BS231-5-AU-CO:
Computational Data Analysis: R for Life Sciences
2023/24
Life Sciences (School of)
Colchester Campus
Autumn
Undergraduate: Level 5
Current
Thursday 05 October 2023
Friday 15 December 2023
15
15 February 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
BSC C700 Biochemistry,
BSC C701 Biochemistry (Including Placement Year),
BSC C703 Biochemistry (Including Year Abroad),
BSC CR00 Biochemistry (Including Foundation Year),
BSC C400 Genetics,
BSC C402 Genetics (Including Year Abroad),
BSC C403 Genetics (Including Placement Year),
BSC CK00 Genetics (Including Foundation Year),
MSCIC098 Biochemistry and Biotechnology (Including Year Abroad),
MSCIC099 Biochemistry and Biotechnology (Including Placement Year),
MSCICZ99 Biochemistry and Biotechnology
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
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 code and strategies to structure code clearly
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. Effectively communicate analyses by writing scripts and functions in R and commenting the code;
2. Attain knowledge of the key methods for reading and writing data files in different formats into R;
3. Using and critically evaluating the key plotting functionalities of R;
4. Apply principles from software development to document and demonstrate how your functions and scripts should be used;
5. Understand and apply functions to perform basic statistical analyses in R (correlation analysis and statistical tests);
6. Demonstrate essential transferable skills and qualities needed to work successfully 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 |
| Coursework |
Assessment 1 - Worksheet |
|
70% |
| Coursework |
Assessment 2 - Group Project |
|
25% |
| Practical |
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 David Clark, email: david.clark@essex.ac.uk.
Dr Dave Clark, Dr Ben Skinner, Dr Martin Wilkes
School Undergraduate Office, email: bsugoffice (Non essex users should add @essex.ac.uk to create the full email address)
Yes
No
No
Dr Thomas Clarke
University of East Anglia
Senior lecturer/associate professor
Available via Moodle
Of 93 hours, 84 (90.3%) hours available to students:
3 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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