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| -rw-r--r-- | slides.tex | 62 | ||||
| -rw-r--r-- | supervised-pca.png | bin | 0 -> 44656 bytes |
2 files changed, 22 insertions, 40 deletions
@@ -89,17 +89,7 @@ \author{Justin Bed\H{o}} \title{Representation learning of compositional counts: an exploration of deep mutational scanning data} -\date{December 13, 2022} - -% Abstract: - -% Deep mutational scanning data provides important functional information on the % effects of protein variants. Many different aspects of proteins can be assayed, % many different experimental designs are possible, and many different scores are % computed leading to very heterogeneous data that is difficult to integrate. - -% In this talk I will explore a representational learning approach on raw count % data. This technique uses recent methods combining compositional data analysis % with a generalised form of principal component analysis to infer protein % representations without specific knowledge of the experimental design or assay % type. - -% Bio - -% Dr Justin Bedő is the Stafford Fox Centenary Fellow in Bioinformatics and % Computational Biology at the Walter and Eliza Hall Institute. He studied % computer science followed by a PhD in machine learning at the Australian % National University and was awarded his doctorate in 2009. He subsequently % worked as a researcher across both academia and industry at NICTA, IBISC % (Informatique, BioInformatique, Systèmes Complexes) CNRS, and IBM Research on % machine learning methods development and applications to biology before joining % the WEHI in 2016. +\date{July 25, 2023} \begin{document} @@ -204,7 +194,7 @@ \end{tikzpicture} \end{column} \end{columns} - \vspace{10pt} \(\Rightarrow\) Information is given only by the ratios of components and any composition can be normalised to the standard simplex where \(\kappa = 1\) (c.f., dividing by library size). + \vspace{10pt} \(\Rightarrow\) Information is given only by the ratios of components and any composition can be normalised to the standard simplex where \(\kappa = 1\) (divide by library size). \end{frame} \begin{frame}{Isomorphisms to Euclidean vector spaces} The simplex forms a \(d-1\) dimensional Euclidean vector space @@ -296,7 +286,7 @@ \begin{itemize} \item Zeros still a problem for \ac{clr} as geometric mean is \(0\). - \item[\(\Rightarrow\)] use median as gague function. + \item[\(\Rightarrow\)] use quantile as gague function. \end{itemize} \end{frame} @@ -304,8 +294,12 @@ \begin{frame}{Activation-Induced Deaminase \footfullcite{Gajula2014}} - \begin{tikzpicture}[remember picture,overlay] - \node[scale=0.85] at (page cs:0,0.08){\input{106-samples.tikz}}; + \begin{tikzpicture} + \node at (page cs:-0.7,0.9){\textbf{Bregman}}; + \node at (page cs:0.3,0.9){\textbf{+1-log + \ac{pca}}}; + \node[scale=0.8] at (page cs:-0.5,0.08){\input{106-samples.tikz}}; + \node[scale=0.8] at (page cs:0.5,0.08){\input{106-samples-log.tikz}}; \end{tikzpicture} \end{frame} @@ -333,16 +327,6 @@ \end{tikzpicture} \end{frame} - \begin{frame}{Activation-Induced Deaminase} - \begin{tikzpicture} - \node at (page cs:-0.7,0.9){\textbf{Bregman}}; - \node at (page cs:0.3,0.9){\textbf{+1-log - \ac{pca}}}; - \node[scale=0.9] at (page cs:-0.5,0.08){\input{106-samples.tikz}}; - \node[scale=0.9] at (page cs:0.5,0.08){\input{106-samples-log.tikz}}; - \end{tikzpicture} - \end{frame} - \begin{frame}{\textsc{Erbb2} \footfullcite{Elazar2016}} \begin{tikzpicture} @@ -362,21 +346,15 @@ \end{frame} \begin{frame}{\textsc{Brca1}: Positional effects} - \begin{columns}[T] - \begin{column}{.4 - \textwidth} - \vspace{1cm} - \[\V\A+\U^\intercal\Q\PP \] - where \(\U \in \R^n\), \(\Q \in \R^l\), \(\PP \in \mathbb{2}^{l\times d}\) - \end{column} - \hfill - \begin{column}{.58 - \textwidth} - \begin{tikzpicture} - \node[scale=.45]{\input{position.tikz}}; - \end{tikzpicture} - \end{column} - \end{columns} + \centering + \begin{tikzpicture} + \node[scale=.45]{\input{position.tikz}}; + \end{tikzpicture} + \end{frame} + + \begin{frame}{\textsc{Brca1}: Supervision} + \centering + \includegraphics[width=.7\linewidth]{supervised-pca.png} \end{frame} \begin{frame}{Acknowledgements} @@ -400,4 +378,8 @@ \end{columns} \end{frame} + \begin{frame}[standout] + Thank you! + \end{frame} + \end{document} diff --git a/supervised-pca.png b/supervised-pca.png Binary files differnew file mode 100644 index 0000000..4081d20 --- /dev/null +++ b/supervised-pca.png |
