Biostatistics and Bioinformatics
A.Y. 2020/2021
Learning objectives
This module teaches the basic principles and practices of the main computational and statistical methods necessary to analyze the "omics" data that current technologies make available.
Expected learning outcomes
At the end of the course, students will be able to examine the genomic information generated by recent molecular biology techniques (eg NGS and microarray).
The students involved the knowledge of IT and statistical tools for the management of genomic data with particular regard to sequencing techniques and all those methods aimed at analyzing, understanding genomic data and animal genetic variability. The course therefore intends to provide students with knowledge of mathematical / statistical and IT tools from the pre-processing of the raw data, to their analysis and their biological interpretation.
The students involved the knowledge of IT and statistical tools for the management of genomic data with particular regard to sequencing techniques and all those methods aimed at analyzing, understanding genomic data and animal genetic variability. The course therefore intends to provide students with knowledge of mathematical / statistical and IT tools from the pre-processing of the raw data, to their analysis and their biological interpretation.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Second semester
Given the current pandemic, only lectures will be given in presence, but can also be followed remotely. The practical will be given remotely using Microsoft Teams.
Course syllabus
Lectures (32 hours):
- Data, variables and their representation and synthesis (3 hours)
- Probability: random variables and distributions (3 hours)
- Correlation between variables, linear and non-linear regression (3 ore)
- Test for hypothesis, type I and type II errors, statistical power of a test (3 hours)
- Parametric tests: different types of t-tests (3 hours)
- Parametric tests: analysis of variance, levels and factors, inter- and intra-group variability, F-test (3 hours)
- Post hoc-comparisons, different types of corrections for multiple hypothesis testing, Bonferroni and FDR methods (3 hours)
- Non-parametric equivalents of t-tests (3 hours)
- Non-parametric equivalents of ANOVA (3 hours)
- Goodness of fit tests, tests for proportions (3 hours)
- Survival analysis (3 hours)
Practical (32 hours):
Application of the topics explained during lecture to real world datasets using the SPSS software.
- Data, variables and their representation and synthesis (3 hours)
- Probability: random variables and distributions (3 hours)
- Correlation between variables, linear and non-linear regression (3 ore)
- Test for hypothesis, type I and type II errors, statistical power of a test (3 hours)
- Parametric tests: different types of t-tests (3 hours)
- Parametric tests: analysis of variance, levels and factors, inter- and intra-group variability, F-test (3 hours)
- Post hoc-comparisons, different types of corrections for multiple hypothesis testing, Bonferroni and FDR methods (3 hours)
- Non-parametric equivalents of t-tests (3 hours)
- Non-parametric equivalents of ANOVA (3 hours)
- Goodness of fit tests, tests for proportions (3 hours)
- Survival analysis (3 hours)
Practical (32 hours):
Application of the topics explained during lecture to real world datasets using the SPSS software.
Prerequisites for admission
No pre-required knowledge is necessary for this course.
Teaching methods
The course consists of both theorical lectures and practical using the SPSS software.
Teaching Resources
Slides used for the lectures and the material for the practical will be shared using Microsoft Teams.
The following is a text that can be used to further explore the topic, but it is optional: Biostatistics for animal science: an introductory text. M. Kaps & W. Lamberson, CABI Editors.
The following is a text that can be used to further explore the topic, but it is optional: Biostatistics for animal science: an introductory text. M. Kaps & W. Lamberson, CABI Editors.
Assessment methods and Criteria
The exam is made of two parts: a group assignment (2-3 people per group), that will be given in the last week of the course, and a mandatory oral exam.
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING - University credits: 6
Practicals: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Bologna Marco