One of the major goals in gene expression data analysis is to explore and discover groups of genes and groups of biological conditions with meaningful relationships. While this problem can be addressed by algorithms, their results require an analysis within context, since they may be affected by many side processes -such as tissue differentiation- that could hinder the target goal. Visual analytics-based methods for exploratory analysis of the gene expression matrix (GEM) are essential in biomedical research since they allow us to frame the analysis within the user's knowledge domain. In this paper, we present a visual analytics approach to discover relevant connections between genes and samples based on linking a reordered GEM heatmap and dual 2D projections of its rows and columns, which can be recomputed conditioned by subsets of genes and/or samples selected by the user during the analysis. We demonstrate the capability of our approach to discover relevant knowledge in three case studies involving two cancer types plus normal tissue from the TCGA database.

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.

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.

MOTIVATION: Biomedical research entails analyzing high dimensional records of biomedical features with hundreds or thousands of samples each. This often involves using also complementary clinical metadata, as well as a broad user domain knowledge. Common data analytics software makes use of machine learning algorithms or data visualization tools. However, they are frequently one-way analyses, providing little room for the user to reconfigure the steps in light of the observed results. In other cases, reconfigurations involve large latencies, requiring a retraining of algorithms or a large pipeline of actions. The complex and multiway nature of the problem, nonetheless, suggests that user interaction feedback is a key element to boost the cognitive process of analysis, and must be both broad and fluid. RESULTS: In this article, we present a technique for biomedical data analytics, based on blending meaningful views in an efficient manner, allowing to provide a natural smooth way to transition among different but complementary representations of data and knowledge. Our hypothesis is that the confluence of diverse complementary information from different domains on a highly interactive interface allows the user to discover relevant relationships or generate new hypotheses to be investigated by other means. We illustrate the potential of this approach with three case studies involving gene expression data and clinical metadata, as representative examples of high dimensional, multidomain, biomedical data. AVAILABILITY AND IMPLEMENTATION: Code and demo app to reproduce the results available at https://gitlab.com/idiazblanco/morphing-projections-demo-and-dataset-preparation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.