Leveraging heterogeneity across multiple datasets

Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

Supplementary Information

Supplementary Figure 1 MAD=0.07 p=0.16

Supplem Figure 1

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density

MAD=0.09 p=4.4e-02

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MAD=0.21 p=3.16e-08 2.71

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Median goodness of fit per platform immunoStates

LM22

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mentary Figure 1: Platform bias in cell mixture deconvolution. Density plots represen Platform in cell mixture deconvolution. Density plots representing the tion of median bias goodness of fit for each platform across all methods grouped by different distribution of median goodness of fit for each platform across all methods grouped ented by by fill color). Significance of platform is computed by ofestimating A different matrices (represented by fill bias color). Significance platform the biasMedian is e (MAD) of each distribution comparing to a null distribution that assumes computed by estimating the and Median Absolute itDistance (MAD) of each distribution and no t comparing it to a null distribution that assumes no technical variation between n between samples. samples. 

Supplementary Fig Supplementary Figure 2 Correlations of estimated cell proportions between scaled and non−scaled methods

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plementary Figure 2: Effect of rescaling expression data to deconvolution. Boxplot displayin l correlation between cell proportions estimated deconvolution with and without rescaling Effect of rescaling expression data to deconvolution. Boxplot displaying samplear Model = 0.989 +/0.002)cell or Robust Regression (r =deconvolution 0.843 +/- 0.034) multiple sa level(rcorrelation between proportions estimated with across and without s matrices. Center lines correspond median boxRegression and the lower and upper rescaling by either Linear Modelto(rthe = 0.989 +/- value 0.002)of oreach Robust (r = 0.843 +/- 0.034) across samples and basis matrices. Center lines correspond to the box correspond to theirmultiple first and the third quartiles, respectively. median value of each box and the lower and upper bounds of each box correspond to their first and the third quartiles, respectively.  

a

c Supplementary Figure 3

a

c

CD14+ monocyte

2

0

CD4+ alpha beta T cell CD8+ alpha beta T cell

−2

CD56dim natural killer cell MAST cell eosinophil

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CD8+ alpha beta T cell

−2

gamma delta T cell

CD56dim natural killer cell MAST cell

myeloid dendritic cell

eosinophil

plasma cell

basophil

plasmacytoid dendritic cell

immuno

CD4+ alpha beta T cell

CD56bright natural killer cell

memory B cell

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macrophage m1

neutrophil

naive B cell

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macrophage m0

basophil

hematopoietic progenitor

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CD14+ monocyte

macrophage m2

gamma delta T cell CD56bright natural killer cell

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macrophage m1 macrophage m2

LM22 204

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CD16+ monocyte macrophage m0

CD1E CD209 CD3G 60 CD40LG 204 431 CD8A CLIC2 12 19 44 CTLA4 FCN1 KLRF1242 MS4A1 TCL1A immunoStates TNFRSF10C

IRIS

CD1E CD209 CD3G CD40LG CD8A CLIC2 CTLA4 FCN1 KLRF1 MS4A1 TCL1A TNFRSF10C

b b

IRIS

neutrophil

−4

Supplementary Figure 3

hematopoietic progenitor naive B cell myeloid dendritic cell plasma cell memory B cell

plasmacytoid dendritic cell

Supp Figu

Creation of the immunoStates matrix. (a) Flow-chart describing the steps for the creation of the immunoStates expression matrix. (b) Heatmap showing expression of the immunoStates signature genes in target cell types. Genes expression values are displayed as z-scores per gene across all cell types. (c) Venn-diagram depicting the overlap between gene-sets between each basis matrix. Genes overlapping across all three matrices are listed on the left side of the diagram. 

Supplementary Figure 4 Supplementary Figure 4 Linear Model

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Robust Regression

Support Vector Regression

LM22 genes using immunoStates expression values

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GPL6480 GPL6102 GPL6947 GPL6104 GPL6244 GPL5175 GPL570 GPL571 GPL96




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% samples

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Supplementary Figure 4: Increasing the amount of data to estimate expression values for genes in a basis matrix does not improve deconvolution accuracy. We used the datasets used to create immunoStates to calculate expression values for each gene in and deconvolved the technical evaluation We Increasing the amount of data toLM22, estimate expression values bias for genes incohort. a basis found increasing the amount of data to estimate expression value of a gene in a basis matrix did not increase matrix does not improve deconvolution accuracy. We used the datasets used to accuracy.

create immunoStates to calculate expression values for each gene in LM22, and deconvolved the technical bias evaluation cohort. We found increasing the amount of data to estimate expression value of a gene in a basis matrix did not increase accuracy.  


