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% hello
\input{../preamble.tex}
\makeglossaries
\input{../bussiness_glossary.tex}
\input{../data_mining_glossary.tex}
\input{../acronyms.tex}
\begin{document}
\MyTitle{Data Understanding Report}
\tableofcontents
\printglossary[type=bus]
\printglossary[type=dm]
\printglossary[type=\acronymtype]
\section{Initial data collection}
\subsection{Technicalities}
\subsubsection{MySQL database set up and data import}
By following the guide on the
\href{https://github.com/LogIN-/fluprint}{FluPrint Github Repository} the MySQL
server was set up. In this work the FluPrint github was first added as a
submodule. This module provides the php scripts to import raw data csv's into
the MySQL database. The operating system and versions of php and MySQL used in
this work were OSX "Big Sur" (on Mac Book air 2017), php 7.3.24 (built-in mac
version), and MySQL 8.0.23 (homebrew).
In the \href{https://github.com/LogIN-/fluprint}{guide} the dependencies to run
the php import script were installed first. This was also done in this work,
except that the hash-file verification step was skipped.
After the php dependencies were installed the MySQL server was started. By
default homebrew recommends to use the \lstinline{homebrew services [option] [SERVICE]} command to start the MySQL server. However, in this work the server
is started using \lstinline{mysql.server start} which provides a socket that
was symlinked using \lstinline{sudo ln -s /tmp/mysql.sock /var/mysql/mysql.sock}. This was done to prevent an error
(\href{https://stackoverflow.com/questions/15016376/cant-connect-to-local-mysql-server-through-socket-homebrew/18090173}{StackOverflow: cant connect to local mysql server through socket homebrew}) thrown
by the php import scripts. Before the import scripts were run a user was added to the
MySQL server and a database was created \ref{lst:addUser}, the password type had to be \lstinline{mysql_native_password}
(\href{https://stackoverflow.com/questions/62873680/how-to-resolve-sqlstatehy000-2054-the-server-requested-authentication-metho}{how to resolve [SQLSTATEHY000] 2054 the server requested authentication method.}).
\begin{lstlisting}[language=sql, caption=Adding user and database to sql server, label={lst:addUser}]
mysql> CREATE USER 'mike'@'localhost' IDENTIFIED BY ';lkj';
mysql> GRANT ALL PRIVILEGES ON * . * TO 'mike'@'localhost';
mysql> ALTER USER 'mike'@'localhost' IDENTIFIED WITH mysql_native_password BY 'mike';
mysql> CREATE DATABASE fluprint;
\end{lstlisting}
The databasename, the username, and password were added to the
\lstinline{config/configuration.json} of the FlruPrint github module. At this
point the configuration for the php import scripts was finished, and the raw
data downloaded in \lstinline{data/upload} were imported in the MySQL server
using \lstinline{php bin/import.php}.
\subsection{Data Requirements}
The following subsections will list the attributes required from the data per
data mining goal.
\subsubsection{Explore and describe SQL queries and corresponding csv tables.}
\subsubsection{Model and visualise the different clinical study populations.}
\subsubsection{Model and visualise the difference between vaccination types.}
\subsubsection{Model and visualise repeat vaccination effects.}
\subsubsection{Apply standard feature selection methods to the most interesting dataset.}
\subsubsection{Fit classification models to the most interesting dataset.}
\citep{chattopadhyaySinglecellTechnologiesMonitoring2014}
The complex heterogeneity of cells, and their interconnectedness with each
other, are major challenges to identifying clinically relevant measurements
that reflect the state and capability of the immune system. Highly multiplexed,
single-cell technologies may be critical for identifying correlates of disease
or immunological interventions as well as for elucidating the underlying
mechanisms of immunity. Here we review limitations of bulk measurements and
explore advances in single-cell technologies that overcome these problems by
expanding the depth and breadth of functional and phenotypic analysis in space
and time. The geometric increases in complexity of data make formidable hurdles
for exploring, analyzing and presenting results. We summarize recent approaches
to making such computations tractable and discuss challenges for integrating
heterogeneous data obtained using these single-cell technologies.
\citep{galliEndOmicsHigh2019}
High-dimensional single-cell (HDcyto) technologies, such as mass cytometry
(CyTOF) and flow cytometry, are the key techniques that hold a great promise
for deciphering complex biological processes. During the last decade, we
witnessed an exponential increase of novel HDcyto technologies that are able to
deliver an in-depth profiling in different settings, such as various autoimmune
diseases and cancer. The concurrent advance of custom data-mining algorithms
has provided a rich substrate for the development of novel tools in
translational medicine research. HDcyto technologies have been successfully
used to investigate cellular cues driving pathophysiological conditions, and to
identify disease-specific signatures that may serve as diagnostic biomarkers or
therapeutic targets. These technologies now also offer the possibility to
describe a complete cellular environment, providing unanticipated insights into
human biology. In this review, we present an update on the current cutting-edge
HDcyto technologies and their applications, which are going to be fundamental
in providing further insights into human immunology and pathophysiology of
various diseases. Importantly, we further provide an overview of the main
algorithms currently available for data mining, together with the conceptual
workflow for high-dimensional cytometric data handling and analysis. Overall,
this review aims to be a handy overview for immunologists on how to design,
develop and read HDcyto data.
\citep{simoniMassCytometryPowerful2018}
Advancement in methodologies for single cell analysis has historically been a
major driver of progress in immunology. Currently, high dimensional flow
cytometry, mass cytometry and various forms of single cell sequencing-based
analysis methods are being widely adopted to expose the staggering
heterogeneity of immune cells in many contexts. Here, we focus on mass
cytometry, a form of flow cytometry that allows for simultaneous interrogation
of more than 40 different marker molecules, including cytokines and
transcription factors, without the need for spectral compensation. We argue
that mass cytometry occupies an important niche within the landscape of
single-cell analysis platforms that enables the efficient and in-depth study of
diverse immune cell subsets with an ability to zoom-in on myeloid and lymphoid
compartments in various tissues in health and disease. We further discuss the
unique features of mass cytometry that are favorable for combining multiplex
peptide-MHC multimer technology and phenotypic characterization of antigen
specific T cells. By referring to recent studies revealing the complexities of
tumor immune infiltrates, we highlight the particular importance of this
technology for studying cancer in the context of cancer immunotherapy. Finally,
we provide thoughts on current technical limitations and how we imagine these
being overcome.
\printbibliography
\end{document}
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