Bulk_RNA_seq_Report.Rmd

---
author: "Owen Hoare"
date: "`r Sys.Date()`"
output:
  html_document:
    theme: cerulean
    toc: TRUE
    toc_float: TRUE
    toc_depth: 6
    fig_width: 12
    fig_height: 8
    fig_caption: true
    code_folding: hide
  pdf_document: default
---

---
title: `r paste("Measles and Toca Virus Evaluation - Bulk RNA_seq",  sep ="<BR>")`
---


### Run informations {.tabset .tabset-fade}

#### Run
<center>
```{r runInfo_run}
library( pander)
pander( c( 'Date'     = as.character( Sys.Date()),
           'Experiment'   = EXPERIMENT_NAME));
```
</center>

#### Parameters
```{r runInfo_params}
listParams = as.list( paramsEnv);
pander(listParams[order(names(listParams))]); # Show params in alphabetical order
```

</center>

#### R
```{r runInfo_r}
pander( sessionInfo());
```


```{r, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::read_chunk(path = file.path( WORKING_DIR, "DEGSeq2.R"))
```

```{r eval=T, error=TRUE, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Load_libraries>>
```

[//]: # "Load the data"
```{r dataLoadpreProcess, fig.dim = c(15,10), echo=FALSE, message=FALSE, warning=FALSE}
<<loadData>>
```
<br /><br />



### CIBERSORTx Analysis and Visualization SingleR {.tabset .tabset-fade}
Let's take a look at CIBERSORTx deconvolution on our mouse PNOC bulk RNA-seq data using 
our scRNA-seq as our reference to do the cell type classification. This was run in 
relative cell fraction mode.
```{r CIBERSORTx, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<CIBERSORTx_PCA>>
```
<br />

#### Basic Heatmap Matrix SingleR
CIBERSORTx Heatmap SingleR
```{r CIBERSORTx_heatmap,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<CIBERSORTx_Heatmap>>
```
<br />

#### Heatmap with correlation SingleR
CIBERSORTx Correlation Heatmap SingleR
```{r CIBERSORTx_correlation_heatmap,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<CIBERSORTx_Correlation_Heatmap>>
```
<br />

#### Dotplot with correlation and p-values SingleR
CIBERSORTx Correlation dotplots with pvalues SingleR
```{r CIBERSORTx_correlation_heatmap_pvalues,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<CIBERSORTx_Correlation_Heatmap_pvalues>>
```
<br />

#### CIBERSORTx cell fraction summary SingleR
CIBERSORTx cell fraction results SingleR
```{r CIBERSORTx_cell_fraction_Result,  message=FALSE, fig.dim = c(30,20), warning=FALSE, autodep=T, include=T, results='asis'}
<<CIBERSORTx_cell_fraction_results>>
```
<br />

#### CIBERSORTx cell fraction summary barplot SingleR
CIBERSORTx cell fraction barplot SingleR
```{r CIBERSORTx_cell_fraction_barplots,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis', fig.dim = c(15,10)}
<<CIBERSORTx_cell_fraction_barplot>>
```
<br />

#### CIBERSORTx cell fraction boxplots SingleR
CIBERSORTx cell fraction boxplots SingleR
```{r CIBERSORTx_cell_fraction_boxplots,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis', fig.dim = c(20,10)}
<<CIBERSORTx_cell_fraction_boxplot>>
```
<br />

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 



### CIBERSORTx Analysis and Visualization Virus {.tabset .tabset-fade}
Now let's do the same thing but for the viral cell fraction.
```{r CIBERSORTx2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<CIBERSORTx_PCA2>>
```
<br />

#### Basic Heatmap Matrix Virus
CIBERSORTx Heatmap Virus
```{r CIBERSORTx_heatmap2,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<CIBERSORTx_Heatmap2>>
```
<br />

#### Heatmap with correlation Virus
CIBERSORTx Correlation Heatmap Virus  
```{r CIBERSORTx_correlation_heatmap2,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<CIBERSORTx_Correlation_Heatmap2>>
```
<br />

