Training activity information
Details
Visualise the variation for multiple aspects of a genomics dataset programmatically using multiple plots
Type
Developmental training activity (DTA)
Evidence requirements
Evidence the activity has been undertaken by the trainee​.
Reflection on the activity at one or more time points after the event including learning from the activity and/or areas of the trainees practice for development.
An action plan to implement learning and/or to address skills or knowledge gaps identified.
Considerations
- For example:
- Mean
- Median
- Quartiles
- Determine if the data are normally distributed
- Cumulative distribution analysis
- Standard deviation
- Identify outliers
- Correlation
Reflective practice guidance
The guidance below is provided to support reflection at different time points, providing you with questions to aid you to reflect for this training activity. They are provided for guidance and should not be considered as a mandatory checklist. Trainees should not be expected to provide answers to each of the guidance questions listed.
Before action
- What principles of effective data visualisation are important? What programming skills in Python (matplotlib) or R (ggplot) are necessary?
- How will you develop your skills in creating informative visualisations of genomic data using programming tools? How will you learn to choose appropriate plot types to represent different aspects of variation? What is your current experience with data visualisation in a genomics context?
- Will you review different types of plots and their applications in genomics? Will you practice using plotting libraries in Python or R with sample data? Have you discussed the specific aspects of variation to visualise with your training officer? What challenges might you face in selecting and generating effective plots? How do you feel about presenting data visually?
In action
- What programming language and plotting libraries are you using (e.g., ggplot in R, matplotlib/seaborn in Python)? Which types of plots are you creating and why are these appropriate for visualising the different aspects of the data? How are you structuring your code to generate multiple plots?
- Are you able to generate the desired plots without errors? Are the plots effectively communicating the variation in the data? Are you adjusting plot parameters (e.g., colours, labels) for clarity?
- If certain plot types are not revealing the data patterns effectively, are you experimenting with alternative visualisations? Are you referring to plotting library documentation for different options?
On action
- Describe the genomics dataset you visualised. What aspects of the data did you choose to visualise and what types of plots did you use?
- What programming skills did you use or develop for data visualisation? How effective were the different types of plots in revealing variation in the data? What did you learn about choosing appropriate visualisations? Did you encounter any challenges in creating effective visualisations? What did you learn from these? How did your insights into the data (‘reflect-in-action’) guide your choice of visualisations? How does the ability to visualise genomic data programmatically relate to communicating findings and supporting clinical decision-making?
- What data visualisation techniques or libraries do you want to explore further? How can you apply your visualisation skills to effectively communicate complex genomic information? What specific actions will you take to improve your data visualisation abilities? What resources or support would be beneficial for further developing your data visualisation skills?
Beyond action
- Have you presented genomic data using multiple plots since this training activity? How effective do you feel your visualisations were in conveying the information?
- Have you explored other plotting libraries or techniques beyond those you used in this training activity? How has your understanding of data visualisation improved?
- How will your ability to create informative visualisations contribute to your communication of complex genomic data to various stakeholders in the future?
Relevant learning outcomes
| # | Outcome |
|---|---|
| # 3 |
Outcome
Apply statistical methods to derive meaningful conclusions from data to support clinical decision making. |
| # 4 |
Outcome
Summarise results of data analysis to stakeholders. |