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| Title | Omics data analysis: From raw data to biological information |
|---|---|
| Course number | 2523 |
| Programme | Tumörbiologi och onkologi (FoTO) |
| Language | English |
| Credits | 3.0 |
| Date | 2011-11-14 -- 2011-11-25 | Responsible KI department | Institutionen för onkologi-patologi |
| Specific entry requirements | |
| Intended learning outcomes | After completed course, the student will be able to use common bioinformatics terminology and explain the principles of the most common data analysis methods in 'omics research. The student will acquire knowledge in basic statistics such as t-test and false discovery rate. The student will also gain a toolbox for multivariate analysis (e.g. PCA and OPLS) of large-scale datasets acquired in 'omics research. The importance of study design and experimental planning will also be obvious after this course. The student will be able to analyze the 'omics data on a canonical pathway level, GO annotations, KEGG pathway analysis, and analyze overlap between data sets. After this course, the student will be able to understand the analysis of ¿omics data to such an extent that he/she will be able to take advantage of an 'omics core facility and collaborate with 'omics data analysis researchers. Further, the student will be able to discuss on the principles of (multivariate) data analysis in the field of systems biology. |
| Contents of the course | * Clinical experimental design and sample selection * The 'omics data analysis workflow in general * Introduction to statistics in 'omics data analysis: false discovery rate/p-values and ratios by SAM or similar software. The terms qualitative vs quantitative, and accuracy vs precision are discussed. * 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 study and demonstrations. * The students will be able to download some of the software tools and use on their own laptops. * The course is a natural continuation of the course ""Proteomics by mass spectrometry: When and how."" |
| Teaching and learning activities | The course contains lectures, literature studies with discussions in seminars, data analysis demonstrations. The students will also be able to use some of the software 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 handed in as a written exam. |
| Literature and other teaching material | J Chromatogr B 866 (2008) 77-88, Statistical data processing in clinical proteomics, Smit S, Hoefsloot HC, Smilde AK. Brief Bioinform (2008) 9 (2): 102-118, Approaches to dimensionality reduction in proteomic biomarker studies, Melanie Hilario and Alexandros Kalousis J Proteome Res. 2008 Jun;7(6):2332-41, Proteomic data analysis workflow for discovery of candidate biomarker peaks predictive of clinical outcome for patients with acute myeloid leukemia, Forshed J, Pernemalm M, Tan CS, Lindberg M, Kanter L, Pawitan Y, Lewensohn R, Stenke L, Lehtiö J. |
| Number of students | 12 - 24 |
| Selection of students | The applicant shall enclose a motivation (max 300 words) of why he/she wants to participate in the course. |
| More information | The course will be given in the lecture hall Sievertrummet at the Karolinska Hospital area. Two weeks full time, Monday to Friday. |
| Additional course leader | |
| Latest course evaluation | Not available |
| Course responsible |
Jenny Forshed Institutionen för onkologi-patologi jenny.forshed@ki.se |
| Contact person | - |
