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diff --git a/data_preparation/main.acn b/data_preparation/main.acn new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/data_preparation/main.acn diff --git a/data_preparation/main.pdf b/data_preparation/main.pdf Binary files differnew file mode 100644 index 0000000..f401b33 --- /dev/null +++ b/data_preparation/main.pdf diff --git a/data_preparation/main.tex b/data_preparation/main.tex index 9c729b3..5186dc3 100644 --- a/data_preparation/main.tex +++ b/data_preparation/main.tex @@ -15,4 +15,57 @@ \section{Data selection} +The data that we use in this work is based on the data used in the simon +manuscript \citep{tomicSIMONAutomatedMachine2019}. This subset of the fluprint +database comprises data from 5 clinical studies, most importantly the +longitudal study SLVP015. It only uses the first visits of donors, as the +classification is the most complete in this dataset +\autoref{fig:dataRepeatvisits}. We will use this dataset to model the high +vaccine response with as predictors the in total 3285 features measured in +assays done by the clinical studies. The data will be prepared in the same way +as in the original work, using the mulset algorithm +\citep{tomicSIMONAutomatedMachine2019}. +\begin{figure} + \includegraphics[width=\textwidth]{data_selection} + \caption{caption}\label{fig:dataRepeatvisits} +\end{figure} + +In addition to repeating a similar procedure as in +\cite{tomicSIMONAutomatedMachine2019}, we will compare the values of features +selected by the models trained on the first visit data to those of second visit +data. Initially the plan was to train new models on the second visit data, +however the classes are extremely unbalanced in the data of repeat visits +\autoref{fig:dataRepeatvisits}. For example in the first visit there are 65 +high responders and 130 low responders, in the second visit there is only data +available for 6 high responders and 44 low responders. Therefore we will only +train model on first visit data, and use the knowledge gained to explore second +visit data. + +\section{Clean data} + +The goal is to obtain one or more tables from the simon data suitable to train +models, thus we are looking to change the data from the long format as in the +database in a wide format where each column is a features measured in an assay. +When attempting to do this it was discovered that some assay data contained +duplicate readouts \autoref{tbl:exampleDuplicate}. Since the values were all +similar it was decided to aggregate the values to unique features using the +mean value. + +\begin{table} +\addtolength{\leftskip} {-2cm} % increase (absolute) value if needed +\addtolength{\rightskip} {-2cm} % increase (absolute) value if needed +\begin{tabular}{rrrrrlrllrrl} +\toprule{} +donor\_id & study & age & outcome & year & type & hai\_response & name & data\_name & assay & data & dup\\ +\midrule{} +285 & 18 & 9.47 & 0 & 2009 & pre & 1 & CD4+ T cells & CD4\_pos\_T\_cells & 13 & 33.8 & TRUE\\ +285 & 18 & 9.47 & 0 & 2009 & pre & 1 & CD4+ T cells & CD4\_pos\_T\_cells & 13 & 34.1 & TRUE\\ +285 & 18 & 9.47 & 0 & 2009 & pre & 1 & CD4+ T cells & CD4\_pos\_T\_cells & 13 & 34.3 & TRUE\\ +285 & 18 & 9.47 & 0 & 2009 & pre & 1 & CD4+ T cells & CD4\_pos\_T\_cells & 13 & 33.0 & TRUE\\ +\bottomrule{} +\end{tabular} + \caption{}\label{tbl:exampleDuplicate} +\end{table} + +\end{document} |