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Programming Regression Regression Model Programming Regression Model diseased Programming Regression Regression Supplementary Figure 5: Goodness of fit Regression in healthy and samples. (a) Boxplots indicatin goodness of fit scores (y-axis) for blood-derived and tissue-derived samples in healthy donors (1383 samples Goodness of fit in healthy and diseased samples. (a) Boxplots indicating goodness across multiple for IRIS, LM22, and immunoStates. Center lines correspon Supplementary Figure deconvolution 5: Goodnessmethods of fit (x-axis) in healthy and diseased samples. (a) Boxplots indicati of to fit the scores (y-axis) for blood-derived and tissue-derived samples in healthy donors of each box and the lower upper boundssamples of each box correspond to their firstsample and th goodness of fitmedian scores value (y-axis) for blood-derived andand tissue-derived in healthy donors (1383 (1383 samples) across multiple deconvolution methods (x-axis) for IRIS, LM22, and third quartiles, respectively. (b) Same as in (a) in disease (2684 samples).Center lines correspo across multiple deconvolution methods (x-axis) forbut IRIS, LM22,samples and immunoStates. Linear Model

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immunoStates. Center lines correspond to the median value of each box and the to the lower medianand value of each box and lower upper bounds of each correspond their first and t upper bounds of the each boxand correspond to their firstbox and the third toquartiles, third quartiles, respectively. (b)as Same in (a) in disease samples (2684 samples). respectively. (b) Same in (a)asbut in but disease samples (2684 samples).  

Supplementary Figure 6 Supplementary Figure 6 Blood

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Supplementary Figure 6: Deconvolution concordance by matrix and method across blood and solid tissue. Boxplots representing the distribution of pairwise correlation coefficients between estimated proportions for all matrices and deconvolution methods. Comparisons were divided in (1) pairs with the samesolid Deconvolution concordance by matrix and method across blood and signature matrix but run with different methods, (2) pairs with different signature matrices but run using the tissue. Boxplots the distribution correlation coefficients same method, and (3)representing pairs where both matrix and method of werepairwise different. Significance analysis was between estimated proportions fortest.allResults matrices deconvolution methods. performed using the Wilcoxon’s paired rank sum are shown and for samples containing blood cells or Comparisons were divided in (1) pairs with the same signature matrix but run with solid tissue biopsy from Lukk et al 2010.

different methods, (2) pairs with different signature matrices but run using the same method, and (3) pairs where both matrix and method were different. Significance analysis was performed using the Wilcoxon’s paired rank sum test. Results are shown for samples containing blood cells or solid tissue biopsy from Lukk et al 2010. 

Supplementary Figure 7   LM22 ● ●

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● ● ● ●● ●● ● ● ● ● ●● ●● ●● ● ● ●●● ● ●● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●● ● ●● ●● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●

20

40

Estimated Cell Proportion (%)

●

●

● ●

● ●

●

● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●

20

60

10

40

● ●

CD14+ Monocyte

●

CD4+ T-Cell

20

30

CD8+ T-Cell

40

Gamma/Delta T-Cell ●

r = 0.86

20

●

memory B-Cell

● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●

Robust Regression

●

● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●● ● ●● ● ● ●●● ● ●● ●● ● ● ● ●● ●●● ● ●● ●●●● ●●●● ● ●● ● ●●● ● ●● ●● ● ●●● ● ● ● ● ●● ●●● ● ●● ●● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ●● ● ● ●

40 40

00

●

20 20

4040

naïve B-Cell NK

60


immunoStates ●

r = 0.69

● ●

●

● ● ●

● ●●

●

● ● ●

●●

● ● ● ●

●

● ● ●

● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●●● ● ● ●●●● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●

0

●

● ●● ●● ● ●●

0

20 20

40

0

●

●

●●


●

20

●

● ●

●

LM22 60


●

●


●

●● ●

● ● ● ●

●

●

immunoStates immunoStates

●●

00

●

30

● ●●

●

0

●

0

●

● ● ●

40

●● ● ●●● ● ●

●●

40

20

●

20

●

●

●

●

40 60

●

10

40

0

30

●


r = 0.20

● ● ● ● ●●●● ● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●

r = 0.79

60

LM22

r = 0.79 ●

IRIS Flow Cytometry Cell Frequency (%)

●

LM22

●

20 20

● ● ●

● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ●●● ●●●●●●● ●●● ● ● ● ●● ●● ● ● ●

60

●

● ●

● ● ●


● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ●●●●● ● ● ● ● ● ● ●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ●● ● ● ● ● ●● ● ●●●●●● ● ●● ● ● ●●● ● ● ●● ●● ● ● ● ● ● ● ●

00

●

●

●

0

●

●●

●

●

●

20

Linear Model

● ●● ●


●

●

80

●●

● ●

0

● ●●

20

● ● ●

●

●

0

r = 0.29

●

IRIS ●

r =-0.09 ● ●

0

40

0

●

● ●

40

● ● ●

● ●

●

30

●

●

● ● ●

immunoStates ●

Flow Cytometry Cytometry Cell Cell Frequency Frequency (%) (%) Flow


IRIS

20

●

● ●

20

●

0

20

●

●

●

60


60

●


●

● ●●

●

● ●

0

●

GSE65133

●

r = 0.78




● ● ●

20

● ●

0

● ● ● ●

40

●

● ●

60

LM22

40

60

●●

●●

60

r = 0.85

0

●

● ● ●

IRIS

20

●

40


60

●


0

● ● ● ●● ● ● ● ●● ● ● ●● ●● ●●● ●● ● ●●● ● ● ● ● ● ●● ● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●

immunoStates ●

r = 0.61

20

● ●


20

● ● ● ●

60


●

r =-0.02



IRIS 60

60


● ●

● ●● ●

●

40

●

● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ●● ● ●● ●● ● ● ● ●●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ●


