Ziyi Li's research while affiliated with University of Texas MD Anderson Cancer Center and other places
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Publications (2)
Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel...
The bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell-type-specific inferences from bulk data. Our real data exploration suggests that the differential expression or methylation status are often correlated among cell types. Based on this observation, we develop...
Citations
... One future direction is to exploit the relations among the cell types, which may better capture the underlying phenotypic states of the subjects. A more recently developed method called CeDAR [51] uses known cell-type hierarchy as prior to infer CTS expression in bulk data as opposed to our de novo sub-cell-type inference and will leave a more detailed comparison as future work. Moreover, we can also extend GTM-decon to modeling multi-omic single-cell data to identify multi-omic CTS topic distributions and then use them to deconvolve multi-omic bulk data. ...
... Several computational methods were recently developed to take sample mixture proportions into modeling, to identify cell type-specific DEG (csDEG). Relevant methods include CARseq [20], TOAST [21], CeDAR [22], CellDMC [23], LRCDE [24], TCA [25], csSAM [26], HIRE [27] and DESeq2 [28]. A schematic overview is shown in Figure 1. ...
