5 most important considerations for biological data analysis
Ignoring these fundamentals could lead to wrong interpretation of data that complicates your machine learning models
It’s a great sense of achievement to know that you have finished analysing your dataset. However, it is almost always a good habit to check through your data analysis and at times, even re-analyse the data in a different way to understand the data better and make the data analysis more outstanding. In this blog entry, I will highlight some key considerations that could be taken into account when checking through your data analysis.
- Could your variables be categorised differently, or expressed as continuous variables for data analysis?
In some instances, the explanatory variable can be expressed as a categorical variable or a continuous variable. If that is indeed the case, I would recommend analysing the data both ways. For example, consider a research question that studies the effect of age on vaccine antibody response. Based on literature, you may have classified subjects into two groups: (I) elderly subjects as 65 years of age and above; (II) young subjects as lower than 65 years of age. The antibody responses are then compared in these 2 groups.
