We present an application of interactive dimensionality reduction (DR) for exploratory analysis of gene expression data that produces two lively updated projections, a sample map and a gene map, by rendering intermediate results of a t-SNE. The user can condition the projections "on the fly" by subsets of genes or samples, so updated views reveal coexpression patterns for different cancer types or gene groups. * This work is part of Grant PID2020-115401GB-I00 funded by MCIN/AEI/ 10.13039/501100011033. The results shown here are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
Biomedical data recorded as a result of clinical practice are often multi-domain -involving lab measurements, medication, patient attributes, logistic information-, and also highly unstructured, with high rates of missing data and asynchronously sampled measurements. In this scenario, we need tools capable of providing a broad picture prior to more detailed analyses. We present here a visual analytics approach that uses the morphing projections technique to combine the visualization of a t-SNE projection of clinical time series, with views of other clinical or patient's information. The proposed approach is demonstrated on an application case study of COVID-19 clinical information taken during the first wave.
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