Topic: spatial context

Explore gene expression while preserving the spatial context lost in bulk and conventional single-cell sequencing. This workshop compares imaging-based and sequencing-based spatial transcriptomics and works through public human and mouse datasets. Hands-on analysis covers loading, quality control, segmentation, integration, cell typing and identification of cellular neighborhoods and niches. Intended for biomedical and life-science graduates or postgraduates with some R experience; demonstrations use R and Python code is also supplied. Familiarity with RNA sequencing is helpful. Sessions on 1

Spatial context plays an important role in visual processing and perception. A rich set of surround effects that involve nonlinear interactions between the center and surround have been found in Primary Visual Cortex (V1). A general summary is that surround suppression is prevalent and the amount of suppression depends on the visual similarity between the center and surround. In recent years, convolutional neural networks (CNNs) have successfully modeled neural behavior across the visual cortices. However, there have been limited studies addressing the multitude of contextual effects that have