#### Dotplot with correlation and p-values Virus
CIBERSORTx Correlation dotplots with pvalues Virus
```{r CIBERSORTx_correlation_heatmap_pvalues2,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<CIBERSORTx_Correlation_Heatmap_pvalues2>>
```
<br />

#### CIBERSORTx cell fraction summary Virus
CIBERSORTx cell fraction results Virus
```{r CIBERSORTx_cell_fraction_Result_Virus,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<CIBERSORTx_cell_fraction_results2>>
```
<br />

#### CIBERSORTx cell fraction summary barplot Virus
CIBERSORTx cell fraction barplot Virus
```{r CIBERSORTx_cell_fraction_barplots2,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis', fig.dim = c(15,10)}
<<CIBERSORTx_cell_fraction_barplot2>>
```
<br />

#### CIBERSORTx cell fraction boxplots Virus
CIBERSORTx cell fraction boxplots Virus
```{r CIBERSORTx_cell_fraction_boxplots2,  message=FALSE, warning=FALSE, autodep=T, include=T, results='asis', fig.dim = c(15,10)}
<<CIBERSORTx_cell_fraction_boxplot2>>
```
<br />

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 



```{r  Prepare_Data, eval=T, error=TRUE, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<PrepareData>>
```

### Measles Control vs Measles Virus Post Treatment {.tabset .tabset-fade}
Let's perform differential expression tests for each contrast using DESeq2

```{r Differential_Expression, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Run_DESeq2>>
cat('\n', '<br>', '\n\n')
```


```{r EmptyDT_initialising_datatable_in_for_loop, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
datatable(CvMT,                        
                                        class = "compact",
                                        filter="top",
                                        rownames = FALSE,
                                        colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj"),
                                        extensions = c('Buttons'),
                                        options = list(
                                          pageLength = 15,
                                          dom = 'Bfrtip',
                                          buttons = c('excel','csv','pdf','copy')
           ))

# this empty datatable is necessary to allow the printing of datatables in the loop (next chunk)
```


```{r MarkerGenes_datatables}
<<Print_Tables>>
```


#### Measles Control vs Heat Inactivated MV


```{r Differential_Expression1, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Contrast1>>
cat('\n', '<br>', '\n\n')
```


```{r EmptyDT_initialising_datatable_in_for_loop1, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
datatable(CvMHI,                        
                                        class = "compact",
                                        filter="top",
                                        rownames = FALSE,
                                        colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
                                        extensions = c('Buttons'),
                                        options = list(
                                          pageLength = 15,
                                          dom = 'Bfrtip',
                                          buttons = c('excel','csv','pdf','copy')
           ))

# this empty datatable is necessary to allow the printing of datatables in the loop (next chunk)
```


```{r MarkerGenes_datatables1}
<<Print_Tables1>>
```

#### Measles Control vs Mid treatment MV


```{r Differential_Expression2, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Contrast2>>
cat('\n', '<br>', '\n\n')
```


```{r EmptyDT_initialising_datatable_in_for_loop2, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
datatable(CvM_MT,                        
                                        class = "compact",
                                        filter="top",
                                        rownames = FALSE,
                                        colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
                                        extensions = c('Buttons'),
                                        options = list(
                                          pageLength = 15,
                                          dom = 'Bfrtip',
                                          buttons = c('excel','csv','pdf','copy')
           ))

# this empty datatable is necessary to allow the printing of datatables in the loop (next chunk)
```


```{r MarkerGenes_datatables2}
<<Print_Tables2>>
```



#### Toca Control vs Post treatment Toca


```{r Differential_Expression3, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Contrast3>>
cat('\n', '<br>', '\n\n')
```


```{r EmptyDT_initialising_datatable_in_for_loop3, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
datatable(CvPTT,                        
                                        class = "compact",
                                        filter="top",
                                        rownames = FALSE,
                                        colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
                                        extensions = c('Buttons'),
                                        options = list(
                                          pageLength = 15,
                                          dom = 'Bfrtip',
                                          buttons = c('excel','csv','pdf','copy')
           ))

# this empty datatable is necessary to allow the printing of datatables in the loop (next chunk)
```


```{r MarkerGenes_datatables3}
<<Print_Tables3>>
```


