Publications by authors named "Shiva Kazempour"
Single-cell RNA sequencing (scRNA-seq), a powerful technique for investigating the transcriptome of individual cells, enables the discovery of heterogeneous cell populations, rare cell types, and transcriptional dynamics in separate cells. Yet, scRNA-seq data analysis is limited by the problem of measurement dropouts, i.e.
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- Analyzing omics data types separately can limit the ability to find important variations that are consistent across different assays.
- Integrating multiple omics data types into one model can enhance statistical power, but it poses challenges due to the different levels at which these data are measured.
- The iNETgrate package was developed to integrate transcriptome and DNA methylation data into a single gene network, showing improved prognostic capabilities over standard clinical methods in five independent datasets.
Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.
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