Biostatistics and Data Analysis

You are here

Home » Biostatistics and Data Analysis
Credits
6
Types
Compulsory
Requirements
This subject has not requirements
Department
UPF
This course provides a comprehensive overview of the essential concepts and methods for analyzing biomedical data. The objective is to achieve a deep understanding of statistical principles and their application in the biological sciences. We begin with a fundamental introduction to probability theory and statistical inference. Students will then explore specific statistical techniques fundamental to the analysis of biomedical data.
Throughout the course, real examples will be used to illustrate these methods, enhancing practical understanding and application. The learning experience includes a combination of in-person theoretical classes, practical sessions and short assignments to reinforce key concepts.

Teachers

Person in charge

  • Laura Aviñó Esteban ( )

Others

  • Armand González Escalante ( )
  • Hafid Laayouni el Alaoui ( )
  • Michael Thompson ( )

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Learning Outcomes

Learning Outcomes

Knowledge

  • K2 - Identify mathematical models and statistical and computational methods that allow for solving problems in the fields of molecular biology, genomics, medical research, and population genetics.
  • K3 - Identify the mathematical foundations, computational theories, algorithmic schemes and information organization principles applicable to the modeling of biological systems and to the efficient solution of bioinformatics problems through the design of computational tools.
  • K5 - Identify the nature of the biological variables that need to be analyzed, as well as the mathematical models, algorithms, and statistical tests appropriate to develop and evaluate statistical analyses and computational tools.

Skills

  • S2 - Computationally analyze DNA, RNA and protein sequences, including comparative genome analyses, using computation, mathematics and statistics as basic tools of bioinformatics.
  • S3 - Solve problems in the fields of molecular biology, genomics, medical research and population genetics by applying statistical and computational methods and mathematical models.
  • S4 - Develop specific tools that enable solving problems on the interpretation of biological and biomedical data, including complex visualizations.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.

Competences

  • C3 - Communicate orally and in writing with others in the English language about learning, thinking and decision making outcomes.
  • C6 - Detect deficiencies in the own knowledge and overcome them through critical reflection and the choice of the best action to expand this knowledge.

Objectives

  1. 1. Acquisition of the basic knowledge of statistical inference.
    Related competences: K2, K3, K5, S4, S8, C3, C6,
  2. 2. Using statistics for solving biological problems.
    Related competences: K2, K3, K5, S2, S4, S8,
  3. 3. Learning how to use R software to analyse biological data.
    Related competences: K5, S2, S3, S4, S8, C3,

Contents

  1. Hypothesis testing and student t test.
    Statistical reasoning, test statistic, p-value, rejection region, Type I and Type II errors, paired data.
  2. Relationships in categorical data, goodness-of-fit test.
    Contingency tables. The chi-square
  3. Analysis of variance, one-way ANOVA, multiple comparison procedures.
    Analysis of variance, one-way ANOVA, multiple comparison procedures.
  4. Two-way ANOVA.
    Variation partition, main effects, and interaction.
  5. Correlation and regression.
    Descriptive and inferential aspects of correlation and simple linear regression.
  6. Multiple regression analysis. Partial correlation.
    Interpretation of regression coefficients, test of significance.
  7. Effect Size and a summary of inference methods.
    P-values, sample size, effect size, and statistical power.

Activities

Activity Evaluation act


Theoretical expository lectures.



Theory
27h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
45h



Mid term exam.


Objectives: 1 2
Week: 1
Theory
0h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Final exam.


Objectives: 1 2 3
Week: 1
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

The theoretical classes are mainly expository. There will also be problem-based sessions and practical sessions with R.

Evaluation methodology

For the course evaluation, the grades of the partial exam (P), the final exam (F), and the R sessions (R) will be considered and combined according to the following formula:
Grade = max(0.2·P + 0.2·R + 0.6·F ; 0.2·R + 0.8·F)

A student is considered to have attended the course if they have taken the final exam. If the student attended but failed the course, they may take the reassessment exam (RT), and in this case, the grade will be calculated as 0.2·R + 0.8·RT (the partial exam grade will not be considered).

Bibliography

Basic:

Web links