The University of Southampton
Courses

MEDI6227 Quantitative Cell Biology

Module Overview

It is important that we provide bioinformatic cell analysis training to students in order to significantly improve research possibilities in their future careers in Biomedical Sciences. The quantitative module in cell biology will focus on the practical use of the methods employed, rather than the mathematics underpinning them. Some of the mathematics will be discussed, but no prior knowledge will be assumed. The analyses will predominantly be conducted either via the graphical user interface in MATLAB using the bioinformatics, statistics and machine learning toolboxes or using basic commands, which will be provided. Southampton has a site licence for this software, as will most Universities. Southampton students can download and install the software directly onto their home machines via www.software.soton.ac.uk. Students with or without experience of programming/mathematics will be enrolled on this course. Students with no background in this area will not be disadvantaged as they will be provided with computing support to succeed. There is no opportunity to repeat the year on this programme

Aims and Objectives

Module Aims

• To provide you with an understanding of high-throughput “omics” technologies and the data they produce. • To develop your ability to analyse complex biomedical datasets. • To develop your skills in critical appraisal of previously published research. • To develop your ability to solve complex research problems.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Extract and process data from a range of high-throughput experimental sources
  • Use basic supervised and unsupervised methods to analyse multivariate biomedical data sets
  • Synthesize the results of different methods of analysis and draw appropriate biological conclusions.
Cognitive Skills

Having successfully completed this module you will be able to:

  • Understand and summarize different methods of data analysis and critically appraise their appropriate use.
  • Apply information identified from published sources to your own investigations.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Organise your own activities to achieve a desired outcome within a limited amount of time.
  • Direct your own learning.
  • Exercise initiative and personal responsibility
Learning Outcomes

Having successfully completed this module you will be able to:

  • Produce concise written summaries of your analysis, including interpretation of statistical results in terms of underlying biology.

Syllabus

a) Multivariate basis of complex disease: introduction to high-throughput “omics” technologies (focussing primarily on RNA-seq, DNA microarrays and proteomics) and the data they generate. b) Extracting and normalizing and basic statistical analysis of RNA-seq and proteomics data. c) Unsupervised analysis of data: clustering and dimensionality reduction. d) Supervised learning and classification. e) Deriving networks, pathways and models from large datasets.

Special Features

The assessment will be structured as an analysis in a short research project

Learning and Teaching

Teaching and learning methods

Teaching will consist of five one-day master classes. Each day will cover one of the 5 syllabus sections. Each session will begin with a taught overview of the material to be covered in the morning, followed by a hands-on session on computer in the afternoon in which the students can explore the various different data types and methods discussed. Example datasets for exploration will be provided at each session. Collaborative working between students will be encouraged during these sessions. The afternoon sessions will be run with a member of academic staff and 1-2 computational PhD/postdoc demonstrators, of which there are many suitable in Southampton (to be paid at standard demonstration rates). The training and analysis will primarily be conducted in MATLAB using the bioinformatics, statistics and machine learning toolboxes. Southampton has a site licence for this software, as will most Universities. Southampton students can download and install the software directly onto their home machines via www.software.soton.ac.uk. All methods will be demonstrated in MATLAB, and full code for example problems will be provided; prior knowledge of programming would be beneficial but not required. Total Study Time The module will reflect the normal distribution of 200 hours of student effort attributable to each 20 credit module. Contact hours: 25 Non-contact hours: 175

TypeHours
Independent Study175
Teaching25
Total study time200

Resources & Reading list

Bishop (2006). Pattern Recognition and Machine Learning. 

Hastie, Tibshirani, Friedman. (2009). The Elements of Statistical Learning. 

Assessment

Assessment Strategy

The assessment for the module provides students with the opportunity to demonstrate achievement of the learning outcomes. There will be three parts to the assessment: one will be based upon a practical data analysis challenge; one will be based upon critical assessment and the other will be a teamwork assessment based on monitoring the engagement of the students in the group. The markers/reviewers will be taken from teachers on the module. Written assignments will be double marked. The standard is HE7 level. Assessed Course Work: 1. A short paper-based class test, comprising approximately 10 questions, will be held after weekly session three to monitor and facilitate the acquisition of basic skills required for the primary substantive summative assessments (10%). 2. Solution to a challenge data analysis problem. Full problem details and data will be provided to the students at the start of the course via Blackboard. Submission will include annotated code. Students will be assessed by the success of their methods (45%). 3. A short written summary to describe and justify the approaches taken in the data analysis problem including the biological interpretation of the results (maximum 2500 words) (45%). Assessment requirements You must pass the module overall at 50% or above. There is no compensation between assessment elements, all three elements must be passed. Candidates who fail one or more elements of the module at the first attempt will be permitted to re-sit the failed elements as supplementary assessments. Candidates who achieve at least 50% overall at the second attempt will be permitted to pass the module with a capped mark of 50%

Summative

MethodPercentage contribution
Class Test 10%
Data Analysis 45%
Written summary  (2500 words) 45%

Referral

MethodPercentage contribution
Class Test 10%
Data Analysis 45%
Written summary  (2500 words) 45%

Costs

Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

All journal articles used will be accessible to students through the University of Southampton electronic journals collection. There are no other cost implications arising from this module

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.

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