| Study programme competencies |
| Code | Study programme competences |
| A5 | CE5 - Development of skills in the management of statistical techniques and their application to data sets from the bioinformatics field. |
| A6 | CE6 - Ability to identify software tools and most relevant bioinformatics data sources, and acquire skill in their use |
| A10 | CE10 - Draft a bioinformatics research project, anticipating obstacles and possible alternative strategies to resolve them. |
| B1 | CB6 - Own and understand knowledge that can provide a base or opportunity to be original in the development and/or application of ideas, often in a context of research |
| B4 | CB9 - Students should know how to communicate their findings, knowledge and latest reasons underpinning them to specialized and non-specialized audiences in a clear and unambiguous way |
| B5 | CB10 - Students should possess learning skills that allow them to continue studying in a way that will largely be self-directed or autonomous. |
| C3 | CT3 - Use the basic tools of the information technology and communications (ICT) necessary for the exercise of their profession and lifelong learning |
| C6 | CT6 - To assess critically the knowledge, technology and information available to solve the problems they face to. |
| C8 | CT8 - Rating the importance that has the research, innovation and technological development in the socio-economic and cultural progress of society |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| G1 - Capacidade para iniciar a investigación e para participar en proxectos de investigación que poden culminar na elabouración duhna teses de doutoramento. | AJ5 AJ6 AJ10 |
BJ1 BJ4 BJ5 |
CJ3 CJ6 CJ8 |
| G2 - Capacidade de aplicación de algoritmos de resolución dos problemas e manexo do software adecuado. | AJ5 AJ6 AJ10 |
BJ1 |
CJ3 |
| G3 - Capacidade de traballo en equipo e de xeito autónomo | AJ5 AJ6 |
BJ1 BJ4 BJ5 |
CJ3 CJ6 CJ8 |
| G4 - Capacidade de formular problemas en termos estatísticos, e de resolvelos utilizando as técnicas axeitadas. | AJ5 AJ6 AJ10 |
BJ1 |
CJ3 CJ6 |
| G6 - Capacidade de identificar e resolver problemas | AJ5 AJ6 AJ10 |
BJ1 BJ5 |
CJ3 |
| G10 - Capacidade de integrarse nun equipo multidisciplinar para a análise experimental | AJ5 AJ6 AJ10 |
BJ1 BJ4 BJ5 |
CJ3 CJ6 CJ8 |
| G11 - Adquirir destreza para o desenvolvemento de software | AJ5 AJ6 |
BJ5 |
CJ3 |
| G12 - Capacidade de análise estatística crítica das mostras, os plantexamentos e resultados | AJ5 AJ10 |
BJ1 BJ5 |
CJ6 CJ8 |
| G14 - Representar un problema real mediante un modelizado estatístico axeitado. | AJ5 AJ6 AJ10 |
BJ1 BJ5 |
|
| G15 - Deseñar un plano de observación ou recollida de datos que permita abordar o problema de interese | AJ5 AJ6 AJ10 |
BJ1 BJ5 |
CJ3 CJ6 |
| E2 - A adquisición dos coñecementos de estatística e investigación de operacións necesarios para a incorporación en equipos multidisciplinares pertencentes a diferentes sectores profesionais. | AJ5 AJ6 AJ10 |
BJ1 BJ4 BJ5 |
CJ3 CJ6 CJ8 |
| E4 - Coñecer as aplicacións dos modelos da estatística e a investigación de operacións. | AJ5 AJ10 |
BJ1 BJ4 BJ5 |
CJ6 |
| E5 - Coñecer algoritmos de resolución dos problemas e manexar o software axeitado. | AJ5 AJ6 AJ10 |
BJ1 BJ5 |
CJ3 CJ6 CJ8 |
| E12 - Realizar inferencias respecto aos parámetros que aparecen no modelo. | AJ5 AJ6 AJ10 |
BJ1 BJ4 BJ5 |
CJ3 CJ6 CJ8 |
| E19 - Tratamento de datos e análise estatística dos resultados obtidos. | AJ5 AJ6 AJ10 |
BJ1 BJ4 BJ5 |
CJ3 |
| E27 - Obter os coñecementos precisos para unha análise crítica e rigurosa dos resultados. | AJ5 AJ10 |
BJ1 BJ4 BJ5 |
CJ6 CJ8 |
| E28 - Complementar a aprendizaxe dos aspectos metodolóxicos con apoio de software. | AJ6 AJ10 |
BJ5 |
CJ3 CJ6 CJ8 |
| E78 - Fomentar a sensibilidade cara os principios do pensamento científico, favorecendo as actitudes asociadas ao desenvovemento dos métodos matemáticos, como: o cuestionamento das ideas intuitivas, a análise crítica das afirmacións, a capacidade de análise e síntese ou a toma de decisións racionais | AJ5 AJ10 |
BJ1 BJ4 BJ5 |
CJ6 CJ8 |
| E82 - O estudiante será capaz de comprender a importancia da Inferencia Estatística como ferramenta de obtención de información sobre a población en estudo, a partir do conxunto de datos observados dunha mostra representativa de esta. Para iso deberá recoñecer a diferenza entre estatística paramétrica e non paramétrica. | AJ5 AJ10 |
BJ1 BJ4 BJ5 |
CJ6 CJ8 |
| E84 - Ser quen de manexar diverso software (en particular R) e interpretar os resultados que proporcionan nos correspondentes estudos prácticos. | AJ5 AJ6 AJ10 |
