From 18cada8a178f1f5a1805a68692c6c09220b0ab9d Mon Sep 17 00:00:00 2001
From: Justin Bedo <cu@cua0.org>
Date: Fri, 18 Oct 2024 16:01:43 +1100
Subject: old changes

---
 slides.tex | 62 ++++++++++++++++++++++----------------------------------------
 1 file changed, 22 insertions(+), 40 deletions(-)

(limited to 'slides.tex')

diff --git a/slides.tex b/slides.tex
index c908b50..8961267 100644
--- a/slides.tex
+++ b/slides.tex
@@ -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}
-- 
cgit v1.2.3

