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% Most of the following are not required for the majority
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% Document Info
\author{Nimisha (lemonbuzz)}
\pdfinfo{
  /Title (data-analysis-in-genomics-and-transdriptomics.pdf)
  /Creator (Cheatography)
  /Author (Nimisha (lemonbuzz))
  /Subject (Data Analysis in Genomics and Transdriptomics Cheat Sheet)
}

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\noindent
\begin{multicols}{3}
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    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Data Analysis in Genomics and Transdriptomics Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Nimisha (lemonbuzz)} via \textcolor{DarkBackground}{\uline{cheatography.com/132716/cs/40775/}}}
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\noindent
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}Nimisha (lemonbuzz) \\
  \uline{cheatography.com/lemonbuzz} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 11th October, 2023.\\
   Page {\thepage} of \pageref{LastPage}.
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  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
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% from this page to resize cheat sheet text:
% www.emerson.emory.edu/services/latex/latex_169.html
\footnotesize % Small font.

\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{p{0.4977 cm} x{4.4793 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Self-Organizing Map (SOM)}}  \tn
% Row 0
\SetRowColor{LightBackground}
 & - create a 1D/2D lattice of artificial neurons \{\{nl\}\} - define no of neurons in each dimension \{\{nl\}\} - assign random weights in same dim as input \{\{nl\}\} - select random data point from input \{\{nl\}\} - choose winning neuron based on similarity with input \{\{nl\}\} - update weights of winning and neighboring neurons (based on learning rate \& neighborhood function) \{\{nl\}\} - repeat for all data points until convergence \tn 
% Row Count 12 (+ 12)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{0.89586 cm} x{4.08114 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Principle Component Analysis (PCA)}}  \tn
% Row 0
\SetRowColor{LightBackground}
\seqsplit{Algorithm} & - obtain distance matrix \{\{nl\}\}- construct matric matrix\{\{nl\}\}- compute eigen values and eigen vectors of matric matrix \{\{nl\}\}- compute cartesian coordinates \tn 
% Row Count 5 (+ 5)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{5.377cm}}{PCA focuses on maximizing variance, and compromises resolution of proximal clusters.} \tn 
% Row Count 7 (+ 2)
% Row 2
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\mymulticolumn{2}{x{5.377cm}}{A distance matrix doesn't reveal the underlying dimensionality of the space in which these points exist.} \tn 
% Row Count 10 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{0.89586 cm} x{4.08114 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{t-distributed Stochastic Neighbor Embedding (tSNE)}}  \tn
% Row 0
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\seqsplit{Algorithm} & - {\bf{Similarity score}} for all points against all points are computed. \{\{nl\}\}- The points are then randomly placed on 2- or 3-dimensional space. \{\{nl\}\}- Using an {\bf{optimization}} method, points are moved step by step based on the similarity score until convergence is achieved \tn 
% Row Count 9 (+ 9)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{5.377cm}}{Retains resolution for close clusters, while scaling the farther clusters to fit in frame.} \tn 
% Row Count 11 (+ 2)
% Row 2
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\mymulticolumn{2}{x{5.377cm}}{Used in combination with PCA.} \tn 
% Row Count 12 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
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\par\addvspace{1.3em}

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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{K-Means Clustering}}  \tn
% Row 0
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\mymulticolumn{2}{x{5.377cm}}{- choose no of clusters (k) \{\{nl\}\} - randomly initialise k cluster centroids \{\{nl\}\} - calculate distance between each data point and each centroid (euclidean or manhattan distance) \{\{nl\}\} - assign data point to cluster whose centroid is closest \{\{nl\}\} - recalculate centroid by taking mean of all data points assigned to the cluster \{\{nl\}\} - iterate until stopping criteria met: \{\{nl\}\}  1. max no of iterations reached  \{\{nl\}\} 2. centroids no longer change significantly} \tn 
% Row Count 10 (+ 10)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
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\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

\end{document}