●

● ●

●

●

● ●

● ●

20

0 40

●

r = 0.91

0

20

40


60


Supplementary Figure 8 LM22

immunoStates ●

●

r =-0.10

● ●●

0

0

0

20

40

10

60

20

0

0 10

20

30

40

50 20

0

5040

50

●

r = 0.50

● ●

●

75

●

0


●●

●

● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ●●● ● ●● ● ●● ● ● ●● ●● ● ●● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ● ● ●● 20 ●● ● ●●●●●● ● ●●● ● ●●●● ● ● ● ●●●● ● ●● ●● ● ●● ●● ●●●● ●● ● ●●● ● ●● ●● ● ● ●● ●● ●● ● ● ● ● ●● ●●● ●● ● ●● ● ●● ●● ● ●● ● ●●● ● ● ● ● ●● ● ● ●●●●●● ● ●● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ●●● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●●● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ●● ●●● ● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●●● ● 0 ●

40

●

60 20

40

●

60

CD8+ T-Cell


●

memory B-Cell

● ●

r = 0.33

0

20

40

60

80

20 0

●

r = 0.65

20

40

8040

0


●

40

0

20

40


60

● ●

●●

0 20

NK

60 60

●

r = 0.32●●

●

●

●

●

r = 0.65

● ●●

● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● 40 ● ●● ● ● 40 ● ● ● ● ● ●● ● ●● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ●● ●● ● ● ●● ●● ● ● ● ● ●● ●●● ●● ● ● ●● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●●● ●● ●● ● ● ● ●● ●● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ●● ● ● ●●●● ● ● ●● ● ●● ●●● ●● ● ● ●●● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ●●● ● ●● ●● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●● ● ●● ●●● ●● ● ● ●● ● ● ●●● ●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ●●● ●●● ●● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ●●● ● ●●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● 20 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●●● ● ● ●● ● ●● ● ●●● ● ●● ● ●● ● ● ●●●● ● ● ●● ● ●●● ● ●● ●● ●●● ● ● ● ● ● ● ●● ● ● ●●●● ●● ● ● ●● ● ● ●● ●●●● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ●● ●● ● ● ●●● ● ●● ●●● ● ●● ●● ● ● ● ●●● ●● ●●● ● ● ● ●● ●● ●●●● ● ● ●● ● ● ●● ● ● ●●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ● ●●● ● ●●● ● ●●●●●● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●●● ● ● ● ●● ● ● ●● ● ●● ● ●●● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ●● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ●● ●● ●●● ● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ●● ● ● ● ●● ●● ● ●● ● ● ●● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ●●●●● ●●●●● ● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ●●● ● ● ●●●●● ●●● ● ●● ● ● ● ● ●● ●●●● ● ●● ●●● ● ● ● ● ●● ●● ●● ● ●● ● ●● ●● ● ● ● ● ●● ●● ● ● ●●● ●● ● ●● ● ●● ●●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ●● ●●● ●●● ●●● ●● ●● ● ● ● ● ●●●●● ● ● ● ●●●●●● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ●●●●●● ●● ●● ●● ● ●● ● ● ●● ●● ●● ●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●●● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ●● ●● ●●● ● ● ●●●●● ●● ● ●● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●●● ● ● ●● ● ●● ● ● ● ●● ●●● ●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ●● ● ●●● ● ●●● ●●● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ●●●● ●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●●● ●● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ●● ●●●● ●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●●●● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ●●● ● ● ●●●●●●●● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ●● ● ● ● ●● ●● ● ●●● ●● ●0●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●●● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ●● ● ●● ● ●●●●●●● ● ● ● ● ●● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● 0 ●●●●●●●●

0

naïve B-Cell

immunoStates ●

r = -0.07

20

Robust Regression


LM22

● ● ● ●

60


CD14+ Monocyte CD4+ T-Cell

●

immunoStates ●



5040



0

0

30

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● 40 ● ●● 40 ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ●● ● ●●● ● ● ●● ●● ●●● ● ●●●● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ●● ● ●● ● ●● ● ● ● ●● ●● ● ● ● ●● ● ● ●●●●● ●●● ● ● ●●● ●● ● ● ● ● ●● ●● ● ●●● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ●●● ● ● ●●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ●●●● ●● ● ● ●● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●●● ●● ● ●● ● ●● ● ● ● ● ●● ●●● ●● ● ● ●● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ●●●●●● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● 20 20 ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ●● ● ●●● ●● ● ● ● ● ● ●● ● ● ●●● ●● ●● ●●● ● ●●● ●● ●● ●●● ● ● ●● ●●●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●●●● ● ● ●● ●●● ● ● ● ●● ● ●● ● ● ● ●●● ●● ●● ●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ●● ● ●● ●● ● ●● ● ●● ●● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ●● ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ● ●●● ●● ●● ● ●● ● ● ●●●● ● ● ●●● ● ● ● ● ●● ● ●●●●● ● ●●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ●●● ● ●●●● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ●● ●● ● ● ●●●●● ● ●● ●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ●●● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ●● ●● ● ● ● ●●●● ●● ● ●●● ● ●● ● ● ●● ●● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●●●● ●● ● ●● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●●●● ●● ● ●●● ●● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ●●● ● ● ●● ● ● ●● ● ● ●● ● ●●● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●●●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●●● ●●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●●● ●●● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ●●●●● ●●● ●● ● ●● ● ● ●●● ●● ● ●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ●●● ●● ●● ●●● ● ●●●● ●● ● ● ●● ●●●●● ● ●● ●●● ● ● ● ● ● ●●●●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ●●● ●●● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●●●● ● ● ●● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● 0●● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ●●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● 0 ●

