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CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data

Fig 5

Deconvolution of mixed RNA from cultured cell lines.

We ran CDSeq with six cell types, α = 5, β = 0.5, and 700 MCMC runs. (A) Difference (“residual”) between estimated and true cell-type proportion plotted against true proportion for CDSeq (green) and CIBERSORT (red). Each plotted point represents the value for a single sample. (B) Radar plot of RMSE for estimates of sample-specific cell-type proportions. CDSeq (green); CIBERSORT (red). Total RMSE summing over cell types is 17% smaller for CDseq compared to CIBERSORT. (C) Difference (“residual”) between estimated and true log2 gene expression level (log2(RPMK)) plotted against true log2 gene expression level for CDseq (green) and csSAM (red). Each plotted point displays the expression value of a single gene, 19653 genes in total. (D) Radar plot of RMSE for gene expression levels. CDSeq (green); csSAM (red). Total RMSE of gene expression (summing over cell types) is 16% smaller for CDseq compared to csSAM.

Fig 5

doi: https://doi.org/10.1371/journal.pcbi.1007510.g005