Experimental Design and Analysis of Biological Data
Code: 107520 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| Biology | OB | 2 |
Contact
- Name:
- Maria Magdalena Gaya Vidal
- Email:
- magda.gaya@uab.cat
Teachers
- Pau Carnicero Campmany
- Moisès Guardiola Bufí
- Francisco Javier Carrasco Trancoso
- Nerea Roher Armentia
- Juan Carlos Balasch Alemany
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
It is recommended to have reviewed the concepts developed in Biostatistics in the first year. It is based on the previous achievement of knowledge of theoretical statistics at a basic and conceptual level. These concepts will be reviewed, expanded and applied in examples related to the degree.
A sufficient level of English reading to understand scientific articles and published examples is a prerequisite.
Objectives and Contextualisation
General objective:
The objective is for students to acquire the basic skills to be able to correctly design the most frequent types of study in Biosciences, apply the appropriate statistical techniques to the design, interpret the results appropriately and, finally, be able to obtain reasoned conclusions in accordance with the data.
This is an instrumental subject, which introduces statistical tools into Biology studies in order to analyze biological data from the description of natural phenomena or experiments, emphasizing their correct use and the interpretation of results.
Course objectives:
1. Learn and apply the basic statistical techniques necessary for the design and analysis of data from related processes and experiments.
2. Learn to explore with descriptive methods various sets of data, resulting from the observation of biological phenomena or experimentation.
3. Understand and interpret appropriately the results obtained in a statistical analysis.
4. Use and practice the basic elements of free statistical software. Learn about computer tools (R software and R Commander and RStudio graphical user interfaces) for statistical data processing.
Learning Outcomes
- CM06 (Competence) Work in experimental design and data analysis in compliance with the ethical aspects inherent to biological studies of different types.
- CM07 (Competence) Integrate the gender perspective, whether in the design of studies or in the analysis of biological data, knowing how to distinguish the effects of sex and gender variables.
- CM08 (Competence) Plan projects and data analysis using biostatistics, genomics, transcriptomics and proteomics tools, with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values, and in accordance with the Sustainable Development Goals.
- KM11 (Knowledge) Describe the different types of statistical and epidemiological analysis applied to the resolution of biological problems in different fields.
- KM12 (Knowledge) Describe the content of databases of interest for biosciences and the methodologies for extracting relevant information in the field of biology.
- SM07 (Skill) Select the statistical tests and computer resources appropriate to each situation and set of biological data.
- SM09 (Skill) Interpret the results of statistical tests applied to the resolution of biological problems in different fields, expressing them appropriately.
Content
• Introduction to Experimental Design: randomization, replication and blocks; general indications.
• Introduction to designs in epidemiology.
• Introduction to R. Data assessment and visualization: outliers, deviations from normality and transformation of variables.
• Remember t-test, independent and paired samples.
• Analysis of variance (ANOVA, ANCOVA)
• Correlation and regression analysis
• Descriptive multivariate analysis: principal component analysis / canonical
• Sample size calculation and type II error estimation.
• Interpretation of results. Statistically significant differences versus relevant differences.
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Practical classes | 30 | 1.2 | CM06, CM07, CM08, KM11, KM12, SM07, SM09, CM06 |
| Theory classes | 20 | 0.8 | CM06, CM07, CM08, KM11, KM12, SM07, SM09, CM06 |
| Type: Supervised | |||
| Individual Tutorials | 3 | 0.12 | CM07, SM07, SM09, CM07 |
| Type: Autonomous | |||
| Practical projects work | 57 | 2.28 | CM06, CM07, CM08, KM11, KM12, SM07, SM09, CM06 |
| Study | 34 | 1.36 | CM06, CM07, CM08, KM11, KM12, SM07, SM09, CM06 |
The core of the learning process is the students' work. Students learn by working, being the mission of the teaching staff help him/her in this task by providing information or showing him/her the sources where one can be obtained and guiding your steps in a way that the learning process can be carried out effectively. In line with these ideas, and in accordance with the objectives of the subject, the course development is based on the following activities:
Theory classes:
Student acquire the scientific and technical knowledge specific to the subject by attending theory classes, complementing them with self-study of the subjects explained in order to assimilate the concepts and the procedures, to detect doubts and to realize summaries and schematics of the subject. In the theory classes, the professor introduces the basic concepts of the subject, showing their application. Theory classes are theoretical-practical classes in which the teacher introduces the basic concepts corresponding to the subject matter, demonstrating their application.
