Training activity information
Details
Describe the differences between two genomics datasets by applying statistical methods including several of the following:
- Pearson, Spearman or distance correlation coefficients
- Significance by t test or Chi squared test
- Linear regression analysis
- Confidence intervals
- p-values and effect sizes
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
- Statistical approaches for assessing differences between datasets
- Distribution
- Correlation
- Sample size
- Power
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 is your understanding of the listed statistical methods? How will you decide which methods are appropriate for comparing two genomics datasets?
- How will you improve your ability to select and apply a range of statistical methods to compare genomics datasets? How will you learn to interpret the results of these tests in the context of the data? What is your current confidence in applying these statistical techniques?
- Will you review the principles and assumptions of each listed statistical method? Will you practice applying these methods using Python or R with sample datasets? Have you discussed potential datasets and comparison questions with your training officer? What challenges might you face in choosing the most appropriate statistical tests or interpreting the results? How do you feel about applying these statistical concepts to real-world data?
In action
- Which statistical methods from the list are you selecting to compare the two datasets and why are these appropriate? How are you applying these methods using programming tools? How are you interpreting the results (correlation coefficients, p-values, effect sizes, etc.) in the context of the genomics data?
- Are you able to successfully apply the chosen statistical tests? Are the results providing insights into the differences between the datasets? Are you documenting your analytical steps and the interpretation of the findings?
- If some of the chosen methods are not providing meaningful results, are you considering alternative methods from the list? Are you visualising the datasets to help understand the differences?
On action
- Describe the two genomics datasets you compared. What statistical methods did you apply? What were the key differences you identified?
- How did you choose which statistical methods to apply to compare the datasets? What did you learn about the application and interpretation of correlation coefficients, t-tests, Chi-squared tests, linear regression, confidence intervals, p-values, and effect sizes in the context of genomics data? Were there any unexpected findings or challenges in applying these methods? What did you learn from them? How did your understanding of statistical principles (‘reflect-in-action’) guide your analysis? How does the ability to compare genomics datasets using statistical methods contribute to your role in clinical decision-making?
- What statistical methods for comparing datasets do you want to explore further? How can you apply these comparative analysis skills to different types of genomics data? What specific actions will you take to enhance your skills in statistical comparison of datasets? What resources or support would be beneficial for further developing your statistical analysis skills?
Beyond action
- Have you had to compare other genomics datasets using statistical methods since this training activity? Which methods did you choose and why?
- How has your understanding of the nuances and appropriate application of these statistical tests evolved since completing this activity?
- How will your proficiency in applying these statistical methods contribute to your ability to draw meaningful conclusions from genomic data in future research or clinical analysis?
Relevant learning outcomes
| # | Outcome |
|---|---|
| # 3 |
Outcome
Apply statistical methods to derive meaningful conclusions from data to support clinical decision making. |