immunoStates IRIS

20

25

● ● ●●

40

r = -0.05


r =-0.25

20


LM22

● ●

0

0 40 10

●

● ●


20

80

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●●● ●● ● ● ●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ●●● ● ● ● ●●● ● ● ● ● ● ●● ● ●● ● ●● ● ● ●●● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ●●●●● ●● ● ●●●●● ● ● ● ● ●●● ● ● ● ●●● ● ● ●●● ●● ● ● ● ●● ●● ● ●● ● ●● ● ● ● ● ●●● ●●●● ● ● ● ● ● 20 ●● ● ●●●● ●● ● ● ●● ●● ●●●●●● ● ● ●●● ● ● ●● ● ●● ●● ● ●●● ● ●● ●● ● ●●●● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ●● ● ●● ●● ● ●● ● ●● ● ●●● ● ●●● ● ● ● ● ● ● ● ●● ● ● ●● ●●●● ●●● ●● ● ●●●● ● ● ● ●●● ●●● ●● ● ● ● ● ●● ● ● ● ● ●●●●●●● ● ● ●● ●●● ● ●● ●●● ●● ● ● ●● ● ●● ● ● ●●●●● ● ●●●●● ● ●● ● ● ● ● ●● ● ● ●●●● ● ● ●●● ●● ● ●● ● ● ●●●●● ● ●● ●●●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ●●● ● ● ● ●● ●●● ● ● ●●● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ●●●● ● ● ●● ●●● ● ●●●●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ●●● ●● ● ● ● ● ● ●● ●●●● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ● ● ● ● ●●●● ● ●● ●● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ●● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ● ●● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ●●●●● ● ● ●● ●● ● ● ● ●● ● ●●●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ●● ●● ●● ●● 0●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●●●●● ●●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●●●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●●●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ●● ● ● ●

immunoStates IRIS

40

60 30

●



r = 0.41

Linear Model

immunoStates

● ●

0

40

LM22 ●

20

20


immunoStates IRIS

GSE59654

● ●

●

0

0


40

●

●

r = 0.59

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● 40 ● 40 ● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ● ●●● ● ●● ●● ●●● ● ● ●●●● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●●● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ●●●● ● ● ● ● ●● ● ● ● ●●●● ● ●● ● ●● ●● ●● ● ● ●● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ●● ● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ●●● ● ●● ●●●● ● ●●● ● ●● ●●●●●●● ●●● ● ● ● ● ●● ● ●●● ●● ● ● ● ●● ● ●●● ●●●●●● ● ●●● ●●●● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ●● ●●● ● ● ●●●● ●●● ●●● ● ●● ● ●●● ● ● ●● ●● ● ●● ● ● ●●● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ●●●●● ●●●● ● ● 20 ●● ●● 20 ●● ●●●● ● ● ●● ●●●● ● ● ●● ●● ● ●●●● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●●●● ●● ●●● ●● ● ●●● ● ● ● ●●● ● ● ● ●● ●● ●● ●●● ● ●● ● ●● ● ● ● ● ● ●● ● ●● ● ● ●●● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ●●●●● ● ●● ●●● ●● ●● ● ● ●● ●●●● ●● ● ● ● ● ●●● ● ●●●● ● ● ●● ● ● ●● ●●●● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ●● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ●●●● ●● ● ● ●● ● ●●● ●● ● ●● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ●●● ● ●● ●● ● ● ● ● ● ● ●● ● ●● ● ●●●● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ●●● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ●● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ● ● ●● ● ●●● ● ●● ● ●● ●● ● ● ●●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ●●●●●● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ●●●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ●● ● ● ● ● ● ● ●●● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ●● ●●● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ●● ● ●● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●●●● ● ● ● ●● ●● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●

● ●


0

●

●

r = 0.22


20

● ●


40



●


immunoStates IRIS

20

40

60


040

20

40


Support Vector Regression Supplementary Figure 8


● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ● ●● ● ●●●● ● ●●● ●

●

40

20 ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●

0

25

50

60

20 ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●●

75

20

●

40

20

0

20

20

40

20 ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ●● ● ● ●●

20

80

30

40

40

20

20 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●

0

20

40

60

● ●

20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

20


20

20 ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●

20

40

60


80

Complete Blood Count (%)

●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ●●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●

PMNs 40

60

80

Lymphocytes

●

● ● ● ●

r = 0.81

●●●● ●●● ●● ●●● ● ●●●● ●●●● ●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ●

60

● ●

40 ●

20 ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

60

20

●

● ●● ● ● ● ● ●● ● ●●● ●● ●●● ● ● ●● ● ●● ● ● ●●● ● ●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ●● ●● ● ● ● ●●● ● ●●● ●●● ● ●●● ● ●

40

Monocytes

Robust Regression

60


immunoStates

80

● ●

●

40

r = 0.55

● ● ●● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ●● ● ● ●●●●●●● ● ●●● ● ●● ● ●

40

80


r = 0.01



LM22 ● ● ● ● ● ● ●● ●●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ●● ●● ● ●● ●● ●● ● ● ● ●● ●● ● ●●● ● ●● ●

60

● ● ● ● ● ●● ● ●●● ● ● ●●● ● ●●● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ●

40

IRIS 80

●

60

80

●

●● ● ● ● ●● ●● ● ● ● ● ●● ● ●● ●● ● ● ●● ●● ●● ●●●●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●

