A1392
Title: Transcriptomics from the perspective of spatial statistics: Challenges and methodological approaches
Authors: Jose Luis Romero Bejar - University of Granada (Spain) [presenting]
Juan Manuel Praena Fernandez - Universidad de Granada (Spain)
Francisco Javier Esquivel - University of Granada (Spain)
Abstract: With the advancement of new bioinformatics technologies, some research has focused on the analysis of the sequencing of genetic material within the framework of omics sciences. The analysis of differential gene expression using multivariate techniques applied to high-dimensional data analysis helps to identify significant differences between samples, highlighting genes that may play key roles in specific biological conditions or diseases. Furthermore, if spatial information about the data was also obtained, spatial statistics could be extremely useful. Spatial statistics involves analyzing spatial data to understand patterns, structures, and relationships within them. Investigating the spatial dependency structure between some specific genetic information in a sample of cells from the same tissue would allow identifying patterns of organization and relationships between cells that could be crucial for understanding complex biological processes, such as tissue development, disease progression, or response to treatments. Understanding how cells interact spatially within their environment can provide insights into how tissues function and how abnormalities, such as cancer and others, develop and spread. The focus is on the recent paradigm of spatial transcriptomics and the potential possibility of bringing together the well-founded field of spatial statistics.
