Add list of figures

Author: klmr

appendix.tex

@@ -24,7 +24,7 @@ \chapter{Supplementary material for \texorpdfstring{\cref*{sec:trna}}{chapter 2}
 \thispagestyle{empty}
 \textfig{trna-workflow}{spill}{\textwidth}
     {Workflow of the genome-wide identification and analysis of protein-coding
-    and \trna genes.}
+    and \abbr{trna} genes.}
     {(A) \rnaseq analysis of protein-coding gene expression, differential
     expression analysis and codon usage analysis. (B) \chipseq analysis of \pol3
     occupancy at \trna gene loci, differential expression analysis of \trna
@@ -33,29 +33,29 @@ \chapter{Supplementary material for \texorpdfstring{\cref*{sec:trna}}{chapter 2}
 \input{./data/expressed-trnas.tex}
 
 \textfig{mrna-heatmap}{spill}{\textwidth}
-    {Hierarchical clustering of \mrna gene expression correlations.}
+    {Hierarchical clustering of \abbr{mrna} gene expression correlations.}
     {The heatmap shows the Spearman correlations of \mrna gene expression values,
     representing the same data as \cref{fig:mrna-pca}. The samples cluster
     hierarchically by tissue, followed by developmental stage.}
 
 \textfig{trna-heatmap}{spill}{\textwidth}
-    {Hierarchical clustering of \trna gene expression correlations.}
+    {Hierarchical clustering of \abbr{trna} gene expression correlations.}
     {The heatmap shows the Spearman correlations of \trna gene expression values,
     representing the same data as \cref{fig:trna-pca}. The samples cluster
     hierarchically by tissue, followed by developmental stage, with few
     exceptions.}
 
 \textfig{correlation-plots-rnaseq-pol3}{text}{0.7\textwidth}
-    {Correlation of \rnaseq and \pol3 \chipseq data during mouse liver and brain
-    development.}
+    {Correlation of \abbr{rnaseq} and \abbr{pol3} \abbr{chipseq} data during
+    mouse liver and brain development.}
     {Correlation of (A) protein-coding gene expression across developmental
     stages, (B) \trna gene expression as measured by \pol3 occupancy, (C)
     triplet codon usage in protein-coding genes, (D) \trna anticodon
     isoacceptor, (E) amino acid usage of protein-coding genes and (F) \trna
     amino acid isotype.}
 
 \textfig{pca-all-stages}{spill}{\textwidth}
-    {Early developmental stage-specific \trna genes are lowly expressed.}
+    {Early developmental stage-specific \abbr{trna} genes are lowly expressed.}
     {(A) Factorial map of the \pca of \pol3 occupied \trna gene expression
     levels in liver (red), brain (yellow), embryonic body without head (light
     red) and head (light yellow) of stage E12.5, as well as whole E9.5 embryo
@@ -70,15 +70,16 @@ \chapter{Supplementary material for \texorpdfstring{\cref*{sec:trna}}{chapter 2}
     are specific for the embryonic stage (“specific”).}
 
 \textfig{codon-usage-mrna}{spill}{\textwidth}
-    {Observed codon usage in \mrna transcriptomes of developing mouse liver.}
+    {Observed codon usage in \abbr{mrna} transcriptomes of developing mouse
+    liver.}
     {Proportional frequencies (\rcu) weighted by transcript expression are shown
     for triplet codons ordered by amino acid as a bar plot, where grey shading
     is by triplet codon. Data is obtained from liver \rnaseq data of all \num{6}
     developmental stages.}
 
 \textfig{anticodon-abundance-trna}{spill}{\textwidth}
-    {Observed anticodon abundance of \trna isoacceptors of developing mouse
-    liver.}
+    {Observed anticodon abundance of \abbr{trna} isoacceptors of developing
+    mouse liver.}
     {Proportional frequencies weighted by \trna gene expression (\raa) are shown
     for anticodon isoacceptors ordered by amino acid isotype as a bar plot,
     where grey shading is by anticodon. Data is obtained from liver \pol3
@@ -110,8 +111,8 @@ \chapter{Supplementary material for \texorpdfstring{\cref*{sec:trna}}{chapter 2}
     left panels.}
 
