BIOSCI 738 : Advanced Biological Data Analysis
Science
2020 Semester One (1203) (15 POINTS)
Course Prescription
Course Overview
- Analysis of variance of data collected from completely randomised, randomised block, split-plot, and repeated measures designs
- Controlling error rates; Multiple comparison procedures,
- Model diagnostics; Data transformations
- Mixed models
- Multiple regression; grouped regression
- Randomisation tests, Bootstrapping
- Ordination and Dimension reduction
- Principal Component Analysis, PCA, and biplots
- Discriminant Functions and Cluster Analysis
- Distance-based methods (Principal Coordinate Analysis, PCO, Multidimensional scaling, NMDS)
Capabilities Developed in this Course
| Capability 1: | Disciplinary Knowledge and Practice |
| Capability 2: | Critical Thinking |
| Capability 3: | Solution Seeking |
| Capability 4: | Communication and Engagement |
Learning Outcomes
- Identify, describe and analyse completely randomised, randomised block, split-plot, and repeated measures designs and when their use is appropriate; Appreciate the connection between the choice of design of an experiment and the analysis of data collected. (Capability 1 and 2)
- Understand the role of blocking in controlling nuisance sources of variation. Distinguish between fixed and random effects and know what is meant by a mixed model. (Capability 2 and 3)
- Understand the role of replication and randomisation for the validity of experimental results. Understand why a statistically significant result does not necessarily imply a true discovery. (Capability 2, 3 and 4)
- Demonstrate the importance of checking model assumptions. Know how to interpret residual plots. Demonstrate the effective presentation of the results of a statistical analysis, including both tabular and graphical methods. (Capability 1, 2, 3 and 4)
- Demonstrate knowledge of the use of resampling procedures in statistical inference, including permutation and randomisation tests and the use of bootstrap techniques. (Capability 1, 3 and 4)
- Understand multivariate data, its use and the problems in its analysis and interpretation (Capability 1 and 3)
- Analyse multivariate data and the major techniques used to analyse it.; Principal Components Analysis (PCA) and visualisation of its output with biplots, Discriminant analysis. (Capability 1, 3 and 4)
- Understand distance based methods for the analysis of multivariate data, Principal Coordinates Analysis, Multidimensional scaling. Understand the conditions under which it is appropriate to use each method for elucidating patterns in multivariate data. (Capability 1, 3 and 4)
- Analyse multivariate data using cluster analysis. (Capability 1, 2 and 3)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Assignments | 55% | Individual Coursework |
| Workshops | 5% | Individual Coursework |
| Final Exam | 40% | Individual Examination |
| 3 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
| Assignments | ||||||||||
| Workshops | ||||||||||
| Final Exam | ||||||||||
Learning Resources
- Recommended, available on short loan: STATISTICAL METHODS IN BIOLOGY: Design and Analysis of Experiments and Regression, Welham, Gezan, Clark & Mead, 2014.
- Available as an e-book: Modern Statistics for Modern Biology, Susan Holmes, Wolfgang Huber, 2018
Special Requirements
Workload Expectations
This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.
For this course, you can expect 2 hours of lectures, a 1 hour practical workshop, 3 hours of reading and thinking about the content and 4 hours of work on assignments and/or test preparation.
Digital Resources
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
Copyright
The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.
You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.
Inclusive Learning
All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.
Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at http://disability.auckland.ac.nz
Special Circumstances
If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page: https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
This should be done as soon as possible and no later than seven days after the affected test or exam date.
Student Feedback
During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.
At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.
Your feedback helps to improve the course and its delivery for all students.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html).
Disclaimer
Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.