Statistical Analysis for Health Sciences

Code:

M4093

Acronym:

M4093

Level:

400

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2019/2020 - 2S

Active?	No
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Master in Bioinformatics and Computational Biology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
E:BBC	0	PE_Bioinformatics and Computational Biology	1	-	6	42	162
M:BBC	0	The study plan since 2018	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

The goal of this course is to provide knowledge on  mathematical/statistical methodologies that are commonly used in Bioinformatics and Computational Biology.

Learning outcomes and competences

At the end of the course, the students are expected to:

• to understand the approached mathematical/statistical models
• realize the conditions for application of those models
• correctly identify scenarios for application of the models
• implement the models through the use of an adequate statistical analysis software
• be able to critically analize the results.  

Working method

Presencial

Program

1. Review of parametric hypothesis tests on one sample and on two samples (independent and paired). Significance level, type-I and type-II errors, power. Permutation tests.
2. Multiple testing. Family-wise error rate. Bonferroni correction. Sidák procedure. Single-step minP procedure of Westfall and Young. Permutation-based single-step minP. Single-step maxT procedure of Westfall and Young. Holm step-down procedure. Step-down minP/maxT procedure of Westfall and Young. False discovery rate. Step-up Benjamini and Hochberg method. Benjamini and Yekutieli method.
3. Completely randomized designs with one factor - the ANOVA1 model: sources of variation, test statistic, parameters estimation. Sample size determination. 
4. Randomized complete block designs - the ANOVA2 RCBD model. Sample size determination.
5. Factorial designs. ANOVA2. Interactions. Replicates number determination. 
6. Designs to study variance. Models with a single random effect. Models with two random effects. Models with mixed effects. Sample sizes determination. 
7. Choice of a topic, depending on the students' needs, from Log-Linear Models/Survival Analysis/Mixed-effects logistic regression/Principal Component Analysis/Some clustering methods.

Mandatory literature

Rita Gaio; Apontamentos preparados pela professora

Complementary Bibliography

Ewens Warren J.; Statistical methods in bioinformatics. ISBN: 0-387-40082-6
Gentleman Robert 340; Bioinformatics and computational biology solutions using R and Bioconductor. ISBN: 0-387-25146-4
Douglas C. Montgomery; Design and analysis of experiments. ISBN: 0-471-15746-5
Dean, Voss & Draguljic; Design and analysis of experiments - 2nd Edition, Springer, 2017

Teaching methods and learning activities

Classes will be simultaneously theoretical and practical, with several real examples of application and always making use of the free programing language R. Withount neglecting 
the mathematica basic principles of the models, a special enphasis will be put on the computational implementation and recognition of scenarios for model application.

Software

R

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	33,30
Trabalho escrito	66,70
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	120,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

In accordance with the rules established by the Conselho Pedagógico from FCUP. 

In order to be approved, each student must score: at least 30% of the total score of each written project, at least 30% of the total score of the written examination and, all together, the elements of evaluation must score 9.5 values (out of 20), or higher.

Calculation formula of final grade

0.33*(mark of the 1st written project) + 0.33*(mark of the 2nd written project) + 0.33*(mark of the written examination)

Classification improvement

The mark obtained in the written reports cannot be improved.