ADD sr figure and nmr cor figure

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
+Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and
-biologically assessed in terms of their known functions and activities
+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