Kharbanda, M. | Search bioRxiv

bioRxiv2022

vissE: A versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and often leads to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis method that summarises redundancies into biological themes and provides various analytical modules to characterise and visualise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissEs versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, removing investigator bias from molecular discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/483195v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1c384b0org.highwire.dtl.DTLVardef@13c6bd3org.highwire.dtl.DTLVardef@1cb1d91org.highwire.dtl.DTLVardef@24cbf_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

CLARA: A web portal for interactive exploration of the cardiovascular cellular landscape in health and disease

Mammalian cardiovascular tissues are comprised of complex and diverse collections of cells. Recent advances in single-cell profiling technologies have accelerated our understanding of tissue cellularity and the molecular networks that orchestrate cardiovascular development, maintain homeostasis, and are disrupted in pathological states. Despite the rapid development and application of these technologies, many cardiac single-cell functional genomics datasets remain inaccessible for most cardiovascular biologists. Access to custom visual representations of the data, including querying changes in cellular phenotypes and interactions in diverse contexts, remains unavailable in publicly accessible data portals. Visualizing data is also challenging for scientists without expertise in processing single-cell genomic data. Here we present CLARA--CardiovascuLAR Atlas--a web portal facilitating exploration of the cardiovascular cellular landscape. Using mouse and human single-cell transcriptomic datasets, CLARA enables scientists unfamiliar with single-cell-omic data analysis approaches to examine gene expression patterns and the cell population dynamics of cardiac cells in a range of contexts. The web-application also enables investigation of intercellular interactions that form the cardiac cellular niche. CLARA is designed for ease-of-use and we anticipate that the portal will aid deeper exploration of cardiovascular cellular landscapes in the context of development, homeostasis and disease. CLARA is freely available at https://clara.baker.edu.au.

cell biology↗