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Assignment 2

BS6203

1. How do you think simultaneous contrast and metamerism can affect the effective representation of continuous variables using colour?

2. 

These are the theme colours you can find right in MS PowerPoint. Looking at both rows and columns, outline where the concepts of saturation, hues and lightness are (you can use the colour wheel on the left to help you.

3. Upload the three datasets attached with this assignment, into the “Microbiome submission” tab of the webtool accessible at https://integrative-microbiomics.ntu.edu.sg and click on “Next” followed by “Merge”. This will take you to “Cluster visualisation” tab. Click on “Similarity Plot”. You will see a heat-map appear, play around by changing different options such as colours and data-transformation. Relating the concepts learnt in the class, which options of the heatmap do you think represents the clusters in the data, best and worst? Discuss the advantages and disadvantages of heatmaps? Can you come-up with an alternative representation of this data?

4. This below figure represents a table showing 3 case scenarios on the columns (where balanced is an easy setup, and severely unbalanced is a challenging setup). Each column is evaluated on 4 metrics, precision, recall, f-score and AUC. You do not need to know what these are, but you only need to know only that the closer to 1, the better. The pre-process describes the use of none (not using any), scaling, quantile and z-score data transformation procedssures before using a batch effect removal algorithm such as Combat, Harman, SVA, Ratio-A and Ratio-G. The no batch and Batch rows refer to the situations where no batch effects exist, and where batch effects were introduced into the data.
Which retinal variables are used here? And how does one go about interpreting this graph?