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ADD single donor causal inference

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Nathan Dwarshuis 2021-09-03 18:44:29 -04:00
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32
tables/causal_inference_single.tex Normal file
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% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Fri, Sep 03, 2021 - 06:28:50 PM
\begin{tabular}{@{\extracolsep{5pt}}lccc}
\\[-1.8ex]\hline
\hline \\[-1.8ex]
% & \multicolumn{3}{c}{\textit{Dependent variable:}} \\
% \cline{2-4}
\\[-1.8ex] & log(Fold Change) & \ptmemp{} & \dpthp{} \\
% \\[-1.8ex] & (1) & (2) & (3)\\
\hline \\[-1.8ex]
Activation Method [DMS] & 0.668$^{***}$ & $-$0.011 & 0.063$^{***}$ \\
Feed Criteria [Glucose] & $-$0.047 & 0.006 & 0.010 \\
IL2 Feed Conc. [IU/ml] & 0.006 & 0.002$^{**}$ & 0.001 \\
Initial Cell Viability & $-$0.048 & 0.005 & 0.009 \\
Media Age (days) & $-$0.001 & $-$0.001$^{***}$ & 0.0001 \\
Thaw Media Age (days) & 0.0002 & 0.0005$^{***}$ & $-$0.0002 \\
IL2 Reconstituted Age & $-$0.001 & $-$0.0001 & 0.0001 \\
Operator [2] & $-$0.658$^{***}$ & 0.058 & 0.115$^{***}$ \\
Operator [3] & $-$0.652$^{*}$ & $-$0.214$^{***}$ & 0.183$^{***}$ \\
Media Sampled [True] & 0.314 & 0.067 & 0.011 \\
Constant & 7.770$^{*}$ & $-$0.208 & $-$1.389$^{**}$ \\
\hline \\[-1.8ex]
% Observations & 80 & 80 & 80 \\
R$^{2}$ & 0.486 & 0.534 & 0.722 \\
Adjusted R$^{2}$ & 0.412 & 0.466 & 0.682 \\
% Residual Std. Error (df = 69) & 0.434 & 0.070 & 0.059 \\
% F Statistic (df = 10; 69) & 6.536$^{***}$ & 7.891$^{***}$ & 17.931$^{***}$ \\
\hline
\hline \\[-1.8ex]
\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\
\end{tabular}

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tex/thesis.tex
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@ -2565,7 +2565,6 @@ longitudinally, so we included a boolean categorical to represented this
modification as removing conditioned media as the cell are expanding could modification as removing conditioned media as the cell are expanding could
disrupt signaling pathways. disrupt signaling pathways.
% TABLE these tables have extra crap in them that I don't need to show
\begin{table}[!h] \centering \begin{table}[!h] \centering
\caption{Causal Inference on treatment variables only} \caption{Causal Inference on treatment variables only}
\label{tab:ci_treat} \label{tab:ci_treat}
@ -2578,6 +2577,13 @@ disrupt signaling pathways.
\input{../tables/causal_inference_control.tex} \input{../tables/causal_inference_control.tex}
\end{table} \end{table}
\begin{table}[!h] \centering
\caption{Causal Inference on treatment variables and control variables (single
donor)}
\label{tab:ci_single}
\input{../tables/causal_inference_single.tex}
\end{table}
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -2624,10 +2630,10 @@ controlling for additional noise, the models explained much more variability
% X}. % X}.
Furthermore, the coefficient for activation method in the case of fold change Furthermore, the coefficient for activation method in the case of fold change
changed very little but still remained quite high (note the log-transformation) changed very little but still remained quite high (note the log-transformation)
with \SI{143}{\percent} increase in fold change compared to beads. Furthermore, with \SI{131}{\percent} increase in fold change compared to beads. Furthermore,
the coefficient for \ptmemp{} dropped to a \SI{2.7}{\percent} increase and the coefficient for \ptmemp{} dropped to a \SI{3.5}{\percent} increase and
almost became non-significant at $\upalpha$ = 0.05, and the \dpthp{} response almost became non-significant at $\upalpha$ = 0.05, and the \dpthp{} response
increased to almost a \SI{9}{\percent} increase and became highly significant. increased to a \SI{7.4}{\percent} increase and became highly significant.
Looking at the bioreactor treatment, we see that using the bioreactor in the Looking at the bioreactor treatment, we see that using the bioreactor in the
case of fold change and \ptmemp{} is actually harmful to the response, while at case of fold change and \ptmemp{} is actually harmful to the response, while at
the same time it seems to increase the \dpthp{} response. We should note that the same time it seems to increase the \dpthp{} response. We should note that
@ -2637,6 +2643,17 @@ bioreactor helped or hurt a certain response. For example, using a Grex entails
changing the cell surface and feeding strategy for the T cells, and any one of changing the cell surface and feeding strategy for the T cells, and any one of
these ‘mediating variables’ might actually be the cause of the responses. these ‘mediating variables’ might actually be the cause of the responses.
Finally, we stratified on the most common donor (vendor ID 338 from Astarte
Biotech) as this was responsible for almost half the data (80 runs) and repeated
the regression (\Cref{tab:ci_single}). Note that in this case, we did not
include any of the donor-dependent variables as well as any of the variables
that were the same value for these 80 runs. In this analysis, fold change and
\dpthp{} remained high (but slightly lowered from the full analysis) and
\ptmemp{} was non-significant. Given this, it appears that high \ptmemp{} may
have been due to other donors besides this one, and that high fold change and
\dpthp{} may have been driven by this single donor but more extreme in other
donors.
\section{Discussion} \section{Discussion}
% DISCUSSION this is fluffy % DISCUSSION this is fluffy