Timetable

  1. Timetable of the lecture “Systems Biology” – summer semester 2023/2024
  2. SYLLABUS of the lecture “Systems Biology” – summer semester 2023/2024

Lecture Systems Biology
summer semester 2023/2024

(This lecture is non-obligatory)

Lectures will be held on Wednesdays only online.

Data Temat
6 March

11:45-13:15

 1. Introduction to R language (dplyr library)

2. Simple data plots: scatterplot, boxplot and (ggplot2 library)

Dr hab. Tomasz Górecki, prof. UAM
6 March

13:30-15:00

 1. Descriptive statistics: mean, median, mode, variance, standard deviation, standard error
2. Statistical tests – introductionDr hab. Tomasz Górecki, prof. UAM
20 March15:00-16:30 Introduction to Systems Biology

Dr Marcin Sajek
13 March

10:00 – 11:30

 1. Goodness of fit tests: exact test, test chi2 and test G
2. Comparison of two populations: – t test for independent and dependent samples. Wilcoxon test

Dr hab. Tomasz Górecki, prof. UAM
13 March

11:45 – 13:15

 1. Assumptions in statistical tests: normality, homoscedasticity of variance – Box-Cox method
2. Comparison of multiple samples – one-way and multiway analysis of variance (ANOVA). Kruskal-Wallis test and Friedman test. Post-hoc comparison

Dr hab. Tomasz Górecki, prof. UAM
13 March

13:30 – 15:00

 Independence tests. Mosaic plot, balloon plot and association plot

Dr hab. Tomasz Górecki, prof. UAM
03 April
10:00 -11:30		 Pearson and Spearman coefficient of correlation. Application of scatterplot and sunflower plot to correlation analysisDr hab. Tomasz Górecki, prof. UAM
03 April
11:45 -13:15		 Simple regression. Multiple regressionDr hab. Tomasz Górecki, prof. UA
03 April
13:30 -15:00		 BioconductoRDr hab. Tomasz Górecki, prof. UAM

SYLLABUS

Summer semester 2023/2024

Course Genetics of Human Development
Host Institution Institute of Human GeneticsPolish Academy of Sciences

Strzeszyńska Street, 32
Language English
The expected effects of teaching in terms of:  knowledge, skills and social qualifications Ph. D. student is supposed to gain knowledge in statistical methods used in biology. In particular, the following specific topics will be elaborated:

  1. R language – basics of programming and visualization.
  2. Main descriptive statistics.
  3. Basics of statistical tests and estimation.
  4. Main assumptions in parametric tests.
  5. Tests to compare two or more means.
  6. Measuring and testing dependency
  7. Analysis of correlation and regression.
  8. Basics of BioconductoR.
Type of course Facultative
Semester/year summer semester 2023/2024
First name/family name of the person responsible for the course dr hab. Tomasz Górecki, prof. UAM
First name/family name of the person responsible for the exam dr hab. Tomasz Górecki, prof. UAM
Format Lecture will be held in English with usage of audio-visual equipment.
Basic and additional requirements Skills in English
Number of ECTSs 2 ECTS
ECTSs summary 1 ECTS corresponds to 25-30 hours of personal studies focused on broadening knowledge based on suggested bibliography (vide bibliography list below).
Method of teaching Lectures will be held using power point presentation and a multimedia projector
Method  of evaluation oral exam
Prerequisite for passing Positive score at the exam
Topics
  1. Introduction to R language (dplyr library)
  2. Simple data plots: scatterplot, boxplot and (ggplot2 library)
  3. Descriptive statistics: mean, median, mode, variance, standard deviation, standard error
  4. Statistical tests – introduction
  5. Goodness of fit tests: exact test, test chi2 and test G
  6. Comparison of two populations: – t test for independent and dependent samples. Wilcoxon test
  7. Assumptions in statistical tests: normality, homoscedasticity of variance – Box-Cox method
  8. Comparison of multiple samples – one-way and multiway analysis of variance (ANOVA). Kruskal-Wallis test and Friedman test. Post-hoc comparison
  9. Independence tests. Mosaic plot, balloon plot and association plot
  10. Pearson and Spearman coefficient of correlation. Application of scatterplot and sunflower plot to correlation analysis
  11. Simple regression. Multiple regression
  12. BioconductoR
Additional material Presentation of each lecture in PDF format and R scripts
Bibliography
  1. Biecek, P. (2016). Odkrywać! Ujawniać! Objaśniać!, Wydawnictwo UW.
  2. Biecek, P. (2017). Przewodnik po pakiecie R, GiS.
  3. Crawley, M.J. (2012), The R Book, Wiley.
  4. Gągolewski, M. (2014). Programowanie w języku R, PWN.
  5. Górecki, T. (2011). Podstawy statystyki z przykładami w R, BTC.
  6. James, G., Witten, D., Hastie, T., Tibshirani, R. (2017). An Introduction to Statistical Learning with Applications in R. Springer.
  7. Koronacki, J., Mielniczuk, J. (2009). Statystyka dla studentów kierunków technicznych i przyrodniczych, WNT
  8. Zieliński, R. (1990). Siedem wykładów wprowadzających do statystyki matematycznej, PWN