BJ4 BJ5 |
CJ3 |
| E86 - Soltura no manexo da teoría da probabilidade e as variables aleatorias. | AJ5 AJ10 |
BJ1 BJ4 BJ5 |
CJ6 |
| Contents |
| Topic | Sub-topic |
| 1. Basic concepts of probability and statistics revisited. |
a. Probability. Random variables and main discrete and continuous distributions. Multivariate distributions. b. Statistical inference: estimation, hypothesis testing and confidence intervals. |
| 2. R statistical programming language revisited. | a. Introduction to R. First steps. Internal functions. Help in R. Functions, loops, vectors. Statistical functions. Plots. Recursivity. R studio. b. Main probability distributions in R. c. Introduction to simulation in R. d. Descriptive statistics in R. e. Hipothesis testing and confidence intervals with R. |
| 3. Linear statistical models. | a. The simple linear regression model. Basic assumptions. Estimation. Testing. Prediction. Model diagnostics. b. The multivariate linear regression model. Basic assumptions. Estimation. Testing. Prediction. Model diagnostics. c. Basic models in experimental desing. One-way and two-way Analysis of Varianza (ANOVA), with or without interaction. Basic assumptions. Estimation. Testing. Model diagnostics. d. The multiple testing problem. False discovery rate. |
| 4. Introduction to stochastic processes. | a. Simple random walk. b. Poisson process and renewal processes. Birth-death processes. c. Markov processes. Markov Chains. |
| 5. Introduction to resampling methods. | a. The uniform Bootstrap. Computing the bootstrap distribution: exact distribution and aproximated distribution using Monte Carlo. Examples. Aplication of the bootstrap for estimating the precision and the bias of an estimator. b. Variations of the uniform Bootstrap. Parametric Bootstrap, symmetrized Bootstrap and smoothed Bootstrap. Discussion and examples. c. Bootstrap methods to construct confidence intervals: percentile method, percentil-t method, simmetrized percentil-t method. Examples. Simulation studies . |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Oral presentation | A5 A6 A10 B1 B4 B5 C8 | 24 | 36 | 60 |
| ICT practicals | A5 A6 A10 B4 B5 C3 C6 | 18 | 36 | 54 |
| Multiple-choice questions | A5 B1 B5 C8 | 1 | 9 | 10 |
| Problem solving | A5 A6 A10 B1 B4 B5 C3 C6 C8 | 4 | 16 | 20 |
| Personalized attention | 6 | 0 | 6 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Oral presentation | Presentation computer. |
| ICT practicals | Datasets statistical analysis using R. |
| Multiple-choice questions | Multiple-choice test on concepts. |
| Problem solving | Deciding statistical tools and strategies for problem solving. Linear model formulation. Design of Experiments. Formulation of resampling plans. |
| Personalized attention |
|
|
| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| ICT practicals | A5 A6 A10 B4 B5 C3 C6 | Computer lab using the open statistical software R. | 30 |
| Problem solving | A5 A6 A10 B1 B4 B5 C3 C6 C8 | Original work on some of the topics of the course concerning some interesting setup in Bioinformatics. | 40 |
| Multiple-choice questions | A5 B1 B5 C8 | Comprehension Test | 30 |
| Assessment comments | |||
|
The assessment will be carried out using a test on R labs, an individual student work, as well as a written concept test. The concept test score will be 30% of the total qualification, the test on R labs will correspond to 30% of the global score, while the remaining 40% will correspond to the individual student work, that has to be presented orally. To pass the subject is necessary to obtain a score of at least 5 out of 10 overall. On July opportunity, students could avoid those test with scores of at least 4 out of 10 in January tests. |
|||
| Sources of information |
| Basic |
Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman and Hall
Davison, A.C. and Hinkley, D.V. (1997). Bootstrap Methods and their Application. Cambridge University Press
Peña Sánchez de Rivera, D. (2000). Estadística: Modelos y Métodos. Alianza Editorial
Cao Abad, R., Francisco Fernández, M., Naya Fernández, S., Presedo Quindimil, M.A., Vázquez Brage, M (2001). Introducción a la Estadística y sus Aplicaciones. Pirámide
Ewens, W.J. and Grant, G.R. (2005). Statistical Methods in Bioinformatics. Springer
Ross, S.M. (1995). Stochastic Processes. Wiley |
| Complementary | |
| Recommendations | ||||||
| Subjects that it is recommended to have taken before |
| Subjects that are recommended to be taken simultaneously | |||||
|
| Subjects that continue the syllabus | ||||||
|
| Other comments | |