ADD single donor causal inference
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% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
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% Date and time: Fri, Sep 03, 2021 - 06:28:50 PM
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\begin{tabular}{@{\extracolsep{5pt}}lccc}
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\\[-1.8ex]\hline
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\hline \\[-1.8ex]
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% & \multicolumn{3}{c}{\textit{Dependent variable:}} \\
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% \cline{2-4}
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\\[-1.8ex] & log(Fold Change) & \ptmemp{} & \dpthp{} \\
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% \\[-1.8ex] & (1) & (2) & (3)\\
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\hline \\[-1.8ex]
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Activation Method [DMS] & 0.668$^{***}$ & $-$0.011 & 0.063$^{***}$ \\
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Feed Criteria [Glucose] & $-$0.047 & 0.006 & 0.010 \\
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IL2 Feed Conc. [IU/ml] & 0.006 & 0.002$^{**}$ & 0.001 \\
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Initial Cell Viability & $-$0.048 & 0.005 & 0.009 \\
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Media Age (days) & $-$0.001 & $-$0.001$^{***}$ & 0.0001 \\
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Thaw Media Age (days) & 0.0002 & 0.0005$^{***}$ & $-$0.0002 \\
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IL2 Reconstituted Age & $-$0.001 & $-$0.0001 & 0.0001 \\
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Operator [2] & $-$0.658$^{***}$ & 0.058 & 0.115$^{***}$ \\
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Operator [3] & $-$0.652$^{*}$ & $-$0.214$^{***}$ & 0.183$^{***}$ \\
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Media Sampled [True] & 0.314 & 0.067 & 0.011 \\
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Constant & 7.770$^{*}$ & $-$0.208 & $-$1.389$^{**}$ \\
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\hline \\[-1.8ex]
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% Observations & 80 & 80 & 80 \\
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R$^{2}$ & 0.486 & 0.534 & 0.722 \\
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Adjusted R$^{2}$ & 0.412 & 0.466 & 0.682 \\
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% Residual Std. Error (df = 69) & 0.434 & 0.070 & 0.059 \\
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% F Statistic (df = 10; 69) & 6.536$^{***}$ & 7.891$^{***}$ & 17.931$^{***}$ \\
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\hline
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\hline \\[-1.8ex]
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\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\
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\end{tabular}
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@ -2565,7 +2565,6 @@ longitudinally, so we included a boolean categorical to represented this
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modification as removing conditioned media as the cell are expanding could
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modification as removing conditioned media as the cell are expanding could
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disrupt signaling pathways.
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disrupt signaling pathways.
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% TABLE these tables have extra crap in them that I don't need to show
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\begin{table}[!h] \centering
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\begin{table}[!h] \centering
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\caption{Causal Inference on treatment variables only}
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\caption{Causal Inference on treatment variables only}
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\label{tab:ci_treat}
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\label{tab:ci_treat}
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@ -2578,6 +2577,13 @@ disrupt signaling pathways.
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\input{../tables/causal_inference_control.tex}
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\input{../tables/causal_inference_control.tex}
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\end{table}
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\end{table}
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\begin{table}[!h] \centering
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\caption{Causal Inference on treatment variables and control variables (single
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donor)}
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\label{tab:ci_single}
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\input{../tables/causal_inference_single.tex}
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\end{table}
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\begin{figure*}[ht!]
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\begin{figure*}[ht!]
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\begingroup
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\begingroup
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@ -2624,10 +2630,10 @@ controlling for additional noise, the models explained much more variability
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% X}.
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% X}.
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Furthermore, the coefficient for activation method in the case of fold change
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Furthermore, the coefficient for activation method in the case of fold change
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changed very little but still remained quite high (note the log-transformation)
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changed very little but still remained quite high (note the log-transformation)
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with \SI{143}{\percent} increase in fold change compared to beads. Furthermore,
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with \SI{131}{\percent} increase in fold change compared to beads. Furthermore,
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the coefficient for \ptmemp{} dropped to a \SI{2.7}{\percent} increase and
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the coefficient for \ptmemp{} dropped to a \SI{3.5}{\percent} increase and
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almost became non-significant at $\upalpha$ = 0.05, and the \dpthp{} response
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almost became non-significant at $\upalpha$ = 0.05, and the \dpthp{} response
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increased to almost a \SI{9}{\percent} increase and became highly significant.
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increased to a \SI{7.4}{\percent} increase and became highly significant.
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Looking at the bioreactor treatment, we see that using the bioreactor in the
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Looking at the bioreactor treatment, we see that using the bioreactor in the
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case of fold change and \ptmemp{} is actually harmful to the response, while at
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case of fold change and \ptmemp{} is actually harmful to the response, while at
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the same time it seems to increase the \dpthp{} response. We should note that
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the same time it seems to increase the \dpthp{} response. We should note that
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@ -2637,6 +2643,17 @@ bioreactor helped or hurt a certain response. For example, using a Grex entails
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changing the cell surface and feeding strategy for the T cells, and any one of
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changing the cell surface and feeding strategy for the T cells, and any one of
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these ‘mediating variables’ might actually be the cause of the responses.
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these ‘mediating variables’ might actually be the cause of the responses.
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Finally, we stratified on the most common donor (vendor ID 338 from Astarte
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Biotech) as this was responsible for almost half the data (80 runs) and repeated
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the regression (\Cref{tab:ci_single}). Note that in this case, we did not
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include any of the donor-dependent variables as well as any of the variables
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that were the same value for these 80 runs. In this analysis, fold change and
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\dpthp{} remained high (but slightly lowered from the full analysis) and
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\ptmemp{} was non-significant. Given this, it appears that high \ptmemp{} may
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have been due to other donors besides this one, and that high fold change and
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\dpthp{} may have been driven by this single donor but more extreme in other
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donors.
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\section{Discussion}
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\section{Discussion}
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% DISCUSSION this is fluffy
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% DISCUSSION this is fluffy
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