#### Toca Control vs Heat Inactivated Toca


```{r Differential_Expression4, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Contrast4>>
cat('\n', '<br>', '\n\n')
```


```{r EmptyDT_initialising_datatable_in_for_loop4, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
datatable(CvHIT,                        
                                        class = "compact",
                                        filter="top",
                                        rownames = FALSE,
                                        colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
                                        extensions = c('Buttons'),
                                        options = list(
                                          pageLength = 15,
                                          dom = 'Bfrtip',
                                          buttons = c('excel','csv','pdf','copy')
           ))

# this empty datatable is necessary to allow the printing of datatables in the loop (next chunk)
```


```{r MarkerGenes_datatables4}
<<Print_Tables4>>
```


#### Toca Control vs Mid treatment Toca


```{r Differential_Expression5, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Contrast5>>
cat('\n', '<br>', '\n\n')
```


```{r EmptyDT_initialising_datatable_in_for_loop5, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
datatable(CvMTT,                        
                                        class = "compact",
                                        filter="top",
                                        rownames = FALSE,
                                        colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
                                        extensions = c('Buttons'),
                                        options = list(
                                          pageLength = 15,
                                          dom = 'Bfrtip',
                                          buttons = c('excel','csv','pdf','copy')
           ))

# this empty datatable is necessary to allow the printing of datatables in the loop (next chunk)
```


```{r MarkerGenes_datatables5}
<<Print_Tables5>>
```


Ending the tabset section
### {.toc-ignore}

<br /><br /><br /><br />

### DEGSeq2 Analysis and Visualization MV {.tabset .tabset-fade}
Let's take a look at some differential gene expression analysis using the DESeq2 package
and I will set it using the likelihood ration option to compare all 8 contrasts.
Then I will interrogate the biology that underpins these genes.
```{r DEGSeq2_Analysis_Run, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<DEGSeq2_Analysis>>
```
<br />


#### Dispersion Plots
Dispersion Plot
```{r Dispersion_Plot, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<DispersionPlot>>
```
<br />

#### MA Plots Control vs MV Post Treatment  
MAPlots Control vs MV Post Treatment  
```{r MA_Plots, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<MAPlots>>
```
<br />

#### MA Plots Control vs MV Post Treatment Significant Genes
MA Plots Control vs MV Post Treatment Most Significant Genes
```{r MAPlots_Significant_Gene, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<MAPlots_Significant_Genes>>
```
<br />

#### Volcano Plots Control vs MV Post Treatment Significant Genes
Volcano Plots Control vs MV Post Treatment Most Significant Genes
```{r Volcano_Plot,  message=FALSE, fig.dim = c(25,20), warning=FALSE, autodep=T, include=T, results='asis'}
<<VolcanoPlot>>
```
<br />

#### Count Plot for Top Marker Gene
Volcano Plots Control vs MV Post Treatment Most Significant Genes
```{r PlotCounts,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<PlotCounts>>
```
<br />

#### Heatmap Control vs MV Post Treatment Most Significant Genes
Heatmap Control vs MV Post Treatment Top Genes (2FC padj < 0.05)
```{r Heatmap_Top_Genes,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<HeatmapTopGenes>>
```
<br />

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 


### GSEA and Pathway Analysis MV {.tabset .tabset-fade}
Here I will examine the pathways associated with the differentially expressed genes comparing the
control measles virus samples vs post treatment (20 days after infection) with measles virus.
```{r GO_Pathway_Enrichment_MV, fig.dim = c(15,10), warning=FALSE, message=FALSE, echo=FALSE, results='asis'}
<<GO_Pathway_Analysis_MV>>
```
<br />

#### Enrichment Plots Control vs MV Post Treatment  
Enrichment Plot MV
```{r E_MapPlot_MV, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<EMapPlot_MV>>
```
<br />

#### cMap Plots Control vs MV Post Treatment 
cMap Plot Control vs MV Post Treatment 
```{r c_MapPlot_MV, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<cMapPlot_MV>>
```
<br />