50


40

● ●

immunoStates

80 Complete Blood Count (%)

● ● ● ● ●●●● ● ● ●●● ●●● ● ● ● ●● ● ● ●● ●●● ●●● ● ●● ● ● ● ● ● ● ● ● ●●● ●●● ●● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ●● ●●● ●● ●●● ● ●● ● ●

●

60

● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●●● ●●● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ●●

r = 0.98


r =-0.03

● ● ● ● ●● ● ● ●●● ● ●● ● ●● ●● ●● ● ●●●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ●● ● ● ● ●●●

40

60

LM22 ● ● ● ● ●

Model


● ● ●● ●● ●● ●●●● ●● ●● ● ●● ● ● ●● ●● ● ● ●● ●● ● ●● ● ● ●● ● ●● ●● ● ● ●● ●●● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ●

●

40

60

Stanford-Ellison 2011 Linear

immunoStates

60

IRIS


20

60

● ● ● ● ● ●●● ● ● ●● ● ●● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●●● ● ●● ● ●●● ●● ● ●●● ●●

r = 0.60


● ● ● ●● ● ●● ● ●●● ● ● ● ●● ● ●● ●● ● ●●●● ● ● ● ●●●● ● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●

40

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ●● ●


● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●



60

80

r = 0.26

● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●


60

LM22 ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●

0

40

● ●●● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ●● ● ● ●● ● ●●●● ● ●● ● ● ●

r = 0.86


IRIS 80

60

80

● ● ●● ●● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●●● ● ● ●● ● ● ● ● ●●●● ● ● ●● ● ● ●

●

40


80

immunoStates ● ●● ● ● ●●● ●●●●● ● ●● ● ● ●●● ●●● ● ●● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ●● ●● ● ●● ● ●● ● ●

r = 0.43

●

60

● ● ● ● ● ●●● ● ●●● ● ● ● ● ●●● ●● ●● ● ●●●● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ●●● ●● ●● ● ●●● ● ● ● ● ●●● ●●●● ● ● ●● ●●● ●● ● ● ● ● ● ●

40

20 ● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●

20

● ● ●● ●● ●● ● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ●● ● ●●●● ● ● ●● ●● ●● ●●● ● ● ● ●●● ●● ● ● ●● ●● ● ●● ● ●● ●● ●● ●●●● ● ● ● ●● ●●● ●● ● ●

40

80

r = 0.40

60


●


60

80

r =-0.07 Complete Blood Count (%)


LM22 ●● ●● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●


IRIS 80

r = 0.82

60

● ●● ● ● ● ● ●● ● ● ●●●● ●● ●●● ●● ● ● ● ●● ●● ● ●● ● ●●● ●● ● ● ●● ●●● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ●

●

40

●

●

● ●● ●● ● ●●● ● ●●● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●

20 ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

20

40


60


Supplementary Figure 10 IRIS

LM22

immunoStates ●

●

● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ●● ● ●● ● ●● ●● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●●● ●● ● ●●●● ●●●● ● ●

40

20 ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●

0

20

40

60

60

20 ● ● ● ●●●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●

0

80

● ● ● ● ● ● ●● ● ● ●● ●●● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ●● ● ●● ● ● ●● ●●●●● ●●● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ● ●

40

10

20


●

30

40

IRIS

20 ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●

0

20

40

80

60 ●

20 ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●

●

0

60

● ●● ● ● ● ● ●● ● ●●●●● ● ●●● ● ● ● ●●● ● ● ●● ●● ●●● ● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●

●

20

30

40

0

50

20 ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

20

40

60

r = 0.66

● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●● ● ●●●● ●● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●●●● ●● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●●●● ● ● ● ●● ●● ● ●●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●

60

40

20 ● ● ● ●●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●●● ● ● ●● ● ● ● ●●

10

20

30

40

80

r = 0.85

60

80

40

20 ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●

0

50


20

40

40

20 ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●

40

60


80


● ● ● ● ● ●● ● ● ● ●●●● ● ●● ● ● ●● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●●● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ●● ●

r = 0.60

60 ●

40

20 ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●●●

0

Robust Regression

60


●

80

r = 0.06

Monocytes

immunoStates

● ● ●● ● ● ●●●● ● ● ● ● ●●● ●● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ●●● ● ●● ●●●● ● ● ● ●● ●● ● ● ● ●● ●

Lymphocytes

● ● ●● ● ● ● ● ●● ● ●●●●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ●●● ● ● ● ●● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●●●●● ●●● ● ●● ● ●● ● ●

60

LM22

20

PMNs 40

●

●

0

80


immunoStates

●

IRIS

60

20

●

● ●●● ● ●●● ●● ● ●●● ● ●●● ● ● ● ● ●●● ●● ● ● ● ● ● ●●● ●●● ●● ●●●● ● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ●● ●



● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ●● ● ●● ● ● ●●●●● ● ● ●●●●●●● ●●● ●● ● ●● ●



80



20

● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ●● ● ● ● ●● ●●●● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ●●● ● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ●●


r = 0.01

40

80

60

●

● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ● ●●● ● ●● ●●● ● ● ● ●● ● ● ● ●● ● ●

0

40

LM22

0

r = 0.97

60

●

●

60

40

●

80

● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●●● ●●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ●●● ● ●● ● ● ●●● ● ●● ● ● ●●

40

IRIS 80

20

Model

immunoStates

r = 0.59


0

● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●




● ●● ●● ● ●● ●● ●● ●● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ●●● ● ● ●● ●● ●

