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Table 2 Reproducibility and concordance with alternative cell type quantification methods

From: Gene expression markers of Tumor Infiltrating Leukocytes

Cell type Correlation with IHC Root Mean Squared Error from IHC Correlation with flow Root Mean Squared Error from Flow Mean pairwise similarity statistic in TCGA SD due to technical noise (log2 scale) Proportion of variance due to noise
B-cells    0.62 0.064 0.59 0.13 0.0022
CD45      cNA 0.1249 0.0024
Cytotoxic cells      0.69 0.0813 0.001
DC      0.46 0.2307 0.0151
Exhausted CD8      0.44 0.1624 0.0062
Macrophages      0.71 0.0828 0.0013
Mast cells      0.74 0.1949 0.0086
Neutrophils      0.48 0.19 0.0026
NK CD56dim cells    0.47 0.071 0.40 0.2347 0.1073
NK cells    0.51 0.118 0.47 0.1938 0.017
T-cells 0.66 1.3 a0.78 a0.064 0.81 0.1116 0.0021
Th1 cells      cNA 0.2212 0.0304
Treg      cNA 0.371 0.049
CD8 T cells 0.53 1.5 0.78 0.138 0.51 0.1842 0.0045
CD4 cells    b0.65 b0.752    
  1. aUsed to normalize the other cell types; 0.78 and 0.064 are the highest correlation and lowest RMSE observed between gene expression and flow for any T-cells vs. other cell type contrast
  2. bCalculated as the T-cell score minus the CD8 cell score
  3. cOnly one marker gene; quality impossible to assess in expression data alone
  4. Root mean squared errors are calculated from log2-scale abundance measurements. The mean pairwise similarity statistic measures how well a gene set’s co-expression pattern adheres to the co-expression pattern of ideal marker genes, with a value of 1 indicating perfect correlation with a slope of 1. The standard deviation (SD) and proportion of variance due to noise were calculated from triplicate gene expression assays from tumor sample RNA