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Better citations and other misc fixes in slides

Ryan C. Thompson 5 lat temu
rodzic
commit
2552d900e6
1 zmienionych plików z 17 dodań i 7 usunięć
  1. 17 7
      presentation.mkdn

+ 17 - 7
presentation.mkdn

@@ -23,13 +23,13 @@ fontsize: 14pt
 
 :::
 
-[^1]: Source: https://www.organdonor.gov/statistics-stories/statistics.html
+[^1]: [organdonor.gov](https://www.organdonor.gov/statistics-stories/statistics.html)
 
 ## Organ transplants are a life-saving treatment
 
 ![Organ donation statistics for the USA in 2018[^2]](graphics/presentation/transplants-organ-CROP-RASTER.png){ height=70% }
 
-[^2]: Source: https://www.organdonor.gov/statistics-stories/statistics.html
+[^2]: [organdonor.gov](https://www.organdonor.gov/statistics-stories/statistics.html)
 
 ## Graft rejection is an adaptive immune response
 
@@ -54,7 +54,7 @@ fontsize: 14pt
 
 ![Kidney allograft survival rates in children by transplant year[^3]](graphics/presentation/kidney-graft-survival.png){ height=65% }
 
-[^3]: Kim & Marks. "Long-term outcomes of children after solid organ transplantation". In: Clinics (2014)
+[^3]:[ Kim & Marks. "Long-term outcomes of children after solid organ transplantation". In: Clinics (2014)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3884158/?report=classic)
 
 ## Rejection is treated with immune suppressive drugs
 
@@ -90,7 +90,7 @@ animal model for experimental graft rejection treatment
 
 ## Today's focus
 
-### Chapter 2
+### \Large Topic 1: Immune memory
 
 \Large
 
@@ -105,6 +105,8 @@ and memory $\mathsf{CD4}^{+}$ T-cell activation
 
 ## Memory cells are a problem for immune suppression
 
+<!-- Need graphics -->
+
 \large
 
 Compared to naïve cells, memory cells:
@@ -118,7 +120,7 @@ Compared to naïve cells, memory cells:
   naïve cells
 * evolve over time to respond even more strongly to their antigen
 
-. . .
+## Memory cells are a problem for immune suppression
 
 \large
 
@@ -177,10 +179,18 @@ Data generated by Sarah Lamere, published in GEO as
 
 ## Finding enriched regions across the genome
 
+<!-- Graphic from SICER paper: Fig1 -->
+<!-- Use one of my IDR example plots for IDR -->
+
 * Scan across the genome looking for regions with read coverage above
-  background level in each donor using SICER peak caller
+  background level in each donor using SICER peak caller[^6]
 * Use Irreducible Discovery Rate framework to identify peaks that are
-  called consistently across multiple donors
+  called consistently across multiple donors[^7]
+
+[^6]: [Chongzhi Zang et al. “A clustering approach for identification of enriched domains from histone modification ChIP-Seq data”. In: Bioinformatics 25.15 (2009) doi: 10.1093/bioinformatics/btp340.](https://doi.org/10.1093/bioinformatics/btp340)
+[^7]: [Qunhua Li et al. “Measuring reproducibility of high-throughput experiments”. In: AOAS (2011)](https://doi.org/10.1214/11-AOAS466)
+
+## Finding enriched regions across the genome
 
 ![Peak-calling summary statistics](graphics/presentation/RCT-thesis-table2.2-SVG-CROP.png)