BS231-5-SP-CO:
Computational Data Analysis: R for Life Sciences
PLEASE NOTE: This module is inactive. Visit the Module Directory to view modules and variants offered during the current academic year.
2023/24
Life Sciences (School of)
Colchester Campus
Spring
Undergraduate: Level 5
Inactive
Monday 15 January 2024
Friday 22 March 2024
15
17 March 2021
Requisites for this module
(none)
(none)
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The amount of data generated by biological experiments is increasing exponentially, mainly due to the development of new powerful technologies for the acquisition of large-scale genetic and genomic data sets. If we would compile the DNA sequence of the human genome into a book, it would be a 200,000 pages book that will take 10 years to read.
Bioinformatics became a compulsory skill for next generation biologists. In recent years, R became the programming language of choice for bioinformatics and biologists in academia and industry are currently using many tools that were developed in R. Computational Data Analysis: R for Life Sciences provides a basic introduction to programming for biologists in R and aims to provide students with the necessary programming skills and hand-on experience in performing data analysis with R. This module would be essential for further bioinformatics courses that students would take in their third year.
1. Use the command line for basic operations and to connect to remote servers.
2. Use R in the command line and in R studio, obtain help for functions
3. Understand the role of variables and how to use them and being able to use the appropriate data structure for the data (vectors, matrices, strings, lists and factors)
4. Understanding the role of objects and the environment.
5. Writing functions and understanding when it is needed to write a function
6. Understanding the role of scripts and writing scripts for any analysis
7. Reading and writing data from files stored on the computer
8. Being able to use conditionals and Boolean logic in R
9. Being able to write loops and understanding when to write loops in R
10. Representing data in plots and storing the plots into different file formats
11. Writing documentation with integrated R code
12. Comment the code, strategies to structure the code and debugging
13. Perform correlation and descriptive statistics and interpret the results.
14. Perform statistical tests and interpret the results.
15. Understanding which statistical test is best suited for different questions.
In order to pass this module the student will need to be able to:
1. write scripts and functions in R and comment the code;
2. read and write data files in different formats;
3. use the basic plot functionalities of R;
4. write documentation and examples of how your functions and scripts should be used;
5. perform basic statistical analysis in R (correlation analysis and statistical tests);
6. demonstrate the ability to work as part of a team.
The amount of data generated by biological experiments is increasing exponentially, mainly due to the development of new powerful technologies for the acquisition of large-scale genetic and genomic data sets. If we would compile the DNA sequence of the human genome into a book, it would be a 200,000 pages book that will take 10 years to read. Bioinformatics became a compulsory skill for next generation biologists. In recent years, R became the programming language of choice for bioinformatics and biologists in academia and industry are currently using many tools that were developed in R. Computational Data Analysis: R for Life Sciences provides a basic introduction to programming for biologists in R and aims to provide students with the necessary programming skills and hand-on experience in performing data analysis with R. This module would be essential for further bioinformatics courses that students would take in their third year.
Lectures - 12h
Workshops - 12 x 2 = 24 h
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
Additional coursework information
assessment one 30% (Moodle quiz). Assessment two 65% (code 35%, presentation 30% of total module). Attendance 5%
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Robert Ferguson, email: rmwfer@essex.ac.uk.
Dr Dave Clark, Dr Ben Skinner
School Undergraduate Office, email: bsugoffice (Non essex users should add @essex.ac.uk to create the full email address)
No
No
No
Dr Thomas Clarke
University of East Anglia
Senior lecturer/associate professor
Available via Moodle
Of 45 hours, 45 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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