40

0

20

●

r = 0.29 Complete Blood Count (%)


● ●● ● ● ●● ●● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●● ●● ● ●● ●

60

0

40

0

50

● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ●●● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ●● ●●● ● ● ●● ● ●●● ● ●●● ● ●●● ●● ● ● ● ●● ● ● ●● ●

60

LM22 ●

80

r = 0.90


20

● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ●● ●●● ● ●●● ● ● ●● ●● ●● ●● ●● ●●● ● ● ●●● ● ● ●● ●● ●● ● ● ● ●● ● ● ● ●●●● ● ●

40


●

80

●

60


0

r = 0.48

80

●●● ● ● ●●● ● ● ●●●●●●● ● ● ●●● ● ● ● ●●● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ●● ●● ● ● ●●●● ●● ● ● ● ●● ●● ● ● ●


60

80

r =-0.02

●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ●●● ● ●● ● ● ● ● ●●● ● ●●● ●



80

r = 0.89

60

40

20 ●● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●

0

20

●● ● ● ● ●● ●●● ●● ●●● ● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●●●● ● ●● ●●●●● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ●●● ● ● ● ● ● ●● ●● ● ●● ● ●●●● ● ●●● ● ● ● ● ●● ● ●●● ●● ●● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ●

40

60


● ●


Supplementary Figure 11 LM22

50

r = 0.02

● ● ●● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ●●● ● ● ● ● ●

●

25 ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●

0

20

40

60

75

50

25

● ● ● ● ● ● ● ●● ● ● ●● ● ●●●● ● ● ●● ● ●● ● ●● ●●● ● ●● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ●●● ●● ●●● ●● ●●● ● ● ● ● ●●● ● ● ●●● ● ● ● ●●●● ● ● ● ●● ●●● ● ● ●●● ● ●●●● ● ●● ● ●● ●● ● ●● ●● ●● ● ●● ●● ● ●● ●● ● ● ●● ● ● ●● ●● ● ● ●● ● ●

r = 0.91

80

20


●● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●●

10

20

30

40

75

50

25

● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ●●●●●●● ● ●● ● ●●● ●● ● ●● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ● ● ● ●●●● ●● ●● ● ●●●● ● ●● ● ● ●● ● ● ●● ●● ●●●● ● ●●●●●

50

20

●● ● ●● ● ● ●

● ● ● ● ●●●● ●●● ● ● ● ●● ●●● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ●● ●●●● ● ● ● ● ● ● ● ●● ●●●●● ● ● ● ● ●

●

25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

25

50

75

50

25

75

●

40

50

25

0

r = 0.84

30

40

20

40

60


75

PMNs

80

75

50

25

● ● ●● ●● ● ●● ● ●● ●● ● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●●● ●● ●●●●● ● ● ● ●●● ●● ●● ● ● ●● ● ● ●●● ●● ● ●● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●●● ●●● ●● ● ● ● ●●

10

Lymphocytes

20

30

40

Monocytes

Robust Regression

50


immunoStates ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ●●●● ●● ●● ● ● ●● ● ●● ●●

r = 0.94

● ● ● ●● ● ● ●

50 ●

25

60

r = 0.76

50

●

● ●● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ●●● ●● ● ●● ●● ●● ● ● ●● ●●● ●●●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●●●●● ● ● ● ● ● ● ●

20

40

● ● ●●

60


●


● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●


25

●● ● ● ● ●●● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ●

40

●

● ●

20



LM22

50

● ● ●●● ● ●● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●●●● ● ●● ● ● ●● ● ●● ● ● ● ●●● ●●● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●●●● ●● ● ● ●● ● ● ●●● ●● ●●● ● ●●●● ● ● ● ● ● ●● ●● ● ●●● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

immunoStates

●

r = 0.82

60

r = 0.59


IRIS


75

60

● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ●● ● ● ●●●● ●● ● ● ●● ● ● ● ●●●● ● ●● ●●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ●●● ●● ● ●●● ●● ●● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ●● ●● ●●●● ●● ● ●● ● ● ● ● ● ●

20


0

●


r = 0.12

●● ● ● ● ●● ●●● ● ● ●● ●● ●● ● ●● ● ● ● ●●● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ● ●● ● ● ●●



● ●● ● ● ● ●●●● ● ●● ●●● ●●● ●● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ●

LM22 ●

40

Model



IRIS

75

20


immunoStates

r = 0.92


●

● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ●● ●● ● ●● ● ● ● ●● ●● ● ● ● ●● ● ●●● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ●●● ● ● ●● ● ●● ●● ● ●

r = 0.87

60


25

0

25

●



● ● ● ● ● ●● ● ●●●●● ●● ● ● ●●● ● ●● ●● ●●● ● ● ●●●●●●● ●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ●●● ● ●●● ●● ●● ● ● ●●● ●● ● ● ● ●●● ●● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ●

50

50

50

LM22

r = 0.65

75

75


IRIS 75

40


75

immunoStates ●

● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●●



IRIS

75

50

25

● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●●●● ● ● ●● ● ● ● ●● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●●● ● ● ● ●●● ● ●● ●● ● ● ● ● ● ●● ●● ● ●● ● ●● ●● ● ●● ●●●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ●●● ● ● ● ●● ● ●●● ● ● ●

r = 0.87

10

20

30

40

50

60



Supplementary Figure 12 GSE65133 PERT

r = 0.20

60 ●

● ● ●

●

● ● ●

●

40



60

LM22 ●

● ● ● ●

●● ● ● ●●

20

0

● ●● ● ●●● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ●● ● ● ● ● ●●●● ● ●● ●● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●

