Hi everyone , I will have to work on an exam and the topic is to build a Visual Analytic Application about any dataset we want. Im new in this field (BioInformatic) but if I have to spend a month in deep programming I wish I could help this community somehow. Im doing Thesis on the role of platelets in cancer and so I know how to obtain differentially expressed genes and everything related to it. Now the project have some rules of which :
Every assignment MUST have a visual part constituted by at least 2 Visualizations coordinated in both ways and interactive.
ANALYTICS: every assignment MUST contain at least 1 computation that is triggered by user visual interactions
Mandatory: every assignment MUST use a PCA or a MDS or a t-SNE algorithm.
Now I though about using a count matrix with sample informations. My idea was to project a PCA analysis over the samples and DE genes, from which you can select directly on the plot the samples you like to study more triggering a second visualization (Heatmap) that would change based on the selection. Furthermore I would additionally add a filter over the genes based on FDR and logFC values previously computed using one of the packages between edgeR and Deseq2. Maybe adding some scatterplot or volcano plot to give even an easier selection of genes. Still have to think about an Analytic to add.
Please if you find that PCA is not applicable or stupid dont mind to tell me, but would be even better if you can explain me what would be better in this case. I'm new in this ( I come from Software Engineering ) but I really wanna help this community (I know sounds dumb but I kinda feel more purpose working in this field) so please feel free to propose what so ever and to critic my choices.