friendly RShiny tool for longitudinal proteomics data analysis.
- A final report documenting statistical methodologies used in the tool, implementation and case studies
ADDITIONAL DETAILS
The intern will receive $3,000 per month of the internship, usually in the form of scholarship payments.
It is expected that the intern will primarily undertake this research project during regular business hours and maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
Please note, applications are reviewed regularly and this internship may be filled prior to the advertised closing date if a suitable applicant is identified. Early submissions are encouraged.
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