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MSc Bioinformatics and Systems Biology at The University of Manchester
MSc Bioinformatics and Systems Biology
Our course is an integrated programme taught by researchers at the forefront of fields spanning bioinformatics, genomics and systems biology.

MSc Bioinformatics and Systems Biology

Year of entry: 2018

Course unit details:
Statistics & Experimental Design

Unit code BIOL65161
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by School of Biological Sciences
Available as a free choice unit? No

Overview

An essential part of training as a biologist is to learn to design experiments and analyse and interpret data correctly.  The unit introduces students to data handling, presentation and statistical data analysis, and in the light of the requirements for a good statistical analysis lays the foundations for good experimental design and critical analysis of research.

Aims

The unit aims to introduce students to the methods and tools used in statistical data analysis and the procedures and tools used in the design of experiments.

Learning outcomes

Students will learn to handle, present and analyse data, to design experiments and understand the limits of experimental evidence. More specifically students will learn to:

  • Test hypotheses and assess the statistical significance of results.
  • Handle data, and the best use of graphics and descriptive statistics.
  • Analyse biological data using classical statistical tests.
  • Analyse data using R.
  • Design successful experiments.
  • Critically assess basic experimental designs and analyses in the literature.

Syllabus

Statistical Data Analysis:

Data and graphics using R

  • Overview of statistics and hypothesis testing, types of data and graphs, descriptive statistics, basics of R statistical programming language.

Probability and statistics

  • Probability distributions, Confidence intervals, Bootstraps, Hypothesis testing using a one-sample distribution.

Classical statistical tests

  • Parametric and non-parametric tests to compare variances, means, proportions and counts in contingency tables. Covariance, parametric and non-parametric correlation.

Regression

  • Estimating slope and intercept, statistical significance of regression, regression in R, model assumptions, model checking, and transformation.

Analysis of variance

  • One-way, two-way and factorial anova, model simplification, model assumptions and pseudoreplication.

Experimental design:

  • This unit provides students with a foundation in experimental design to ensure that they can design effective and efficient experiments. The topics covered include; standardization, sample size, hypothesis testing, experimental units, controls, replication, randomization, independence, pseudoreplication, covariates and power analysis as applied to basic statistical tests.

Teaching and learning methods

Delivery and assessment will be through lectures, workshops, group discussions and e-learning. Students will participate in computer practical sessions, and submit a plan for the experimental design and analysis of their current research project.

Employability skills

Analytical skills
Students learn how to test hypotheses, analyse data and its significance using various statistical tests on various programmes and how to present data in terms of graphics.
Project management
Students will design successful experiments and manage the processes involved, taking into account the time needed for rigorous data analysis.
Problem solving
Students will understand the limits of experimental design and ways to overcome these problems.
Research
Students will critically assess basic experimental designs and analyses in the literature.

Assessment methods

Assessment tasks:

Online multiple choice exam: 2 hours in length - 50% weighting within the unit

Online Statistics Assessments 1 to 5: 5 x 2 hours in length - 25% weighting within the unit

Critical assessment of literature: 25% weighting within the unit

Statistics practical exercises (compulsory): 5 x 3 hours in length - ( - 5%* )

Project review assignment (compulsory): 1 week in length - ( - 5%* )

(*5% will be subtracted from the final unit mark for each incomplete exercise or assignment)

The overall pass mark for BIOL65161 is 65%. Should you fail to achieve this mark, you will be asked to (re-)submit any missing or failed elements and will have to achieve a mark of at least 65% in the referral exam (online multiple choice format).

Feedback methods

On-the-spot online feedback will be provided on 5 weekly statistics assessed assignments and a formative test. Students are also provided with oral feedback and individual help in understanding course content and in applying what is learnt to the students’ own projects during practicals and discussion groups.

Recommended reading

   The following book is compulsory and is provided to all students on the course:

 

   Crawley, M.J, Statistics: An introduction using R. (Wiley, 2005).

Study hours

Independent study hours
Independent study 0

Teaching staff

Staff member Role
Christopher Knight Unit coordinator

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