< Back to Article
Deconvolving cell-type-specific gene expression profiles from bulk RNA-seq samples
Fig 3
BLUE captures distinct gene expression patterns in real bulk RNA-seq datasets and outperforms existing deconvolution methods.
a, Gene expression values of healthy and T2D conditions (in 
space). Each column is a cell-type-specific GEP averaged over cells of the same cell type in all samples under one condition (healthy or T2D). Each row is a gene that Segerstolpe et al. selected to be differentially expressed between two conditions. b, Scatter plot of the gene expression difference. The x-axis is ground truth differences calculated by subtracting the average expression profile under T2D condition from the average expression profile under the healthy condition. The y-axis is the predicted differences calculated in the same way as x-axis. The dashed line is the linear fitting between y and x, with a linear fitting score R2 = 0.52 and pearson correlation r = 0.72. c, Scatter plot of predicted proportions for beta cell vs. hemoglobin A1c (HbA1c) level. From the left subfigure to the right are predictions from CIBERSORTx, MuSiC, BLUE. Red lines are linear regression results. Linear fitting score R2 and pearson correlation r are shown in each plot. Blue dots are normal HbA1c level (under 6.5) while yellow triangles are HbA1c level that corresponds to T2D condition.
doi: https://doi.org/10.1371/journal.pcbi.1014101.g003