| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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Group 1 |
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For following programme components you must have acquired a credit certificate, exemption, already tolerated unsatisfactory grade or selected tolerable unsatisfactory grade.
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Concepts of Bioinformatics DL (3584)
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4,0 stptn |
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Linear Models DL (3577)
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5,0 stptn |
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Or group 2 |
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For following programme components you must have acquired a credit certificate, exemption, already tolerated unsatisfactory grade or selected tolerable unsatisfactory grade.
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Concepts of Bioinformatics DL (3584)
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4,0 stptn |
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Linear Models DL (3577)
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8,0 stptn |
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Master Bioinformatics - distance learning | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student knows the societal relevance of statistics and data science. | | | - DC
| The student can reflect on and explain the societal relevance of a task, particularly within the programme specialization | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective writer, both within their own field as well as across disciplines. | | | - DC
| The student is an effective oral communicator in their own field. | | | - DC
| The student is an effective oral communicator, both within their own field as well as across disciplines. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student will learn about state-of-the-art statistics and bioinformatics methods for the analysis of microbiome studies.
The following topics are included:
- what is the microbiome
- from sequencing reads to OTU and ASV tables
- data characteristics: overdispersion, sparseness, compositionality, ...
- data exploration and visualization: barplots, PCoA plots, RCM plots
- diversity indices (alpha and beta diversity)
- testing for differential abundance
- advanced modelling of microbiome data (e.g. longitudinal data analysis)
- advanced case studies (example of intervention microbiome experiments, basic settings and differential abundance across intervention levels and ecosystem of microbiome (OTUs, alpha diversity, family level etc))
- advanced modelling of microbiome data (High dimensional microbiome biomarkers)
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Distance learning ✔
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Project ✔
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Q&A ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 50 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | The student may bring a copy of her/his own paper (including appendices and R code) and her/his slides for the presentation to the exam. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | The student must have submitted the paper in time. |
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| Consequences | If the paper is not submitted or is not submitted in time, the student will fail for the course. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | If the student received a pass mark for the papers in the first chance exam period, the student may keep these partial marks and in the second chance exam the student must present the paper and will receive questions related to the papers (just like for the first chance exam). If the student did not receive a pass mark for the papers in the first chance exam period, the student will receive new assignments. |
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| Prerequisites |
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basics of statistical inference and probability, linear models, basics of multivariate and high dimensional data analysis, multiple hypothesis testing and FDR control, and programming in R
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| Compulsory course material |
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Lecture notes or slides will be made available on Blackboard. |
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| second year Master Biostatistics - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Data Science - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Quantitative Epidemiology - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student knows the societal relevance of statistics and data science. | | | - DC
| The student can reflect on and explain the societal relevance of a task, particularly within the programme specialization | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective writer, both within their own field as well as across disciplines. | | | - DC
| The student is an effective oral communicator in their own field. | | | - DC
| The student is an effective oral communicator, both within their own field as well as across disciplines. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The student will learn about state-of-the-art statistics and bioinformatics methods for the analysis of microbiome studies.
The following topics are included:
- what is the microbiome
- from sequencing reads to OTU and ASV tables
- data characteristics: overdispersion, sparseness, compositionality, ...
- data exploration and visualization: barplots, PCoA plots, RCM plots
- diversity indices (alpha and beta diversity)
- testing for differential abundance
- advanced modelling of microbiome data (e.g. longitudinal data analysis)
- advanced case studies (example of intervention microbiome experiments, basic settings and differential abundance across intervention levels and ecosystem of microbiome (OTUs, alpha diversity, family level etc))
- advanced modelling of microbiome data (High dimensional microbiome biomarkers)
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|
|
|
|
|
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Distance learning ✔
|
|
|
|
Project ✔
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|
|
|
Q&A ✔
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|
|
|
|
|
|
|
Paper ✔
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|
|
|
Presentation ✔
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|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 50 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | The student may bring a copy of her/his own paper (including appendices and R code) and her/his slides for the presentation to the exam. |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | The student must have submitted the paper in time. |
|
|
|
| Consequences | If the paper is not submitted or is not submitted in time, the student will fail for the course. |
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|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | If the student received a pass mark for the papers in the first chance exam period, the student may keep these partial marks and in the second chance exam the student must present the paper and will receive questions related to the papers (just like for the first chance exam). If the student did not receive a pass mark for the papers in the first chance exam period, the student will receive new assignments. |
|
|
|
|
|
| Prerequisites |
| |
basics of statistical inference and probability, linear models, basics of multivariate and high dimensional data analysis, multiple hypothesis testing and FDR control, and programming in R
|
|
|
| Compulsory course material |
| |
Lecture notes or slides will be made available on Blackboard. |
|
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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