Deconvolving cell-type-specific gene expression profiles from bulk RNA-seq samples
Fig 4
An integrative framework for patient subtyping based on the output from BLUE.
BLUE was trained on one independent AML scRNA-seq reference dataset from [19] and applied to deconvolve TCGA-AML dataset into cell-type proportions and sample-specific, cell-type-specific GEPs. The top branch defined patient groups based on predicted cell-type proportions, similar to the analysis workflow in one previous study [22]. GMP patient group exhibits the most favorable prognosis outcome compared to the other three patient groups in the TCGA-AML cohort. The survival pattern failed to be validated in the TARGET-AML cohort. The bottom branch defines TCGA patient groups based on the predicted cell-type-specific GEPs and the resulting three groups exhibited distinct survival differences. The same survival pattern was also observed in TARGET-AML patient cohort, after applying a simple classification pipeline on selected gene expressions to classify TARGET-AML patients.