STA5MB
MODELS FOR BIOINFORMATICS
STA5MB
2018
Credit points: 15
Subject outline
The advance in omics technology have seen an exponential increase in the volume of biological data in the last ten years. Statistical models play important roles in drawing conclusions from and making sense of the complex and often noisy omics data. This subject will introduce students to statistical issues and potential solutions to problems commonly encountered at various stage of omics data analysis, from data acquisition, alignment, quality controls, data analysis, visualization and interpretation. Topics covered will include introduction to next-generation sequencing and microarray technologies, batch effects and other unwanted variations, multiple hypothesis testing problems, statistical tests and models for high-dimensional data, data visualization and utilizing biological database via pathway-based analysis. Students will also be introduced to intermediate level of R programming language, including writing customized scripts and functions, developing R packages and working with 'pipe' operator. Bioconductor packages (www.bioconductor.org) and other freely-available Bioinformatics software will be used for all Lab sessions.
SchoolSchool Engineering&Mathematical Sciences
Credit points15
Subject Co-ordinatorAgus Salim
Available to Study Abroad StudentsYes
Subject year levelYear Level 5 - Masters
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites Must be admitted in the Master of Data Science (SMDS) and have passed STM4PM. Other students require Coordinators Approval.
Co-requisitesN/A
Incompatible subjectsN/A
Equivalent subjectsN/A
Special conditionsN/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Statistics in Human Genetics and Molecular Biology | Recommended | Cavan Reilly. 2009 | CRC Press |
| Readings | R programming for Bioinformatics | Recommended | Robert Gentleman. 2009 | CRC Press |
| Readings | Online learning materials | Prescribed | Agus Salim. 2017 | La Trobe Univ |
Graduate capabilities & intended learning outcomes
01. Demonstrate specialised theoretical and technical skills in solving statistical issues in bioinformatics
- Activities:
- Modeled via concepts, illustrations and examples in online lectures and reading materials. Problems in computer labs will require problem-solving skills based on realistic datasets
02. Use specialised cognitive and technical skills to critically analyse, reflect on and synthesise complex information, problems, concepts and theories relevant to solving statistical issues in bioinformatics
- Activities:
- Modeled via concepts, illustrations and examples in online lectures and reading materials. Problems in computer labs will require problem-solving skills based on realistic datasets
03. Apply established theories relevant to statistical issues in bioinformatics
- Activities:
- Modeled via concepts, illustrations and examples in online lectures and reading materials. Some questions in computer labs will require students apply their statistical theory knowledge to solve real-life problems in bioinformatics
04. Use advanced communication skills to transmit knowledge and ideas of the role of statistics in bioinformatics to others
- Activities:
- Modeled via illustrations and examples in online lectures and reading materials. Examples on effective communications of statistical concepts to non-statisticians will be provided
05. Demonstrate autonomy, expert judgement, adaptability and responsibility as an applied statistician
- Activities:
- Students solving real-life problems in computer labs independently with inputs from tutor/lecturer. The problems will require students to exercise their statistical judgement and adapt existing methods independently
Subject options
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Melbourne, 2018, Semester 2, Blended
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorAgus Salim
Class requirements
Unscheduled Online ClassWeek: 31 - 43
One 2.0 hours unscheduled online class per week on any day including weekend during the day from week 31 to week 43 and delivered via online.
"Lectures in pre-recorded online video format"
Computer LaboratoryWeek: 31 - 43
One 2.0 hours computer laboratory every two weeks on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| 4 Online quizzes (Each quiz equivalent 250 words) | There are 4 short quizzes throughout the semester. Students can attempt each quiz a maximum of three times and the best mark for that quiz taken. Randomly assigned questions for each quiz instance. | 10 | 01, 02, 03 |
| Assignment (Equivalent to 1000 words) | 1 assignment, submitted online. Assignment will involve significant use of real-life data. | 10 | 01, 02, 03, 04 |
| Written project (Equivalent to 2500 words) | The project will require significant use of R programming skills and may require novel approach to problem-solving. | 25 | 01, 02, 03, 04, 05 |
| 2 Hour Exam | (1 hour theory and 1 hour practical). The practical part will require students to solve exam questions using R. | 55 | 01, 02, 03, 04, 05 |