Investigating higher-order interactions in single-cell data with scHOT.
Shila GhazanfarYingxin LinXian-Bin SuDavid Ming LinEllis PatrickZe-Guang HanJohn C MarioniJean Yee Hwa YangPublished in: Nature methods (2020)
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.
Keyphrases
- single cell
- rna seq
- genome wide
- induced apoptosis
- high throughput
- genome wide identification
- electronic health record
- cell cycle arrest
- cell fate
- big data
- copy number
- dna methylation
- endoplasmic reticulum stress
- cell death
- oxidative stress
- machine learning
- bioinformatics analysis
- climate change
- cell proliferation
- deep learning
- decision making