%0 Journal Article %1 ghazanfar2020investigating %A Ghazanfar, Shila %A Lin, Yingxin %A Su, Xianbin %A Lin, David Ming %A Patrick, Ellis %A Han, Ze-Guang %A Marioni, John C. %A Yang, Jean Yee Hwa %D 2020 %J Nature Methods %K cell correlation decision disruption expression fate gene genomics higher image linage order pseudotime single spatial transcriptomics %R 10.1038/s41592-020-0885-x %T Investigating higher-order interactions in single-cell data with scHOT %U https://doi.org/10.1038/s41592-020-0885-x %X Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered ‘pseudotime’ offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT—single-cell higher-order testing—which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates various higher-order measurements including variability or correlation. We demonstrate the use of scHOT by studying coordinated changes in higher-order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene–gene correlations across space using spatially resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first-order differential expression testing and provides a framework for interrogating higher-order interactions using single-cell data.

@article{ghazanfar2020investigating, abstract = {Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered ‘pseudotime’ offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT—single-cell higher-order testing—which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates various higher-order measurements including variability or correlation. We demonstrate the use of scHOT by studying coordinated changes in higher-order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene–gene correlations across space using spatially resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first-order differential expression testing and provides a framework for interrogating higher-order interactions using single-cell data.}, added-at = {2020-07-15T22:10:49.000+0200}, author = {Ghazanfar, Shila and Lin, Yingxin and Su, Xianbin and Lin, David Ming and Patrick, Ellis and Han, Ze-Guang and Marioni, John C. and Yang, Jean Yee Hwa}, biburl = {https://www.bibsonomy.org/bibtex/2ef83a151b16d8662a60ea22aa742b5f1/becker}, description = {Investigating higher-order interactions in single-cell data with scHOT | Nature Methods}, doi = {10.1038/s41592-020-0885-x}, interhash = {741f0defbf4e4d599e07910ee19d633d}, intrahash = {ef83a151b16d8662a60ea22aa742b5f1}, issn = {15487105}, journal = {Nature Methods}, keywords = {cell correlation decision disruption expression fate gene genomics higher image linage order pseudotime single spatial transcriptomics}, refid = {Ghazanfar2020}, timestamp = {2020-07-15T22:10:49.000+0200}, title = {Investigating higher-order interactions in single-cell data with scHOT}, url = {https://doi.org/10.1038/s41592-020-0885-x}, year = 2020 }