#### RidgePlot Control vs MV Post Treatment
Ridge Plot Control vs MV Post Treatment
```{r RidglePlotMV, fig.dim = c(15,15), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<RidglePlot_MV>>
```
<br />

#### GSEA TopPathway MV
GSEA Top Pathway MV
```{r GSEATopPathway_MV,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<GSEA_TopPathway_MV>>
```
<br />

#### Total Publised Papers on this Pathway
Total Publised Papers on this Pathway Since 2010
```{r Published_Papers, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Published_Articles_MV>>
```
<br />


```{r KEGGPaths, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<KEGG_Pathways>>
```
<br />

#### KEGG TopPathway MV
KEGG Top Pathway MV
```{r KEGG_Pathways_MV,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<KEGG_TopPathway_MV>>
```
<br />

#### KEGG Pathway Enrichment Control vs MV Post Treatment
KEGG Pathways Enriched Control vs MV Post Treatment
```{r KEGG_Paths_Enrichment, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<KEGG_Pathway_Enrichment>>
```
<br />


#### cNet KEGG Pathway Enrichment
cNet KEGG Pathway Enrichments
```{r cnet_Pathway_Enrich,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<cnet_KEGG_Pathway_Enrichment>>
```
<br />

#### RidglePlots KEGG Pathway Enrichment
RidglePlots Pathway KEGG Enrichments
```{r cnet_KEGG_Pathway_Enrich,  message=FALSE, fig.dim = c(15,15), warning=FALSE, autodep=T, include=T, results='asis'}
<<RidgePlot_KEGG_Pathway_Enrichment>>
```
<br />

#### Top KEGG Pathway Enrichment
Top KEGG Pathway Enriched
```{r Top_KEGG_Pathway_Enriched,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<Top_KEGG_Pathway_Enrichment>>
```
<br />

#### Full Measles KEGG Pathway
Measles KEGG Pathway
```{r Measles_KEGG_Pathways,  message=FALSE, fig.dim = c(20,15), warning=FALSE, autodep=T, include=T, results='asis'}
<<Measles_KEGG_Pathway>>
knitr::include_graphics("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/Bulk_RNA/01_Input_Data/Mouse/mmu05162.pathview.png")
```
<br />

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 



### DEGSeq2 Analysis and Visualization Toca {.tabset .tabset-fade}
Now I will pull out the differentially expressed genes associated with control vs Toca virus post treatment.
The same logic applies as in my previous analysis. 
```{r DEGSeq2_Analysis_Toca_2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<DEGSeq2_Analysis_Toca2>>
```

#### MA Plots Control vs Toca Post Treatment  
MAPlots Control vs Toca Post Treatment  
```{r MA_Plots2, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<MAPlots2>>
```
<br />

#### MA Plots Control vs Toca Post Treatment Significant Genes
MA Plots Control vs Toca Post Treatment Most Significant Genes
```{r MAPlots_Significant_Gene2, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<MAPlots_Significant_Genes2>>
```
<br />

#### Volcano Plots Control vs MV Post Treatment Significant Genes
Volcano Plots Control vs Toca Post Treatment Most Significant Genes
```{r Volcano_Plot2,  message=FALSE, fig.dim = c(25,20), warning=FALSE, autodep=T, include=T, results='asis'}
<<VolcanoPlot2>>
```
<br />

#### Count Plot for Top Marker Gene
Volcano Plots Control vs Toca Post Treatment Most Significant Genes
```{r PlotCounts2,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<PlotCounts2>>
```
<br />

#### Heatmap Control vs Toca Post Treatment Most Significant Genes
Heatmap Control vs Toca Post Treatment Top Genes (2FC padj < 0.05)
```{r Heatmap_Top_Genes2,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<HeatmapTopGenes2>>
```
<br />

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 


### GSEA and Pathway Analysis Toca {.tabset .tabset-fade}
```{r GO_Pathway_Enrichment_MV2, fig.dim = c(15,10), warning=FALSE, message=FALSE, echo=FALSE, results='asis'}
<<GO_Pathway_Analysis_MV2>>
```
<br />

#### Enrichment Plots Control vs Toca Post Treatment  
Enrichment Plot Toca
```{r E_MapPlot_MV2, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<EMapPlot_MV2>>
```
<br />