 \textfig{rnaseq-pol3-aa-usage-brain}{spill}{\textwidth}
-    {\mrna codon usage and \pol3 occupancy of \trna isotypes in developing mouse
-    brain tissue.}
+    {\abbr{mrna} codon usage and \abbr{pol3} occupancy of \abbr{trna} isotypes
+    in developing mouse brain tissue.}
     {Proportional frequency weighted by transcript expression of (A) arginine
     triplet codons, (B) amino acids, (C) \pol3 binding of arginine isoacceptors
     and (D) \pol3 binding of amino acid isotypes. Grey shading is by triplet
@@ -136,20 +137,21 @@ \chapter{Supplementary material for \texorpdfstring{\cref*{sec:trna}}{chapter 2}
     highly and (iii) lowly expressed protein-coding gene sets.}
 
 \textfig{transcriptomic-pol3-codon-usage}{spill}{\textwidth}
-    {Transcriptomic \mrna codon usage and \pol3 binding to \trna isoacceptors
-    correlate in developing mouse liver and brain.}
+    {Transcriptomic \abbr{mrna} codon usage and \abbr{pol3} binding to
+    \abbr{trna} isoacceptors correlate in developing mouse liver and brain.}
     {Plots show correlation of proportional \pol3 binding to \trna isoacceptors
     (\(x\)-axis) and transcriptomic codon frequencies weighted by expression
     obtained from \rnaseq data (\(y\)-axis). Correlation plots for developing
     liver (A–F) and brain (G–L) are shown. Indexed box in top left indicates
     developmental stage. Grey dots represent degenerated codons. Spearman’s rank
-    correlation coefficients (\(\rho\)) are reported along with their \(p\)-values
-    (\(p\)) in bottom right of each panel.}
+    correlation coefficients (\(\rho\)) are reported along with their
+    \(p\)-values (\(p\)) in bottom right of each panel.}
 
 \thispagestyle{empty}
 \textfig{codon-anticodon-correlation-with-wobble-only-missing}{spill}{\textwidth}
-    {Transcriptomic \mrna codon usage and wobble corrected \pol3 binding to
-    \trna isoacceptors correlate in developing mouse liver and brain.}
+    {Transcriptomic \abbr{mrna} codon usage and wobble corrected \abbr{pol3}
+    binding to \abbr{trna} isoacceptors correlate in developing mouse liver and
+    brain.}
     {Plots show correlation of proportional \pol3 binding to \trna isoacceptors
     corrected according to wobble pairing (\(x\)-axis) and transcriptomic codon
     frequencies weighted by expression obtained from \rnaseq data (\(y\)-axis).
@@ -160,8 +162,8 @@ \chapter{Supplementary material for \texorpdfstring{\cref*{sec:trna}}{chapter 2}
 
 \thispagestyle{empty}
 \textfig{transcriptomic-pol3-aa}{spill}{\textwidth}
-    {Transcriptomic \mrna amino acid usage and \pol3 binding to \trna isotypes
-    correlate in developing mouse liver and brain.}
+    {Transcriptomic \abbr{mrna} amino acid usage and \abbr{pol3} binding to
+    \abbr{trna} isotypes correlate in developing mouse liver and brain.}
     {Plots show correlation of \pol3 binding to \trna isotypes (\(x\)-axis) and
     transcriptomic amino acid frequencies weighted by expression obtained from
     \rnaseq data (\(y\)-axis). Correlation plots for developing liver (A–F) and
@@ -175,7 +177,7 @@ \chapter{Supplementary material for \texorpdfstring{\cref*{sec:trna}}{chapter 2}
 \input{./data/meme-hits.tex}
 
 \textfig{colocalisation-e155-p22}{spill}{\textwidth}
-    {Differentially expressed \trna genes show no colocalisation with
+    {Differentially expressed \abbr{trna} genes show no colocalisation with
     differentially expressed protein-coding genes.}
     {In each plot, the blue line is the cumulative distribution of the ratio of
     the number of upregulated \mrna genes to the number of all \mrna genes in
@@ -207,10 +209,10 @@ \chapter{Supplementary material for \texorpdfstring{\cref*{sec:pol3}}{chapter 4}
 
 \textfig{pol3-inputs}{spill}{\textwidth}
     {Input library coverage}
-    {of different features in six stages of development in liver. The analysis was
-    performed under the assumption that different features have similar amount
-    of input binding (normalised for feature length). As we can see here, this
-    is not quite the case.}
+    {of different features in six stages of development in liver. The analysis
+    was performed under the assumption that different features have similar
+    amount of input binding (normalised for feature length). As we can see here,
+    this is not quite the case.}
 
 \textfig{sine-summary-1}{spill}{\textwidth}
     {\abbr{transsine} binding by \abbr{pol3} across development in liver and

chip-seq.tex

@@ -33,7 +33,7 @@ \subsection{\abbr{chipseq} is a \abbr{dna} binding assay}
 (\cref{fig:chip-seq-workflow}) \citep{Park:2009}.
 
