Thesis work: Tackling missing data in high throughput experiments

Job description
Do you enjoy applying statistics and mathematics to biological problems? Would you like to help develop life changing medicines? Then AstraZeneca might be of interest to you!

Tackling missing data in high throughput protein expression experiments
Thesis work: 30 credits 

At AstraZeneca, we turn ideas into life changing medicines and strive to continuously meet the unmet needs of patients worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you’re our kind of person.

Responsibilities
As an MSc student in the Quantitative Biology group in Gothenburg, you will be part of a large team of statisticians and bioinformaticians and will have the opportunity to collaborate with colleagues across sites in Sweden, the UK and the USA. You will improve both your programming and statistical skills to tackle challenges in our drug development pipeline. Working alongside the team you will contribute to our work on proteomics with the goal of assessing suitability of therapeutic candidates for further experimental follow-up.  You will improve both your programming and statistical skills to tackle challenges in our drug development pipeline.

Background
Proteomics data generated from mass spectrometry (MS) experiments are very complex and deep data. These experiments are usually carried out in relatively few samples compared to other omics experiments. Processing the data is extremely complex and there is no standard way to quality control or analyse the data. There are often missing values and there is large variation within and between experimental batches. This affects how the data may be analysed. Analysis usually takes the form of comparing relative protein abundance between two experimental conditions. Missing data and variance may bias this comparison if not properly controlled for.

Two classes of missingness include: ‘missing at random’ i.e. due to the way the peptides are selected for MS protein quantification; and another is ‘below detection level’. In our experiments it is especially important to understand whether compound treatment resulted in successful degradation of the target protein.  It is also necessary to determine if there are any off-target effects which may indicate toxicity. 

We can harness the data from untreated cells to understand which proteins are ‘usually’ expressed in a specific cell line. ‘Usually’ should include housekeeping genes which are ubiquitously expressed and others which follow temporal or cell type specific expression patterns. We can derive an underlying probability distribution/matrix for the presence or absence of an individual protein or for clusters of proteins (that may share the same regulatory factors). Thus, we should be able to better inform our prediction of types of missingness based on protein identity or cluster identity. We should also be able to impute missing values and derive a probability score for that imputed value based on prior knowledge.

Variation within and between batches of the same experiment is a challenge when normalizing and quality checking the data with the ultimate aim of combining all the data into a single analysis.  Differences due to the inherent variation of the experimental system may lead to spurious associations with treatment. There are many methods which have been derived to analyse RNA expression data which takes the experimental variation into account.  It is unclear whether the assumptions of these analytical methods are satisfied by the data generated by MS protein quantification experiments. There may be an opportunity to build on existing methods or to develop a new method.

Qualifications
Essential Requirements
• Competence in mathematics
• Basic programming and statistical skills
• Enjoy working as part of a team at AstraZeneca’s site in Gothenburg

Desirable Requirements
• A basic understanding of biology would be a great asset

Application
Randstad Life Sciences is cooperating with AstraZeneca in this recruitment process. We only accept applications through Randstad’s website.

Deadline for application: 2020-03-31, selection and interviews will be ongoing. The position may be filled before the last day of application, therefore, apply as soon as possible.

For more information: Kerstin Karlsson kerstin.karlsson@randstad.se or Eleonor Ehrman +4673-343 41 09.

About the company
Our Gothenburg site is one of AstraZeneca's three strategic science centers. We thrive in a multinational environment working cross-functionally across the globe with AstraZeneca colleagues as well as academic and industry partners. Our way of life is to foster a working environment that nurtures, collaboration, openness and innovation. Therefore, we have created space for meetings, socializing and relaxation, where spontaneous meetings can give birth to new innovations. The unexpected ideas or thoughts that can come from a chat over something as simple as a cup of coffee or a stroll on our “walk and talk” meeting trail. 

AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law. AstraZeneca only employs individuals with the right to work in the country/ies where the role is advertised.