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ADD results for does

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tables/doe_ratio.tex
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@ -1,22 +1,22 @@
% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Thu, Jul 29, 2021 - 04:15:57 PM
% Date and time: Thu, Jul 29, 2021 - 05:45:05 PM
\begin{tabular}{@{\extracolsep{5pt}}lc}
\\[-1.8ex]\hline
\hline \\[-1.8ex]
\\[-1.8ex] & CD4:CD8 CD62L+CCR7+ Ratio \\
\hline \\[-1.8ex]
Dataset [2] & 893,357.900$^{***}$ \\
Functional mAb \% & 28,209.730$^{***}$ \\
IL2 Conc. (IU/ml) & 50,896.490$^{***}$ \\
DMS Conc. (1/ml) & 926.925$^{***}$ \\
Intercept & $-$3,368,762.000$^{***}$ \\
Dataset [2] & 0.020 \\
Functional mAb \% & 0.002$^{***}$ \\
IL2 Conc. (IU/ml) & 0.001 \\
DMS Conc. (1/ml) & 0.0001$^{***}$ \\
Intercept & $-$0.144$^{*}$ \\
\hline \\[-1.8ex]
Observations & 30 \\
R$^{2}$ & 0.835 \\
Adjusted R$^{2}$ & 0.808 \\
Residual Std. Error & 493,168.700 (df = 25) \\
F Statistic & 31.571$^{***}$ (df = 4; 25) \\
R$^{2}$ & 0.879 \\
Adjusted R$^{2}$ & 0.860 \\
Residual Std. Error & 0.039 (df = 25) \\
F Statistic & 45.554$^{***}$ (df = 4; 25) \\
\hline
\hline \\[-1.8ex]
\textit{Note:} & \multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\

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tex/thesis.tex
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@ -50,6 +50,7 @@
\newacronym{cpp}{CPP}{critical process parameter}
\newacronym{dms}{DMS}{degradable microscaffold}
\newacronym{doe}{DOE}{design of experiments}
\newacronym{adoe}{ADOE}{adaptive design of experiments}
\newacronym{gmp}{GMP}{Good Manufacturing Practices}
\newacronym{cho}{CHO}{Chinese hamster ovary}
\newacronym{all}{ALL}{acute lymphoblastic leukemia}
@ -155,10 +156,12 @@
\end{flushleft}
}
% a BME's best friend
\newcommand{\invivo}{\textit{in vivo}}
\newcommand{\invitro}{\textit{in vitro}}
\newcommand{\exvivo}{\textit{ex vivo}}
% various CD-whatever crap
\newcommand{\cd}[1]{CD{#1}}
\newcommand{\anti}[1]{anti-{#1}}
\newcommand{\antih}[1]{anti-human {#1}}
@ -180,17 +183,21 @@
\newcommand{\ptcar}{\gls{car}+}
\newcommand{\ptcarp}{\ptcar~\si{\percent}}
% DOE responses I don't feel like typing ad-nauseam
\newcommand{\pilII}{\gls{il2} concentration}
\newcommand{\pdms}{\gls{dms} concentration}
\newcommand{\pmab}{functional \gls{mab} surface density}
% vendor and product stuff I don't feel like typing
\newcommand{\catnum}[2]{(#1, #2)}
\newcommand{\product}[3]{#1 \catnum{#2}{#3}}
\newcommand{\thermo}{Thermo Fisher}
\newcommand{\miltenyi}{Miltenyi Biotech}
\newcommand{\bl}{Biolegend}
% the obligatory misc category
\newcommand{\inlinecode}{\texttt}
\newcommand{\subcap}[2]{\subref{#1}) #2}
\newcommand{\sigkey}{Significance test key: *p<0.1; **p < 0.05; ***p<0.01}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@ -2201,7 +2208,6 @@ between T cells. Since \gls{il2} is secreted by activated T cells themselves,
T cells in the \gls{dms} system may need less or no \gls{il2} if this hypothesis
were true.
% TODO this plots proportions look dumb
% TODO explain what the NLS lines are in b
% TODO plot the differences in lower IL2 concentrations to better show this
@ -2263,6 +2269,30 @@ at \SI{10}{\IU\per\ml} throughout the remainder of this aim.
\input{../tables/doe_runs.tex}
\end{table}
% FIGURE first DOE results to show how the second DOE was motivated
We conducted two consecutive \glspl{doe} to optimize the \pth{} and \ptmem{}
responses for the \gls{dms} system. In the first \gls{doe} we, tested \pilII{} in
the range of \SIrange{10}{30}{\IU\per\ml}, \pdms{} in the range of
\SIrange{500}{2500}{\dms\per\ml}, and \pmab{} in the range of
\SIrange{60}{100}{\percent}.