#### cMap Plots Control vs Toca Post Treatment 
cMap Plot Control vs MV Post Treatment 
```{r c_MapPlot_MV2, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<cMapPlot_MV2>>
```
<br />

#### RidgePlot Control vs Toca Post Treatment
Ridge Plot Control vs MV Post Treatment
```{r RidglePlotMV2, fig.dim = c(15,15), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<RidglePlot_MV2>>
```
<br />

#### GSEA TopPathway Toca
GSEA Top Pathway Toca
```{r GSEATopPathway_MV2,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<GSEA_TopPathway_MV2>>
```
<br />

#### Total Publised Papers on this Pathway
Total Published Papers on this Pathway Since 2010
```{r Published_Papers2, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Published_Articles_MV2>>
```
<br />


```{r KEGGPaths2, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<KEGG_Pathways2>>
```
<br />

#### KEGG TopPathway Toca
KEGG Top Pathway Toca
```{r KEGG_Pathways_MV2,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<KEGG_TopPathway_MV2>>
```
<br />

#### KEGG Pathway Enrichment Control vs Toca Post Treatment
KEGG Pathways Enriched Control vs Toca Post Treatment
```{r KEGG_Paths_Enrichment2, fig.dim = c(15,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<KEGG_Pathway_Enrichment2>>
```
<br />


#### cNet KEGG Pathway Enrichment Toca
cNet KEGG Pathway Enrichments Toca
```{r cnet_Pathway_Enrich2,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<cnet_KEGG_Pathway_Enrichment2>>
```
<br />

#### RidglePlots KEGG Pathway Enrichment Toca
RidglePlots Pathway KEGG Enrichments Toca
```{r cnet_KEGG_Pathway_Enrich2,  message=FALSE, fig.dim = c(15,15), warning=FALSE, autodep=T, include=T, results='asis'}
<<RidgePlot_KEGG_Pathway_Enrichment2>>
```
<br />

#### Top KEGG Pathway Enrichment Toca
Top KEGG Pathway Enriched Toca
```{r Top_KEGG_Pathway_Enriched2,  message=FALSE, fig.dim = c(15,10), warning=FALSE, autodep=T, include=T, results='asis'}
<<Top_KEGG_Pathway_Enrichment2>>
```
<br />

#### Full Herpes Simplex Virus KEGG Pathway
Measles KEGG Pathway
```{r Measles_KEGG_Pathways2,  message=FALSE, fig.dim = c(20,15), warning=FALSE, autodep=T, include=T, results='asis'}
<<Measles_KEGG_Pathway2>>
knitr::include_graphics("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/Bulk_RNA/01_Input_Data/Mouse/mmu05168.pathview.png")
```
<br />

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 



### Survival Analysis of Mouse Infected with Measles Virus {.tabset .tabset-fade}
Here I will assess if injecting the mouse with measles or Toca virus has any survival benefit.
I will compare 2 groups with 6 contrasts. The first survival curve will look at measles virus
with control vs heat inactivated virus and subsequently 20 days post treatment. The same comparisons will 
be done for the Toca visus to see which virus works best and promoted prolonged survival. 
```{r SurvivalCurve_MV, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Survival_Analysis_MV_Infected_Virus>>
```

<br />

#### Forest Plot of Mouse Infected with MV Virus
Now I will make a forest plot to summarize my results and see if tumor weight is also a factor
in driving poor prognosis. The results indicate that post viral treatment is an independent prognostic factor.
```{r SurvivalForestMV, fig.dim = c(18,6), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<ForestPlot_MV_Infected_Virus>>
```

<br />

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 

<!-- Ending the tabset section -->
### {.toc-ignore} 
<br /><br /> 


### Survival Analysis of Mouse Infected with Toca Virus {.tabset .tabset-fade}
Now let's do the same analysis but only look at the effected of treating the mice with Toca virus.
```{r SurvivalCurve_Toca, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Survival_Analysis_Toca_Infected_Virus>>
```

