Course catalogue doctoral education - VT19

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Title Omics data analysis: From quantitative data to biological information
Course number 2523
Programme 1-Included in several programmes
Language English
Credits 3.0
Date 2014-11-10 -- 2014-11-21
Responsible KI department Department of Oncology-Pathology
Specific entry requirements
Intended learning outcomes After completed course, the student will be able to:
* Understand and perform the basics of a data analysis workflow for omics expression data (transcriptomics, proteomics, metabolomics)
* Understand the aspects of study design, experimental planning and sample selection
* Know how to do basic quality control of data by use of boxplots, PCA etc
* Know what normalization, data transformation etc means and what it does to your data
* Know the principles of some basic statistics such as t-test and false discovery rate
* Know the principles of PCA and PLS, when to apply and how to validate those models
* Use tools for network and pathway analysis
* Use tools for GO annotation/enrichment
Contents of the course * The omics data analysis workflow: from quantitative data to biological information (emphasis on analysis of transcriptomics, proteomics, metabolomics expression data) * Clinical experimental design and sample selection * Introduction to data transformation and normalization. * Introduction to basic statistics in omics data analysis: significance test/p-values/false discovery rate * Introduction to multivariate statistical analysis (PCA and PLS): Outlier and pattern analysis by PCA, supervised analysis by PLS/O-PLS, finding significantly influential features, data model validity etc. * Introduction to Gene Ontology and enrichment analysis * Introduction to network and pathway analysis * Case studies on clinical biomarker discovery * Literature study with a critical view on how omics data is analyzed in clinical research. * Current state of the art in omics data analysis is highlighted through case studies, literature studies and demonstrations.
Teaching and learning activities The course contains lectures and software demonstrations. The students will participate in a literature study with discussions in seminar groups as well a student exam project. The students will also be able to download and use some of the software in workshops during the course.
Compulsory elements * Attendance on lectures and data analysis demonstrations. * Attendance to literature study discussion seminar. * Attendance to examination seminar and hand in the written examination assignments. * Extra written literature study can be used to compensate absence.
Examination * A literature study with a critical view on an omics data analysis subject shall be discussed in a literature group. Discussion points are reported and presented for the group. * The students shall do an omics data analysis project (preferably related to their own research) including the different parts taken up at the course. This is discussed in an examination seminar and also handed in as a written exam.
Literature and other teaching material Villavicencio-Diaz TN, Rodriguez-Ulloa A, Guirola-Cruz O, Perez-Riverol Y. Bioinformatics tools for the functional interpretation of quantitative proteomics results. Curr Top Med Chem. 2014;14(3):435-49. Chapter 8. Statistical analysis. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry, First Edition. Ingvar Eidhammer, Harald Barsnes, Geir Egil Eide and Lennart Martens. © 2013 John Wiley & Sons, Ltd. Malik R, Dulla K, Nigg EA, Körner R. From proteome lists to biological impact- tools and strategies for the analysis of large MS data sets. Proteomics 2010;10(6):1270-1283. Dunkler D, Sánchez-Cabo F, Heinze G. Statistical Analysis Principles for Omics Data. Methods in Molecular Biology. Totowa, NJ: Humana Press; 2011. Smit S, Hoefsloot HCJ, Smilde AK. Statistical data processing in clinical proteomics. Journal of Chromatography B 2008;866(1-2):77-88. Eriksson L, Antti H, Gottfries J, et al. Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm). Anal Bioanal Chem 2004;380(3):419-429.
Number of students 15 - 30
Selection of students Student motivation to why they need to learn about omics data analysis.
More information The course is scheduled between 9 am and 5 pm every day. The students should preferably bring their own computer. This is primarily a PhD course but also others are most welcome.

This course is included in the following doctoral programmes: Allergy, immunology and inflammation (Aii), Developmental biology and cellular signaling (DECS), Infection biology, Regenerative medicine as well as Tumor biology and oncology. See http://ki.se/en/education/doctoral-programmes.

Additional course leader
Earlier evaluation of the course Evaluation report
Course responsible Lina Hultin-rosenberg
Department of Oncology-Pathology
+46-8-52481212
lina.hultin-rosenberg@ki.se

Tomtebodavägen 23A

17121
Solna
Contact person Lina Hultin-rosenberg
Institutionen för onkologi-patologi
+46-8-52481212
lina.hultin-rosenberg@ki.se

Tomtebodavägen 23A

17121
Solna


Helena Bäckvall
Institutionen för onkologi-patologi
0851770363
helena.backvall@ki.se


Ann-Sofi Sandberg
Institutionen för onkologi-patologi

Annsofi.Sandberg@ki.se