Tweak formatting and order of some slides

presentation.mkdn

@@ -98,31 +98,6 @@ mechanisms, or a diagram for periodic checking. -->
 
 :::
 
-## My thesis topics
-
-<!-- TODO: Needs revision -->
-
-### Topic 1: Immune memory
-Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve
-and memory $\mathsf{CD4}^{+}$ T-cell activation
-
-### Topic 2: Diagnostics for rejection
-Improving array-based diagnostics for transplant rejection by
-optimizing data preprocessing
-
-### Topic 3: Blood profiling during treatment
-Globin-blocking for more effective blood RNA-seq analysis in primate
-animal model for experimental graft rejection treatment
-
-## Today's focus
-
-### \Large Topic 1: Immune memory
-
-\Large
-
-Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve
-and memory $\mathsf{CD4}^{+}$ T-cell activation
-
 ## Memory cells: faster, stronger, and more independent
 
 ![Naïve T-cell activated by APC](graphics/presentation/T-cells-A-SVG.png)
@@ -152,8 +127,6 @@ Compared to naïve cells, memory cells:
 
 Result:
 
-\normalsize
-
 * Memory cells require progressively higher doses of immune suppresive
   drugs
 * Dosage cannot be increased indefinitely without compromising the
@@ -161,12 +134,38 @@ Result:
 
 :::
 
+## 3 problems relating to transplant rejection
+
+### 1. How are memory cells different from naïve?
+
+\onslide<2->{Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve
+and memory $\mathsf{CD4}^{+}$ T-cell activation}
+
+### 2. How can we diagnose rejection noninvasively?
+
+\onslide<3->{Improving array-based diagnostics for transplant rejection by
+optimizing data preprocessing}
+
+### 3. How can we evaluate effects of a rejection treatment?
+
+\onslide<4->{Globin-blocking for more effective blood RNA-seq analysis in primate
+animal model for experimental graft rejection treatment}
+
+## Today's focus
+
+### \Large 1. How are memory cells different from naïve?
+
+\Large
+
+Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve
+and memory $\mathsf{CD4}^{+}$ T-cell activation
+
 ## We need a better understanding of immune memory
 
 * Cell surface markers fairly well-characterized
 
 * But internal mechanisms poorly understood
-  
+
 . . .
 
 \vfill
@@ -177,51 +176,66 @@ Result:
 histone modification is involved in $\mathsf{CD4}^{+}$ T-cell
 activation and memory.
   
-## Experimental design
+## Which histone marks are we looking at?
 
-* Separately isolate naïve and memory $\mathsf{CD4}^{+}$ T-cells from
-  4 donors
-* Activate with CD3/CD28 beads
-* Take samples at 4 time points: Day 0 (pre-activation), Day 1 (early
-  activation), Day 5 (peak activation), and Day 14 (post-activation)
-* Do RNA-seq + ChIP-seq for 3 histone marks (H3K4me2, H3K4me3, &
-  H3K27me3) for each sample.
+. . .
 
-Data generated by Sarah Lamere, published in GEO as
-[GSE73214](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73214)
+::: incremental
 
-## Time points capture phases of immune response
+* **H3K4me3:** "activating" mark associated with active transcription
+
+* **H3K4me2:** Correlated with H3K4me3, hypothesized "poised" state
+
+* **H3K27me3:** "repressive" mark associated with inactive genes
+
+:::
+
+. . .
+
+\vfill
+
+All involved in T-cell differentiation, but activation dynamics
+unexplored
+
+## ChIP-seq measures DNA bound to marked histones[^chipseq]
 
 \centering
 
-![](graphics/presentation/immune-response.png)<!-- { height=75% } -->
+![](graphics/presentation/NRG-chipseq.png){ height=70% }
+
+[^chipseq]: [Furey. "ChIP-seq and beyond: New and improved methodologies to detect and characterize protein-DNA interactions". In: Nature Reviews Genetics (2012)](http://www.nature.com/articles/nrg3306)
 