<br />

####  Forest Plot of Mouse Infected with Toca Virus
Again I will make a forest plot to summarize my results and see if tumor weight is also a factor
in driving poor prognosis. The results indicate the post viral treatment is an independent prognostic factor.
```{r SurvivalForestToca, fig.dim = c(18,6), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<ForestPlot_Toca_Infected_Virus>>
```

<br />

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<br /><br /> 


### Volcano3D Plots Measels Virus Re-examining differential expression {.tabset .tabset-fade}
The Volcano3D plots adds an extra dimension to our analysis. Here I can perform a differential gene expression analysis using
the same DEGSeq2 package. However, here I can make 3 contrast together. That is control, heat inactivated virus and post treatment.
I will do one set for measeles virus alone and seperately for Toca virus. Here I will exclude mid-treatment as this is the least informative group
given I already did this in my previous analysis. Volcano3D will allow us to pick on some new genes potentially missed by using DESeq2 alone. 
```{r Volcano3DMV, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano3D_MV>>
```

<br />

#### Volcano3D MV Plots 
```{r Volcano3D_MV_DEGPlots, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano3D_MV_DEGPlot>>
```

<br />

#### Volcano3D MV Radial Plots 
```{r Volcano3D_MV_DEGP_RadialPlots, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano3D_MV_DEGP_RadialPlot>>
```

<br />

#### Volcano3D MV Boxplots Top Genes
```{r Volcano3D_MV_Boxplot, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano3D_MV_Boxplots>>
```

<br />

#### Volcano2D MV Top DEG Plots 
```{r Volcano2D_MV_DEGPlot, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano2D_MV_DEGPlots>>
```

<br />

#### Volcano3D MV GO Pathways 
```{r Volcano2D_MV_GOPathway, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano2D_MV_GOPathways>>
```

<br />

#### Volcano3D MV Enrichment Plots
```{r Volcano2D_MV_GO_Enrichments, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano2D_MV_GO_Enrichment>>
```
<br />

#### Volcano3D DEG Measles Virus
Let's perform differential expression tests for each contrast using DESeq2

```{r MeaslesDEGVolcano3D1, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<MeaslesDEGVolcano3D>>
cat('\n', '<br>', '\n\n')
```


```{r EmptyDT_initialising_datatable_in_for_loop6, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
datatable(measles,
                                              class = "compact",
                                              filter="top",
                                              rownames = FALSE,
                                              colnames = c("gene", "Control_MV", "Mid_treatment_MV", "Post_treatment_MV", "x", "y", "r", "angle", "z", "pvalue", "col", "col", "significant"),
                                              extensions = c('Buttons'),
                                              options = list(
                                                pageLength = 15,
                                                dom = 'Bfrtip',
                                                buttons = c('excel','csv','pdf','copy')
                                              ))

# this empty datatable is necessary to allow the printing of datatables in the loop (next chunk)
```


```{r MarkerGenes_datatables6}
<<Print_TablesMV>>
```

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<br /><br /> 


### Volcano3D Plots Toca Virus Re-examining differential expression {.tabset .tabset-fade}
Now I will do the same but this time for Toca virus. Aagin I will use 3 contrasts 
control, heat inactivated and post treatment to make the comparison. Once again I will investigate the 
biology of these different genes and pathways. 
```{r Volcano3DMV2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano3D_MV2>>
```

<br />

#### Volcano3D Toca Plots 
```{r Volcano3D_MV_DEGPlots2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano3D_MV_DEGPlot2>>
```

<br />

#### Volcano3D Toca Radial Plots 
```{r Volcano3D_MV_DEGP_RadialPlots2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano3D_MV_DEGP_RadialPlot2>>
```

<br />

#### Volcano3D Toca Boxplots Top Genes
```{r Volcano3D_MV_Boxplot2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano3D_MV_Boxplots2>>
```

<br />

#### Volcano2D Toca Top DEG Plots 
```{r Volcano2D_MV_DEGPlot2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano2D_MV_DEGPlots2>>
```

<br />

#### Volcano3D Toca GO Pathways 
```{r Volcano2D_MV_GOPathway2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano2D_MV_GOPathways2>>
```

