ADD luminex doe figure

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Nathan Dwarshuis 2021-07-29 13:46:05 -04:00
parent 1c65546e41
commit 572f10a9e4
2 changed files with 24853 additions and 23 deletions

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@ -1946,6 +1946,29 @@ provide these benefits.
\subsection{study design} \subsection{study design}
\begin{figure*}[ht!]
\begingroup
\includegraphics{../figures/modeling_overview.png}
\phantomsubcaption\label{fig:mod_overview_flow}
\phantomsubcaption\label{fig:mod_overview_doe}
\endgroup
\caption[Modeling Overview]
{Overview of modeling experiments.
\subcap{fig:mod_overview_flow}{Relationship
between \gls{doe} experiments and AI driven prediction. \glspl{doe} will
be used to determine optimal process input conditions, and longitudinal
multiomics data will be used to fit predictive models. Together, these
will reveal predictive species that may be used for \glspl{cqa} and
\glspl{cpp}.}
\subcap{fig:mod_overview_doe}{Overview of the two \gls{doe} experiments; the
initial \gls{doe} is given by the blue points and the augmented \gls{doe}
is given by the blue points.}
}
\label{fig:mod_overview}
\end{figure*}
The first DOE resulted in a randomized 18-run I-optimal custom design where each The first DOE resulted in a randomized 18-run I-optimal custom design where each
DMS parameter was evaluated at three levels: IL2 concentration (10, 20, and 30 DMS parameter was evaluated at three levels: IL2 concentration (10, 20, and 30
U/uL), DMS concentration (500, 1500, 2500 carrier/uL), and functionalized U/uL), DMS concentration (500, 1500, 2500 carrier/uL), and functionalized
@ -2206,29 +2229,6 @@ Venn diagram from the venn R package.
\input{../tables/doe_runs.tex} \input{../tables/doe_runs.tex}
\end{table} \end{table}
\begin{figure*}[ht!]
\begingroup
\includegraphics{../figures/modeling_overview.png}
\phantomsubcaption\label{fig:mod_overview_flow}
\phantomsubcaption\label{fig:mod_overview_doe}
\endgroup
\caption[Modeling Overview]
{Overview of modeling experiments.
\subcap{fig:mod_overview_flow}{Relationship
between \gls{doe} experiments and AI driven prediction. \glspl{doe} will
be used to determine optimal process input conditions, and longitudinal
multiomics data will be used to fit predictive models. Together, these
will reveal predictive species that may be used for \glspl{cqa} and
\glspl{cpp}.}
\subcap{fig:mod_overview_doe}{Overview of the two \gls{doe} experiments; the
initial \gls{doe} is given by the blue points and the augmented \gls{doe}
is given by the blue points.}
}
\label{fig:mod_overview}
\end{figure*}
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -2269,6 +2269,17 @@ Venn diagram from the venn R package.
% TODO this section header sucks % TODO this section header sucks
\subsection{AI modeling reveals highly predictive species} \subsection{AI modeling reveals highly predictive species}
\begin{figure*}[ht!]
\begingroup
\includegraphics{../figures/doe_luminex.png}
\endgroup
\caption[Cytokine release profile of T cells from DOE]
{T cells show robust and varying cytokine responses over time}
\label{fig:doe_luminex}
\end{figure*}
Due to the heterogeneity of the multivariate data collected and knowing that no Due to the heterogeneity of the multivariate data collected and knowing that no
single model structure is perfect for all applications, we implemented an single model structure is perfect for all applications, we implemented an
agnostic modeling approach to better understand these TN+TCM responses. To agnostic modeling approach to better understand these TN+TCM responses. To