 \textfig{chip-seq-workflow}{body}{0.75\textwidth}
-    {Typical \chipseq workflow.}
+    {Typical \abbr{chipseq} workflow.}
     {Starting from a sample with a target protein bound to \dna (\num{1}),
     the protein is covalently cross-linked to the \dna (mainly with
     formaldehyde, \num{2}). Next, the \dna is fragmented (mainly via sonication,
@@ -80,7 +80,7 @@ \subsection{Quantifying expression of \abbr{trna} genes}
 appropriate regions (\cref{fig:trna-pol3-map-ambiguous-reads}).
 
 \textfig{trna-pol3-binding-profile}{body}{\textwidth}
-    {\trna \pol3 \chip binding profile.}
+    {\abbr{trna} \abbr{pol3} \abbr{chip} binding profile.}
     {The shaded, bell-shaped area shows an idealised binding profile of \chipseq
     data spanning the \trna gene with the A and B box highlighted, as well as
     its flanking regions upstream and downstream of the gene body. This overlap
@@ -123,7 +123,7 @@ \subsection{Quantifying expression of \abbr{trna} genes}
             flanking regions to extrapolate most likely mapping positions for
             ambiguous reads.}
     \endgroup}
-    {Mapping ambiguous \chip reads.}
+    {Mapping ambiguous \abbr{chip} reads.}
     {\chip reads originating from \trna genes can often not be mapped
     unambiguously to any given \trna. Instead, information form the gene’s
     flanking regions is used to determine the more likely provenance.}

codons.tex

@@ -135,7 +135,7 @@ \subsection{Codon usage differs between genes involved in cell proliferation and
             \end{tabu}
         \end{minipage}
     }
-    {\pca of mean \go term codon usage.}
+    {\abbr{pca} of mean \abbr{go} term codon usage.}
     {Each dot corresponds to one \go term. The position of the dots is derived
     by rotating the matrix of the mean codon usage of the genes belonging to
     each \go term. Created using methods of \citet{Gingold:2014} (the precise
@@ -206,8 +206,8 @@ \subsection{Differential codon usage in cell-condition specific gene sets
         \includegraphics[width=\textwidth]{gingold-3c-mrna}\\[-0.3\baselineskip]
         \subcaption{\label{fig:gingold-3c-b}Fold change in \abbr{mrna} level}
     \endgroup}
-    {Projection of the \trna and \mrna expression changes on the codon usage
-    map for cells after induced differentiation via retinoic acid.}
+    {Projection of the \abbr{trna} and \abbr{mrna} expression changes on the
+    codon usage map for cells after induced differentiation via retinoic acid.}
     {Both panels show the same \pca that I reproduced in \cref{fig:go-cub-pca}.
     In the top panel, the colours correspond to the fold change in predicted
     translation efficiency of the genes constituting each \go term, given the
@@ -279,7 +279,7 @@ \subsection{The effect of gene set size on codon usage bias}
 explained by random variation alone.
 
 \textfig{significant-go-term-cub-var}{spill}{\textwidth}
-    {\go term codon usage variation.}
+    {\abbr{go} term codon usage variation.}
     {Each point corresponds to a \go term whose Pearson correlation of codon
     usage with the genomic background is plotted against its gene set size.
     \go terms with an \fdr-adjusted \(p < 0.05\) are coloured in red.}
@@ -468,12 +468,12 @@ \subsection{Other genomic features may drive the perceived codon usage bias}
 categories (\cref{fig:go-rcu-pca}).
 
