Fig. 2: Aggregate cross-sample performance of participant and comparator deconvolution methods.
From: Community assessment of methods to deconvolve cellular composition from bulk gene expression
Aggregate score (primary metric: Pearson correlation; secondary metric: Spearman correlation) of participant (first submission only) and comparator methods in (A) coarse- and (B) fine-grained sub-Challenges over bootstraps (n = 1000; Methods). Comparator methods (bold) are shown only if their published reference signatures include all cell types in each respective sub-Challenge: CIBERSORTx (coarse-grained only) and xCell. Boxplots display median (center line), 25th and 75th percentiles (hinges), and 1.5x interquartile range (whiskers). Methods ordered by median Pearson correlation in respective sub-Challenge. DNN deep neural network, ENS ensemble, NMF non-negative matrix factorization, NNLS non-negative least squares, OTH other, PI probabilistic inference, REG other regression, SUM summary, SVR support vector regression, UNK unknown/unspecified, Frac unnormalized fractions that need not sum to one, Norm normalized scores (comparable across cell types and samples), Prop proportions that sum to one. Source data are provided as a Source Data file.
