Universitat Internacional de Catalunya
Data Analysis in Biomedical Sciences
Other languages of instruction: Catalan, Spanish
Introduction
In recent years, biomedicine has witnessed a huge increase in the production of patient data, both clinical and molecular. In fact, it is not uncommon to obtain massive sequencing data from a patient and combine it with the results of clinical trials and gene expression experiments, all with the aim of achieving better management of their health problems. However, this process is made difficult by the volumes of data handled. To facilitate the processing and understanding of such data, we have an ever-growing arsenal of graphical representation tools, dimensionality reduction, etc., which allow for sophisticated data analysis in biomedical environments. In this course, the student will become familiar with this arsenal of tools and acquire a solid knowledge of their applications.
Pre-course requirements
It is recommended to have completed and passed:
Introduction to bioinformatics
Objectives
Know the tools available for processing and understanding massive data obtained from patients.
Competences/Learning outcomes of the degree programme
- CB01 - Students must demonstrate that they have and understand knowledge in an area of study that is based on general secondary education, and it tends to be found at a level that, although it is based on advanced textbooks, also includes some aspects that involve knowledge from the cutting-edge of their field of study.
- CB03 - Students must have the ability to bring together and interpret significant data (normally within their area of study) to issue judgements that include a reflection on significant issues of a social, scientific and ethical nature.
- CB04 - That students can transmit information, ideas, problems and solutions to specialist and non-specialist audiences.
- CB05 - That students have developed the necessary learning skills to undertake subsequent studies with a high degree of autonomy.
- CE07 - To apply statistical tools to Health Science studies.
- CE19 - To be aware of the principles of biomedical science related to health and learn how to work in any field of Biomedical Sciences (biomedical companies, bioinformatics laboratories, research laboratories, clinical analysis companies, etc.).
- CG07 - To incorporate basic concepts related to the field of biomedicine both at a theoretical and an experimental level.
- CG10 - To design, write up and execute projects connected to the field of Biomedical Sciences.
- CG11 - To be aware of basic concepts from different fields connected to biomedical sciences.
- CT01 - To develop the organisational and planning skills that are suitable in each moment.
- CT02 - To develop the ability to resolve problems.
- CT03 - To develop analytical and summarising skills.
- CT04 - To interpret experimental results and identify consistent and inconsistent elements.
- CT05 - To use the internet as a means of communication and a source of information.
- CT06 - To know how to communicate, give presentations and write up scientific reports.
- CT07 - To be capable of working in a team.
- CT08 - To reason and evaluate situations and results from a critical and constructive point of view.
- CT09 - To have the ability to develop interpersonal skills.
- CT10 - To be capable of autonomous learning.
- CT11 - To apply theoretical knowledge to practice.
- CT12 - To apply scientific method.
- CT13 - To be aware of the general and specific aspects related to the field of nutrition and ageing.
- CT14 - To respect the fundamental rights of equality between men and women, and the promotion of human rights and the values that are specific to a culture of peace and democratic values.
Learning outcomes of the subject
The following is contemplated as a specific learning outcome for this subject:
- The student will learn to analyse large amounts of data from molecular biology experiments, clinical experiments, etc., with the aim of generating knowledge about the associated biomedical problems.
Syllabus
1.- Preliminaries. Defining your analysis goals. Data collection and cleaning
1.1.- A primer on data visualization and interpretation: barplots, scatterplots, boxplots, etc.
2.- Representing multidimensional data in two dimensions:
2.1.- Principal Components Analysis (PCA).
2.2.- Recent techniques for dimensionality reduction: T-SNE and UMAP.
2.3.- Alternatives for binarized data: logistic PCA.
2.4.- Other dimensionlity reduction methods: Diffusion maps, autoencoders.
3.- Data clustering
3.1.- Motivation. Identification of common behavior among patients, samples, etc.
3.2.- Unsupervised learning: an introduction
3.3.- Main techniques. DBSCAN. K-means.
Teaching and learning activities
In person
Lectures: lectures for 2 hours in blocks of 50 minutes on a theoretical topic by the teacher.
Clinical cases or case methods (CM): Presentation of a real or imaginary situation. Students work on the questions posed in small groups or in active interaction with the teacher and the answers are discussed. The teacher actively intervenes and, if necessary, contributes new knowledge.
Virtual education (EV): Online material that the student can consult from any computer, at any time, and which will contribute to self-learning of concepts related to the subject.
Evaluation systems and criteria
In person
Students in the first call:
Case methods: 25%
Midterm exam: 35%
Final exam: 40%
Students in the second or subsequent call: the grade for the case methods will be saved and the final exam will represent 75% of the final grade. Repeating students who wish to repeat the midterm in the 3rd or 5th call may do so by previously communicating this to the professor.
General points to bear in mind about the evaluation system:
In order to be able to make an average, the final exam must obtain a minimum grade of 5.
In addition to what was mentioned above, to pass the subject, the average of all the grades must be 5 or higher.
The continuous nature of this evaluation means that it is not possible to evaluate the subject if you have not participated in 75% of the hours.
Improper use of electronic devices (such as recording and sharing information about students and teachers during different sessions, as well as using these devices for recreational rather than educational purposes) may result in expulsion from class.