Biological data normalization using R programming
Due to the ongoing increase in advanced technologies, a massive volume of data has been created in all fields of science and there is an urgent need to analyze such huge data. Preprocessing of the data is essential before analyzing any data, and data normalization is a part of data preprocessing. The measurement unit used can affect the data analysis. For instance, changing measurement units from a scale into a different scale may lead to very different results (meters to centimeters for height, kilograms to grams for weight). The smaller scale may affect more on results or we can say the attribute has greater effect or weight. In data preprocessing, we sometimes use the term standardization instead of normalization. We held a webinar on December 21th, 2017 related to data normalization. In this webinar, we first focused on the necessity of normalization and different types of normalization algorithms. Finally, using R programming different types of normalization was implemented on a microarray dataset and the difference between normalized and unnormalized data was shown through different plots. The teaching material is available through these links; Slides and R codes.