0

10

20

30


●

●

40

immunoStates ●

r = 0.78

60

● ●

● ● ● ●

●

●

●

●

●

●

●

40 ● ●

●

● ●●

●

●

●● ● ●●●

20

0

● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●●● ●● ●●● ● ●●● ●●● ●● ●● ● ●●●●● ●● ●● ● ● ● ● ●● ●● ● ● ●● ●● ● ●● ●●● ●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●

0

10

●


IRIS

20

30

●

CD14+ Monocyte

●● ●

● ● ● ● ● ● ●

40

●

CD4+ T-Cell

●

●

●

CD8+ T-Cell

● ●

●●

● ●

●

● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ●● ●●●● ● ●● ● ● ● ● ●●● ● ●●● ●● ●● ● ● ●●● ●● ●●● ● ● ● ●● ●● ●● ●

20

0


●

r = 0.48

0

10

20

Gamma/Delta T-Cell memory B-Cell

●

naïve B-Cell NK 30




GSE59654 PERT

LM22

r = -0.33 ● ● ●

40

20

0

● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ● ●●● ●● ● ●●● ● ● ● ●●●●●● ●● ● ● ● ●●● ● ●● ● ●●● ● ●● ●●●● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ●● ● ●● ●● ●● ● ●● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●●●● ● ●● ● ●● ●●● ● ●●● ● ● ● ●● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●●● ●● ● ● ● ● ● ●●● ●●● ●● ● ●● ● ● ● ●● ●● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ●●● ●● ● ●●● ●●● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ●●●● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●

0

10

20

30


40

immunoStates ●

r = 0.64

● ●

● ●

40

20

0

●

●●

● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ●●● ● ●●● ● ● ●●● ● ●● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●●● ● ● ●●●● ●●● ●● ● ●● ● ●●● ●● ●● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●●●● ● ● ● ● ● ●● ● ● ● ● ●● ● ●●● ● ●●● ● ●●●● ● ● ●●● ● ● ● ● ●● ● ●●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●

0

20

r = -0.03

●

40


●



● ●


IRIS

● ●

40

20

0 60

CD14+ Monocyte

●

● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ●● ● ●●● ● ●●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ● ● ●●● ● ●● ●● ● ● ●●● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●●● ●● ● ● ●●●● ● ●●●● ● ● ● ● ● ● ● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ●● ●●● ●●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ●● ●● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●●●●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●●● ● ●● ● ● ●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ●

0

10

20

30

CD4+ T-Cell CD8+ T-Cell memory B-Cell naïve B-Cell ● ●

NK

● ● ●

40

50



Supplementary Figure 14 IRIS

LM22

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

75

● ● ● ● ● ● ● ● ●

●

25 ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ●

0

50

25

0

0

r=0

75

r = 0.91

40

60

80

0

50

Lymphocytes Monocytes

25

r = 0.84

50

2013

50

2013

2013

20


r = 0.75 PMNs

75

25

25

75

0

75

50


r = 0.16

75

2012

25

PERT

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

25

2012

50


r = -0.17

75

2012


50

0

0

0

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

r = 0.78

2011

25

50

75

2011

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

r = 0.64

75

2011

50

r = -0.14

Stanford-Ellison 2011-2013

immunoStates

25

0

20

40

60


0

20

40

60



Correlation plots between measured and estimated proportions. (7) Correlation plots of estimated (x-axis) and measured cell proportions (y-axis) for each method and matrix combination for samples in GSE65133. Correlation is measured by Pearson’s correlation coefficient. (8) Same as in (7) for GSE59654. (9,10,11) Same as in (7) for Stanford-Ellison 2011, 2012, and 2013 sample cohorts respectively. (12,13,14) Same as in previous figures but using the PERT method. 

Supplementary Figure 15 ●

r = 0.20

●

● ●

●

● ● ● ● ●● ● ● ● ● ● ●● ● ●● ●●● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●

20

●

●

●●

● ●

● ●

●

●

40

0

●

●

0

20

60

● ● ● ● ●

● ●●

● ● ●

● ● ● ●

●

● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●●●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●● ●

●

40

●

● ●

40

20

●

● ● ●

●●

0

●

immunoStates

r = 0.69

0

● ●

60

●

r = 0.91

●

● ●

40

20

40

●

● ●

● ● ●

●

● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ●

0



● ●●

0

20


b

CD14+ Monocyte CD4+ T-Cell

20

40

60

CD8+ T-Cell


Gamma/Delta T-Cell IRIS

LM22 ●

60

● ●

● ● ● ●

● ●

●

40

●

●

● ●

● ●

20

● ●

●

● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●

0

10

immunoStates ●

60 ● ● ●

● ●

●

●

●

●●

●

● ●

40

● ●● ●

●

●

20

● ● ●

●

●

●

● ●

●

● ●

● ●● ●

● ● ● ●

0 20

30

40


50

10

20

30

40


50



●

● ● ● ●


60

LM22 Flow Cytometry Cell Frequency (%)

