diff --git a/figures/nmr_cors.svg b/figures/nmr_cors.svg
new file mode 100644
index 0000000..e865a86
--- /dev/null
+++ b/figures/nmr_cors.svg
@@ -0,0 +1,12458 @@
+
+
+
+
diff --git a/figures/sr_omics.svg b/figures/sr_omics.svg
new file mode 100644
index 0000000..4ef2ebd
--- /dev/null
+++ b/figures/sr_omics.svg
@@ -0,0 +1,8539 @@
+
+
+
+
diff --git a/tex/thesis.tex b/tex/thesis.tex
index c732626..154331b 100644
--- a/tex/thesis.tex
+++ b/tex/thesis.tex
@@ -2309,14 +2309,25 @@ three methods. Lastly, SR and PLSR achieved R2>90\% while other ML methods
exhibited exceedingly variable LOO-R2 (0.3\%,RF-51.5\%,LASSO) for CD8+ TN+TCM
cells.
-% FIGURE the CD4/CD8 model results using SR
+\begin{figure*}[ht!]
+ \begingroup
+
+ \includegraphics{../figures/sr_omics.png}
+
+ \endgroup
+ \caption[Symbolic Regression Cytokine Dependencies]
+ {Multi-omics culturing media prediction profiles at day 6 using symbolic
+ regression.}
+ \label{fig:sr_omics}
+\end{figure*}
The top-performing technique, SR, showed that the median aggregated predictions
for CD4+ and CD8+ TN+TCM cells increases when IL2 concentration, IL15, and IL2R
increase while IL17a decreases in conjunction with other features. These
patterns combined with low values of DMS concentration and GM-CSF uniquely
characterized maximum CD8+ TN+TCM. Meanwhile, higher glycine but lower IL13 in
-combination with others showed maximum CD4+ TN+TCM predictions (Fig.2).
+combination with others showed maximum CD4+ TN+TCM predictions
+(\cref{fig:sr_omics}).
\begin{figure*}[ht!]
\begingroup
@@ -2345,6 +2356,30 @@ concentration, were commonly selected in >=5 ML methods (Fig.3a,c). Moreover,
IL13 and IL15 were found predictive in combination with these using SR
(Supp.Table.S4).
+\begin{figure*}[ht!]
+ \begingroup
+
+ \includegraphics{../figures/nmr_cors.png}
+ \phantomsubcaption\label{fig:nmr_cors_lactate}
+ \phantomsubcaption\label{fig:nmr_cors_formate}
+ \phantomsubcaption\label{fig:nmr_cors_glucose}
+ \phantomsubcaption\label{fig:nmr_cors_matrix}
+
+ \endgroup
+ \caption[NMR Day 4 correlations]
+ {\gls{nmr} features at day 4 are strongly correlated with each other and the
+ response variables. Highly correlated relationships are shown for
+ \subcap{fig:nmr_cors_lactate}{lactate},
+ \subcap{fig:nmr_cors_formate}{formate}, and
+ \subcap{fig:nmr_cors_glucose}{glucose}. Blue and blue connections indicate
+ positive and negative correlations respectively. The threshold for
+ visualizing connections in all cases was 0.8.
+ \subcap{fig:nmr_cors_matrix}{The correlation matrix for all predictive
+ features and the total \ptmemh{} response.}
+ }
+ \label{fig:nmr_cors}
+\end{figure*}
+
\section{discussion}
% optimization of process features
@@ -2384,8 +2419,8 @@ features were observed to slightly improve prediction and dominated the ranking
of important features and variable combinations when modeling together with NMR
media analysis and process parameters (Fig.3b,d).
-Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and IL15 were
-biologically assessed in terms of their known functions and activities
+Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and
+IL15 were biologically assessed in terms of their known functions and activities
associated with T cells. T helper cells secrete more cytokines than T cytotoxic
cells, as per their main functions, and activated T cells secrete more cytokines
than resting T cells. It is possible that some cytokines simply reflect the
@@ -2423,8 +2458,6 @@ ability to induce large numbers of memory T cells by functioning in an
autocrine/paracrine manner and could be explored by blocking either the cytokine
or its receptor.
-% FIGURE correlation plots from supplement (as alluded to here)
-
Moreover, many predictive metabolites found here are consistent with metabolic
activity associated with T cell activation and differentiation, yet it is not
clear how the various combinations of metabolites relate with each other in a