 \textfigtwo{go-aa-pca}
-    {\pca of mean \go term amino acid usage.}
+    {\abbr{pca} of mean \abbr{go} term amino acid usage.}
     {Each dot corresponds to one \go term. The position of the dots is derived
     by rotating the matrix of the mean amino acid usage of the genes belonging
     to each \go term. Colours and symbols as in \cref{fig:go-cub-pca}.}
     {go-rcu-pca}
-    {\pca of mean \go term \rcu.}
+    {\abbr{pca} of mean \abbr{go} term \abbr{rcu}.}
     {Each dot corresponds to one \go term. The position of the dots is derived
     by rotating the matrix of the mean \rcu of the genes belonging to each \go
     term. Colours and symbols as in \cref{fig:go-cub-pca}.}
@@ -517,17 +517,17 @@ \subsection{Other genomic features may drive the perceived codon usage bias}
     {%
         \begin{minipage}{0.5\textwidth}
             \includegraphics[width=\textwidth]{cub-pc1-vs-gc}%
-            \subcaption{\label{fig:cu-vs-gc}\gc bias against codon usage
+            \subcaption{\label{fig:cu-vs-gc}\abbr{gc} bias against codon usage
             (Pearson’s \(\rho = -0.96\)).}
         \end{minipage}%
         \begin{minipage}{0.5\textwidth}
             \includegraphics[width=\textwidth]{aa-pc2-vs-gc}%
-            \subcaption{\label{fig:aa-vs-gc}\gc bias against amino acid usage
-            (Pearson’s \(\rho = -0.58\)).}
+            \subcaption{\label{fig:aa-vs-gc}\abbr{gc} bias against amino acid
+            usage (Pearson’s \(\rho = -0.58\)).}
         \end{minipage}%
     }
-    {\gc bias against a principal component of the \go term codon \& amino acid
-    usage \pca.}
+    {\abbr{gc} bias against a principal component of the \abbr{go} term codon \&
+    amino acid usage \abbr{pca}.}
     {Each point represents one \go term. The \(x\) coordinate corresponds to
     that of the named principal component of the \pca in
     \cref{fig:go-cub-pca,fig:go-aa-pca}. The \(y\) coordinate corresponds to

data/GO_m_phase.tex

@@ -1,5 +1,5 @@
 \tabcapof{go-m-phase}
-    {Gene identifiers for GO term “M phase of mitotic cell cycle”.}{}
+    {Gene identifiers for \abbr{go} term “M phase of mitotic cell cycle”.}{}
 
 \begin{longtable}{lll}
     \toprule

data/GO_psp.tex

@@ -1,5 +1,5 @@
 \tabcapof{go-psp}
-    {Gene identifiers for GO term “M phase of mitotic cell cycle”.}{}
+    {Gene identifiers for \abbr{go} term “M phase of mitotic cell cycle”.}{}
 
 \begin{longtable}{lll}
     \toprule

rna-seq.tex

@@ -95,7 +95,7 @@ \subsubsection{\abbr{rnaseq} sample preparation}
 sequencing machine (\cref{fig:rna-seq-workflow}).
 
 \textfig{rna-seq-workflow}{body}{0.75\textwidth}
-    {Typical \rnaseq workflow.}
+    {Typical \abbr{rnaseq} workflow.}
     {Starting from a total \rna sample (\num{1}), the \rna of interest is
     enriched either via poly(A) selection or \rrna depletion (\num{2}). The
     enriched fraction is fragmented and size-selected (\num{3}). The fragments

thesis.cls

@@ -301,8 +301,8 @@
 
 \let\labelfont\textbf
 
-\newcommand*\figcap[3]{\caption{\label{fig:#1}\labelfont{#2} #3}}
-\newcommand*\figcapof[3]{\captionof{figure}{\label{fig:#1}\labelfont{#2} #3}}
+\newcommand*\figcap[3]{\caption[#2]{\label{fig:#1}\labelfont{#2} #3}}
+\newcommand*\figcapof[3]{\captionof{figure}[#2]{\label{fig:#1}\labelfont{#2} #3}}
 
 \newcommand*\tabcap[3]{%
     \begingroup%

thesis.tex

@@ -52,6 +52,10 @@
     \hypersetup{linkcolor=thesis@toccolor}
     \tableofcontents
     \listofabbrevs
+    \cleardoublepage
+    \phantomsection
+    \addcontentsline{toc}{chapter}{\listfigurename}
+    \listoffigures
 \endgroup
 
 \chapterfile{summary}