<br />

#### Volcano3D Toca Enrichment Plots
```{r Volcano2D_MV_GO_Enrichments2, fig.dim = c(15,10), echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Volcano2D_MV_GO_Enrichment2>>
```
<br />

#### Volcano3D DEG Toca Virus
Let's perform differential expression tests for each contrast using DESeq2

```{r MeaslesDEGVolcano3D2, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<TocaDEGVolcano3D>>
cat('\n', '<br>', '\n\n')
```


```{r EmptyDT_initialising_datatable_in_for_loop7, message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
datatable(Toca,
                                              class = "compact",
                                              filter="top",
                                              rownames = FALSE,
                                              colnames = c("gene", "Control_Toca", "Mid_treatment_Toca", "Post_treatment_Toca", "x", "y", "r", "angle", "z", "pvalue", "col", "col", "significant"),
                                              extensions = c('Buttons'),
                                              options = list(
                                                pageLength = 15,
                                                dom = 'Bfrtip',
                                                buttons = c('excel','csv','pdf','copy')
                                              ))

# this empty datatable is necessary to allow the printing of datatables in the loop (next chunk)
```


```{r MarkerGenes_datatables7}
<<Print_TablesToca>>
```


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<br /><br /> 


### Interrogating Reasons For Low Viral Uptake {.tabset .tabset-fade}
Here I will look at specific markers of phagocytosis to see if the immune system
is responsible for clearing the virus before it had a chance to infect the cells. If so this may 
hinder the immune response and may help explain the reason for such a low detection rate of the virus.
To test the correlation between cell type fraction from CIBERSORTx and gene expression I will use a 
spearman ranked correlation.

```{r LowViralUptake, echo=FALSE, optipng='-o7', message=FALSE, warning=FALSE}
<<Interrogating_low_viral_uptake>>
```
<br />

#### Correlation Plots of Phagocyte Markers
Correlation Plots of Phagocytes Genes
```{r Interrogating_phagocyte_markers_correlation1, fig.dim = c(25,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Interrogating_phagocyte_markers_correlation>>
```
<br />

#### Boxplots of Phagocyte Markers
Boxplots of Phagocyte Genes
```{r Interrogating_phagocyte_markers_boxplots1, fig.dim = c(15,15), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Interrogating_phagocyte_markers_boxplots>>
```
<br />

#### Correlation Plots of Immunoglobulin and Apoptotic Markers
Correlation Plots of Immunoglobulin and Apoptotic Genes
```{r Interrogating_immunoglobulin_apoptotic_markers_correlation1, fig.dim = c(25,10), message=FALSE, warning=FALSE, autodep=T, include=T, results='asis'}
<<Interrogating_immunoglobulin_apoptotic_markers_correlation>>
```
<br />

#### Boxplots of Immunoglobulin and Apoptotic Markers
Boxplots of Immunoglobulin and Apoptotic Genes
```{r Interrogating_immunoglobulin_apoptotic_markers_boxplots,  message=FALSE, fig.dim = c(15,15), warning=FALSE, autodep=T, include=T, results='asis'}
<<Interrogating_immunoglobulin_apoptotic_markers_boxplots>>
```
<br />

#### Full Phagocytosis KEGG Pathway
Phagocytosis KEGG Pathway using DEG from MV control and MV post-treatment
```{r KEGG_Pathway_Phagocytosis1,  message=FALSE, fig.dim = c(20,15), warning=FALSE, autodep=T, include=T, results='asis'}
<<KEGG_Pathway_Phagocytosis>>
knitr::include_graphics("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/Bulk_RNA/01_Input_Data/Mouse/mmu04666.pathview.png")
```
<br />

#### Full Apoptosis KEGG Pathway
Apoptosis KEGG Pathway using DEG from MV control and MV post-treatment
```{r KEGG_Pathway_Apoptosis1,  message=FALSE, fig.dim = c(20,15), warning=FALSE, autodep=T, include=T, results='asis'}
<<KEGG_Pathway_Apoptosis>>
knitr::include_graphics("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/Bulk_RNA/01_Input_Data/Mouse/mmu04210.pathview.png")
```
<br />


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<br /><br />