Syllabus database for doctoral courses
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Syllabus database for doctoral courses
SYLLABI FOR DOCTORAL COURSES
| Swedish title | Omics dataanalys: Från rådata till biologisk information |
|---|---|
| English title | Omics data analysis: From raw data to biological information |
| Course number | 2523 |
| Credits | 3.0 |
| Responsible KI department | Department of Oncology-Pathology |
| Specific entry requirements | |
| Grading | Passed /Not passed |
| Established by | The Board of Doctoral Education |
| Established | 2012-02-21 |
| Purpose of the course | |
| Intended learning outcomes | After completed course, the student will * Be able to use common chemomterics- and bioinformatics terminology * Know the principles of some basic statistics such as t-test and false discovery rate. * Know the principles of PCA and PLS * Know when multivariate and univariate methods are applicable and how to apply them * Be able to evaluate a PCA and a PLS model * Know what normalization, data transformation, etc means and what it does to your data * Know the important aspects of study design, experimental planning and sample selection * Be able to use tools for canonical pathway level analysis * Be able to use tools for GO annotation * Know tools for KEGG pathway analysis * Understand the analysis of ¿omics data to such an extent that you will be able to take advantage of an 'omics core facility and collaborate with ¿omics data analysis researchers. |
| Contents of the course | * The 'omics data analysis workflow * Clinical experimental design and sample selection * Introduction to statistics in 'omics data analysis: false discovery rate/p-values and fold change. * Qualitative vs quantitative data analysis * Accuracy and precision in 'Omics data. * Introduction to multivariate statistical analysis (PCA and PLS): Outlier and pattern analysis by PCA, supervised analysis by O-PLS modeling, finding significantly influential features, data model validity etc. (software: SIMCA) * Introduction to pathway analysis: the possibilities of canonical pathway analysis. (software: Ingenuity) * Case studies on clinical biomarker discovery * Literature studies 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. The students will be able to download some of the software tools and use on their own laptops during the course. |
| Teaching and learning activities | The course contains lectures, literature studies with discussions in seminars, and data analysis demonstrations. The students will also be able to use some of the software in workshops during the course. |
| Compulsory elements | * Attendance on lectures and data analysis demonstrations * 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 a data analysis 'omics subject shall be discussed in a literature/expert group. Discussion questions is reported and presented for the group. * The students shall do data analysis on a 'omics project (preferably related to their research) including the different moments taken up at the course. This is discussed in an examination seminar and handed in as a written exam. |
| Literature and other teaching material | Madsen R, Lundstedt T, Trygg J. Chemometrics in metabolomics¿ A review in human disease diagnosis. Analytica Chimica Acta 2010;659(1-2):23¿33. 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. 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. |
Course responsible |
Lina Hultin-rosenberg Department of Oncology-Pathology +46-8-52481212 lina.hultin-rosenberg@ki.se Tomtebodavägen 23A 17121 Solna |
| Contact person |
Jenny Forshed Department of Oncology-Pathology jenny.forshed@ki.se |
