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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 using "R". 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.
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
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.
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

None

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 using "R". All methods will be demonstrated in "R", 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
Teaching25
Independent Study175
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 four parts to the assessment: one will be a class based paper test of basic skills, one will be based upon a practical data analysis challenge; one will be based upon critical assessment of the methods used in the data analysis challenge and the final will be a critical appraisal of a paper. The markers/reviewers will normally be taken from teachers on the module. Written assignments will be double marked. The standard is HE7 level. Assessed Course Work: 1. (10%) 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. 2. (30%) 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 method to achieve a thorough analysis of the dataset provided to include: 1. A fully annotated R script that executes all analysis commands and runs without error (0.5 of this mark section). 2. A set of professionally produced figures with appropriate captions that summarize the analysis (0.5 of this mark section). (summative) 3. (30%) Description of Methods. A written summary of data visualisation techniques and their applications in cell and molecular biology, including sections on dimensionality reduction and clustering as a minimum, using the references provided in lectures and on Blackboard to start. Students are encouraged to explore a range of different methods, based upon their own reading. 1500 word limit. 4. (30%) Critical review of a published paper that should include a discussion of the methods developed, the extent to which the computational methods used provided biological insight and how these methods have developed since publication. 1500 word limit Assessment requirements You must pass the module with an average overall mark of 50% or above. There is compensation between assessment elements provided a mark of 40% or higher is attained in each element. 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 30%
Written critical appraisal  (1500 words) 30%
Written summary  (1500 words) 30%

Referral

MethodPercentage contribution
Class Test 10%
Data Analysis 30%
Written critical appraisal  (1500 words) 30%
Written summary  (1500 words) 30%

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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