Practical classes:
The practical classes will consist of three practical blocks. These sessions are held with a smaller group of students, in which the scientific and technical knowledge presented in the theoretical classes is developed to complete their understanding and deepen their knowledge through the development of three practical projects using appropriate software. This will be done both in class and independently by the students.
In the computer practice sessions, the student will learn to use computer tools for descriptive analysis of data sets and statistical inference.
Note: 15 minutes of a class will be reserved, within the calendar established by the centre/degree, for the complementation by the students of the questions of evaluation of the performance of the professor and of evaluation of thesubject/module.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Practical exam | 20% | 3 | 0.12 | CM06, CM07, CM08, KM11, KM12, SM07, SM09 |
| Practical projects work | 45% | 0 | 0 | CM06, CM07, CM08, KM11, KM12, SM07, SM09 |
| Theory exam | 35% | 3 | 0.12 | CM06, CM07, CM08, KM11, KM12, SM07, SM09 |
Continuous evaluation.
The evaluation of the subject consists of a a continuous assessment part of the acquired skills: there will be a theoretical exam with a weight of 35%. A practical exam with a computer that will have a weight of 20% in the final assessment of the subject. These two exams will be the rocoverable part of the subject. The remaining 45% of the grade will be obtained from the submission of three assignments (each one counts for 15%). These assignments are not recoverable.
To participate in the recovery examination, students must have previously been assessed in a set of activities whose weight is equivalent to a minimum of two thirds of the total grade of the subject. Therefore, the students will obtain the "Non-evaluable" qualification when the evaluation activities carried out have a weighting of less than 67% in the final grade.
Unique evaluation.
The unique evaluation consists of the theory exam, the practical exam and, regarding the submissions of the three oractical assignments, a single submission can be made on the same day as the practical exam and they are not recoverable.
Minimum grades.
A minimum mark of 4.5 out of 10 is required for each exam (theoretical or practical). If these minimum marks are achieved, the final mark is the weighted average of the different assessable parts and to pass must be equal to or greater than 5.0.
Use of AI
For this subject, the use of Artificial Intelligence (AI) technologies is permitted exclusively in support tasks, such as bibliographic or information searches, text correction, translations, for code creation or other activitiesat the discretion of the teaching staff. The student must clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how these have influenced the process and the final result of the activity. Lack of transparency in the use of AI in this assessable activity will be considered a lack of academic honesty and may lead to a partial or total penalty in the grade of the activity, or greater sanctions in serious cases.
Bibliography
Alan Grafen, Rosie Hails. Modern statistics for the life sciences. Oxford University Press, 2002.
Bardina, X. Farré, M. Estadística descriptiva. Manuals UAB, 2009.
Besalú, M. Rovira C. Probabilitats i estadística. Publicacions i Edicions de la Universitat de Barcelona, 2013.
Delgado, R. Probabilidad y Estadística para ciencias e ingenierías. Delta, Publicaciones Universitarias. 2008.
Devore, Jay L. Probabilidad y Estadística para ingeniería y ciencias. International Thomson Editores. 1998.
Legendre, P., & Legendre, L. Numerical Ecology (3rd English ed.). Amsterdam: Elsevier. 2012.
Milton, J. S. Estadística para Biología y Ciencias de la Salud. Interamericana de España, McGraw-Hill, 2007 (3a ed. ampliada).
Remington, R. D. Schork, M. A. Estadística Biométrica y Sanitaria. Prentice/Hall Internacional, 1974.
Robert R. Sokal, F. James Rohlf. Biometry: The principles and practice of statistics in biological research. W.H. Freeman and Company, New York. 2013.
StatSoft Electronic Statistics Textbook (http://www.statsoft.com/Textbook)
Software
In the computer practice sessions, the student will learn to use the free software R with the graphical user interface R Commander (or an equivalent graphical interface), in order to apply the statistical tools for the descriptive analysis of data sets and statistical inference. The student will also work with the JAMOVI software (https://www.jamovi.org/download.html), and will train students in their use.
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLAB) Practical laboratories | 121 | Catalan/Spanish | second semester | morning-mixed |
| (PLAB) Practical laboratories | 122 | Catalan/Spanish | second semester | morning-mixed |
| (PLAB) Practical laboratories | 123 | Catalan/Spanish | second semester | morning-mixed |
| (PLAB) Practical laboratories | 124 | Catalan/Spanish | second semester | morning-mixed |
| (TE) Theory | 12 | Catalan/Spanish | second semester | morning-mixed |