-## Why study these histone marks?
+## Experimental design
 
 ::: incremental
 
-* **H3K4me3:** "activating" mark associated with active transcription
+* Separately isolate naïve and memory $\mathsf{CD4}^{+}$ T-cells from
+  4 donors
 
-* **H3K4me2:** Correlated with H3K4me3, hypothesized as a "poised" state
+* Activate with CD3/CD28 beads
 
-* **H3K27me3:** "repressive" mark associated with inactive 
+* Sample at 4 time points: Day 0 (pre-activation), Day 1 (early
+  activation), Day 5 (peak activation), and Day 14 (post-activation)
 
-* All 3 involved in T-cell differentiation, but activation dynamics
-  unexplored
+* RNA-seq + ChIP-seq of 3 histone marks (H3K4me2, H3K4me3, & H3K27me3)
+  for each sample.
 
 :::
 
-## ChIP-seq sequences DNA bound to marked histones[^chipseq]
+Data generated by Sarah Lamere, published in GEO as
+[GSE73214](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73214)
 
-\centering
+## Time points capture phases of immune response
 
-![](graphics/presentation/NRG-chipseq.png){ height=70% }
+\centering
 
-[^chipseq]: [Furey. "ChIP-seq and beyond: New and improved methodologies to detect and characterize protein-DNA interactions". In: Nature Reviews Genetics (2012)](http://www.nature.com/articles/nrg3306)
+![](graphics/presentation/immune-response.png)
 
 ## A few intermediate analysis steps are required
 
-![Flowchart of workflow for data analysis](graphics/CD4-csaw/rulegraphs/rulegraph-all-RASTER100.png)
+\centering
+
+![](graphics/CD4-csaw/rulegraphs/rulegraph-all-RASTER100.png)
 
 ## Histone modifications occur on consecutive histones
 
@@ -231,15 +245,13 @@ Data generated by Sarah Lamere, published in GEO as
 
 ## Histone modifications occur on consecutive histones
 
-![Strand cross-correlation plots](graphics/presentation/CCF-plots-A-SVG.png)
-
-## Histone modifications occur on consecutive histones
-
-![Strand cross-correlation plots](graphics/presentation/CCF-plots-B-SVG.png)
-
-## Histone modifications occur on consecutive histones
-
-![Strand cross-correlation plots](graphics/presentation/CCF-plots-C-SVG.png)
+\begin{figure}
+\centering
+\only<1>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/CCF-plots-A-SVG.png}}
+\only<2>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/CCF-plots-B-SVG.png}}
+\only<3>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/CCF-plots-C-SVG.png}}
+\caption{Strand cross-correlation plots show histone-sized wave pattern}
+\end{figure}
 
 ## SICER identifies enriched regions across the genome
 
@@ -263,23 +275,15 @@ Data generated by Sarah Lamere, published in GEO as
 
 ## Peaks in promoters correlate with gene expression
 
-![Expression distributions of genes with and without promoter peaks](graphics/presentation/FPKM-by-Peak-Violin-Plots-A-SVG.png)
-
-## Peaks in promoters correlate with gene expression
-
-![Expression distributions of genes with and without promoter peaks](graphics/presentation/FPKM-by-Peak-Violin-Plots-B-SVG.png)
-
-## Peaks in promoters correlate with gene expression
-
-![Expression distributions of genes with and without promoter peaks](graphics/presentation/FPKM-by-Peak-Violin-Plots-C-SVG.png)
-
-## Peaks in promoters correlate with gene expression
-
-![Expression distributions of genes with and without promoter peaks](graphics/presentation/FPKM-by-Peak-Violin-Plots-D-SVG.png)
-
-## Peaks in promoters correlate with gene expression
-
-![Expression distributions of genes with and without promoter peaks](graphics/presentation/FPKM-by-Peak-Violin-Plots-Z-SVG.png)
+\begin{figure}
+\centering
+\only<1>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-A-SVG.png}}
+\only<2>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-B-SVG.png}}
+\only<3>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-C-SVG.png}}
+\only<4>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-D-SVG.png}}
+\only<5>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-Z-SVG.png}}
+\caption{Expression distributions of genes with and without promoter peaks}
+\end{figure}
 
 ## The story so far