IRIS



a

memory B-Cell ●

60

● ●● ●

●

40

●

●

NK

● ●

20 ● ● ●

0

●

●

●

0

●

● ●

naïve B-Cell

●

●

●

● ● ● ● ● ● ● ●● ● ● ●

● ● ●● ●

20

40

60


Supplementary Figure 15: Example of systematic under- and over-estimation of cell proportions in cell mixture deconvolution. (a) Correlation plots of estimated (x-axis) and measured cell proportions (y-axis) in GSE65133 for all matrices using Support Vector Regression. (b) Highlighted CD4+ T-cell (yellow) and Monocyte (red) cell proportion estimates against measured values. Solid lines represent the best fit with a Example of systematic over-estimation of cell proportions in cell linear model, whereas the dashed lineunderrepresentand the 45-degree diagonal.

mixture deconvolution. (a) Correlation plots of estimated (x-axis) and measured cell proportions (y-axis) in GSE65133 for all matrices using Support Vector Regression. (b) Highlighted CD4+ T-cell (yellow) and Monocyte (red) cell proportion estimates against measured values. Solid lines represent the best fit with a linear model, whereas the dashed line represent the 45-degree diagonal. 

n data from Affymetrix, immunoStates outperformed both Affymetrix-specific basis f the deconvolution method used. Similar comparison is also present in Figure 4, where orts were profiled using Affymetrix. As shown in Figure 4, immunoStates consistently ymetrix-specific basis matrices irrespective of the deconvolution method used.


100 60

60

40

75

30

40

40

50

20

20 25

20

10

00

0

0

20

40

0

6010

20

40 20

60

0

30 10

20

30

naïve B-Cell NK




100 60

60

40

75 40

40

50 20

20 25

00

20

0 0

20

40

0

10 60

20

20

40

60

0 30

10

20

30

40 40




Robust Regression


immunoStates r= r = 0.86


matrices and deconvoluted rofiled using platform As expected, GPL10558 is d number of a GPL10558s missing a cell types. deconvoluted 0558-specific was inverse timated and proportions, od used (first


’s suggestion, we created two Illumina-specific basis matrices. One of these two basis PL10558 as it GPL10558-specific Illumina-specific immunoStates basis matrix basis matrix only sorted immunoStates r = -0.71 r = 0.58 r = 0.79 r = 0.79 ion profiles platform (8 ; the second Linear matrix was Model immune cell CD14+ Monocyte CD4+ T-Cell that were CD8+ T-Cell immunoStates the Illumina Gamma/Delta T-Cell r = -0.74 r = 0.24 r = 0.79 r = 0.79 memory B-Cell asets, 1,670

-0.72

r = 0.43

r = 0.86

100 60

60

75

40

40

40

50 20

20 25

00

20

0 0

20

40

60

0

20

20

40

40 60

0

20

40 60

60


immunoStates r= r = 0.91

-0.65

r = 0.32

60

r = 0.91 60

50

75

40

40 50

40 30 20

20 25

20

10 0

0 0

20

40

60

0 20

20

40

60

40

0

20

40


Estimated Cell Proportions (%)

Figure R2: Comparison of Illumina-specific basis matrices with immunoStates.

ne cell types GPL10558-specific basis matrix, used in the first column, was created using only n usingComparison all sorted immune cell gene expression profiles GPL10558;with Illumina-specific of Illumina-specific basisusing matrices immunoStates. GPL10558mina, which basis matrix, used in the second column, was created using sorted immune cellonly sorted immune specific basis matrix, used in the first column, was created using an Illuminagene expression profiles using any Illumina microarrays; the third column used cell gene expression profiles using GPL10558; Illumina-specific basis matrix, used in immunoStates for deconvolution. Irrespective of the method used, estimated cell with the moresecond column, was created using sorted immune cell gene expression profiles proportions using immunoStates has higher correlation than both Illuminawever, it still using any Illumina microarrays; the third column used immunoStates for when deconvoluting GSE65133 that was generated using e cell deconvolution. types specific basis matrices Irrespective of the method used, estimated cell proportions using microarrays, GPL10558. naïve B immunoStates cells, Illumina-based has higher correlation than both Illumina- specific basis matrices when was low to moderate correlation betweenthat estimated proportions measured deconvoluting GSE65133 was cellular generated usingandIllumina-based microarrays, cond column in Figure R2). In contrast, immunoStates has consistently high correlation GPL10558.   portions (third column in Figure R2).

lts demonstrate that our proposed one-size-fits-most single matrix has higher accuracy basis matrix for each microarray platform.

Supplementary Table 1 Datasets used to measure platform bias

GSE

GPL

Brand

Samples

GSE38958

GPL5175

Affymetrix

115

GSE37912

GPL5175

Affymetrix

74

GSE21942

GPL570

Affymetrix

29

GSE19314

GPL570

Affymetrix

58

GSE22356

GPL570

Affymetrix

30

GSE55098

GPL570

Affymetrix

22

GSE17114

GPL570

Affymetrix

29

GSE17393

GPL571

Affymetrix

15

GSE23832

GPL6244

Affymetrix

12

GSE14577

GPL96

Affymetrix

15

GSE11907

GPL96

Affymetrix

122

GSE11909

GPL96

Affymetrix

115

GSE9006

GPL96

Affymetrix

105

GSE3365

GPL96

Affymetrix

68

GSE15573

GPL6102

Illumina

33

GSE18885

GPL6104

Illumina

127

GSE33463

GPL6947

Illumina

102