% TODO explain why not all runs were used
After performing the first \gls{doe} we augmented the original design matrix
with an \gls{adoe} which was built with three goals in mind. Firstly we wished
to validate the first \gls{doe} by assessing the strength and responses of each
effect. Secondly, we wished to improve our confidence in regions that showed
high complexity, such as the peak in the \gls{dms} concentration for the total
\ptmem{} cell response. Thirdly, we wished to explore additional ranges of each
response. Since \pilII{} and \pdms{} appeared to continue positively influence
multiple responses beyond our tested range, we were curious if there was an
optimum at some higher setting of either of these values. For this reason, we
increased the \pilII{} to include \SI{40}{\IU\per\ml} and the \pdms{} to
\SI{3500}{\dms\per\ml}. Note that it was impossible to go beyond
\SI{100}{\percent} for the \pmab{}, so runs were positioned for this parameter
with validation and confidence improvements in mind. The runs for each \gls{doe}
were shown in \cref{tab:doe_runs}.
\begin{figure*}[ht!]
\begingroup
@ -2316,6 +2346,57 @@ at \SI{10}{\IU\per\ml} throughout the remainder of this aim.
\input{../tables/doe_ratio.tex}
\end{table}
The response plots from both \glspl{doe} are shown in \cref{fig:doe_responses}
for total \ptmem{} cells, total \pth{} cells, total \ptmemh{} cells, and CD4:CD8
ratio in the \ptmem{} compartment. In general, the responses for the first and
second \gls{doe} seemed to overlap, although not perfectly. Interestingly, only
the \ptmem{} response seemed to have anything more complex than a linear
relationship, particularly in the case of \pilII{} and \pdms{}, which showed
intermediate optimums (\cref{fig:doe_responses_mem}). In the case of \pilII{},
it was not clear if this optimum was simply due to a batch effect of being from
the first or second \gls{doe}. The optimum for \pdms{} appeared in the same
location albeit more pronounced in the second \gls{doe} so, giving more
confidence to the location of this second order feature. The remainder of the
responses showed mostly linear relationships in all parameter cases
(\cref{fig:doe_responses_cd4,fig:doe_responses_mem4,fig:doe_responses_ratio}).
% TODO it seems arbitrary that I went straight to a third order model, the real
% reason is because it seemed weird that a second order model didn't find
% anything to be significant
We performed linear regression on the three input parameters as well as a binary
parameter representing if a given run came from the first or second \gls{doe}
(called `dataset'). Starting with the total \ptmem{} cells response, we fit a
first order regression model using these four parameters
(\cref{tab:doe_mem1.tex}). While \pilII{} was found to be a significant
predictor, the model fit was extremely poor ($R^2$ of 0.331). This was not
surprising given the apparent complexity of this response
(\cref{fig:doe_responses_mem}). To obtain a better fit, we added second and
third degree terms (\cref{tab:doe_mem2.tex}). Note that the dataset parameter
was not included in the second order interaction as this was treated as a
blocking variable, which are typically not assumed to have interaction effects.
Also note that the response was log-transformed, which yielded a better fit. In
this model many more parameters emerged as being significant, including the
quadratic terms for \pdms{} and \pilII{}, in agreement with what can be
qualitatively observed in the response plot (\cref{fig:doe_responses_mem}).
Furthermore, the dataset parameter was weakly significant, indicating a possible
batch effect between the \glspl{doe}. We should also note that despite many
parameters being significant, this model was still only mediocre in describing
this response; the $R^2$ was 0.741 but the adjusted $R^2$ was 0.583, indicating
that our data might be underpowered for a model this complex. Further
experiments beyond what was performed here may be needed to fully describe this
response.
% TODO combine these tables into one
We performed linear regression on the other three responses, all of which
performed much better than the \ptmem{} response as expected given the much
lower apparent complexity in the response plots
(\cref{fig:doe_responses_cd4,fig:doe_responses_mem4,fig:doe_responses_ratio}).
All these models appeared to fit will, with $R^2$ and adjusted $R^2$ upward of
0.8. In all but the CD4:CD8 \ptmem{} ratio, the dataset parameter emerged as
significant, indicating a batch effect between the \glspl{doe}. All other
parameters except \pilII{} in the case of CD4:CD8 \ptmem{} ratio were
significant predictors.
\begin{figure*}[ht!]
\begingroup
@ -2330,9 +2411,17 @@ at \SI{10}{\IU\per\ml} throughout the remainder of this aim.
\subcap{fig:doe_sr_contour_ratio}{CD4:CD8 ratio in the \ptmem{}
compartment}.
}
\label{fig:doe_responses}
\label{fig:doe_sr_contour}
\end{figure*}
We then visualized the total \ptmemh{} cells and CD4:CD8 \ptmem{} ratio using
the response explorer in DataModeler to create contour plots around the maximum
responses. For both, it appeared that maximizing all three input parameters
resulted in the maximum value for either response (\cref{fig:doe_responses}).
While not all combinations at and around this optimum were tested, the model
nonetheless showed that there were no other optimal values or regions elsewhere
in the model.
% TODO this section header sucks
\subsection{AI modeling reveals highly predictive species}