RT Journal
A1 Schrod, Stefan
A1 Lück, Niklas
A1 Lohmayer, Robert
A1 Solbrig, Stefan
A1 Völkl, Dennis
A1 Wipfler, Tina
A1 Shutta, Katherine H.
A1 Ben Guebila, Marouen
A1 Schäfer, Andreas
A1 Beißbarth, Tim
A1 Zacharias, Helena U.
A1 Oefner, Peter J.
A1 Quackenbush, John
A1 Altenbuchinger, Michael
T1 Spatial Cellular Networks from omics data with SpaCeNet
JF Genome Research 
JO Genome Research 
YR 2024 
FD September 01 
VO 34 
IS 9 
SP 1371 
OP 1383 
DO 10.1101/gr.279125.124 
UL http://genome.cshlp.org/content/34/9/1371.abstract 
AB Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells, together with the cells' spatial distribution, provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. “SpaCeNet” is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence (CI) relations between captured variables within individual cells and by disentangling these from CI relations between variables of different cells.