This is a data report outlining the a pearson correlation analysis of viral qpcr and clinical score data in the CC lines.

This analysis is based off code documented here:

https://www.r-bloggers.com/r-defining-your-own-color-schemes-for-heatmaps/

library(gplots)

## Warning: package 'gplots' was built under R version 3.2.5

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

library(ggplot2)
library(reshape2)

## Warning: package 'reshape2' was built under R version 3.2.5

sessionInfo()

## R version 3.2.3 (2015-12-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 14393)
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## [1] LC_COLLATE=English_United States.1252 
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## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
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## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
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## other attached packages:
## [1] reshape2_1.4.2 ggplot2_2.2.1  gplots_3.0.1  
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##  [1] Rcpp_0.12.8        knitr_1.15.1       magrittr_1.5      
##  [4] munsell_0.4.3      colorspace_1.3-2   stringr_1.1.0     
##  [7] plyr_1.8.4         caTools_1.17.1     tools_3.2.3       
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## [19] digest_0.6.11      tibble_1.2         bitops_1.0-6      
## [22] evaluate_0.10      rmarkdown_1.3      gdata_2.17.0      
## [25] stringi_1.1.2      scales_0.4.1       backports_1.0.4

oas1b_corr_plot <- read.csv(file="C:\\gale_lab\\oas1b_manuscript\\revisions\\qPCR_and_outcomes.csv", header=T)

#transponse values 
m <- melt(cor(oas1b_corr_plot[,c(5:12)], use="pairwise.complete.obs"))

# create a correlation plot image using ggplot2
p <- ggplot(data=m, aes(x=Var1, y=Var2, fill=value)) + geom_tile() + theme(text = element_text(size=20),
axis.text.x = element_text(angle=90, hjust=1))

We update the color panel to show those conditions with the highest correlation.

#set up a coloring scheme using colorRampPalette
red=rgb(1,0,0); green=rgb(0,1,0); blue=rgb(0,0,1); white=rgb(1,1,1)
RtoWrange<-colorRampPalette(c(red, white ) )
WtoGrange<-colorRampPalette(c(white, green) ) 

p <- p + scale_fill_gradient2(low=RtoWrange(100), mid=WtoGrange(100), high="gray")

p + theme(text = element_text(size=20),
axis.text.x = element_text(angle=90, hjust=1))

In the correlation heatmap above, there is a relationship between viral load and clinical score at days 4 and 7 post infection. There is also a correlation between clinical score and viral load in the brain at day 12 post infection.

But this is against all Oas1b haplotypes, lets see if we observe a correlation in the Null/Null lines (N/N) and Heterozygous lines.

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.2.5

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
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#filter heterozygous data

oas1b_corr_plot_NF_FN <- filter(oas1b_corr_plot, Oas1b_status %in% c("Null+Functional","Functional+Null"))

m_NF_FN <- melt(cor(oas1b_corr_plot_NF_FN[,c(5:12)],use="pairwise.complete.obs"))

## Warning in cor(oas1b_corr_plot_NF_FN[, c(5:12)], use =
## "pairwise.complete.obs"): the standard deviation is zero

# filter null data

oas1b_corr_plot_NN <- filter(oas1b_corr_plot, Oas1b_status %in% c("Null+Null"))

#plot null null data

m_NN <- melt(cor(oas1b_corr_plot_NN[,c(5:12)],use="pairwise.complete.obs"))

p_NN <- ggplot(data=m_NN, aes(x=Var1, y=Var2, fill=value)) + geom_tile() + theme(text = element_text(size=20),
axis.text.x = element_text(angle=90, hjust=1))

#reset the coloring scheme using colorRampPalette
red=rgb(1,0,0); green=rgb(0,1,0); blue=rgb(0,0,1); white=rgb(1,1,1)
RtoWrange<-colorRampPalette(c(red, white ) )
WtoGrange<-colorRampPalette(c(white, green) ) 

p_NN <- p_NN + scale_fill_gradient2(low=RtoWrange(100), mid=WtoGrange(100), high="gray")

p_NN + theme(text = element_text(size=20),
axis.text.x = element_text(angle=90, hjust=1))

We note a higher correlation with the clinical score and disease phenotypes in N/N RIXs.

Now lets looks at heterzygous RIXs and their relationships to phenotypes

p_NF_FN <- ggplot(data=m_NF_FN, aes(x=Var1, y=Var2, fill=value)) + geom_tile() + theme(text = element_text(size=20),
axis.text.x = element_text(angle=90, hjust=1))

#reset the coloring scheme using colorRampPalette
red=rgb(1,0,0); green=rgb(0,1,0); blue=rgb(0,0,1); white=rgb(1,1,1)
RtoWrange<-colorRampPalette(c(red, white ) )
WtoGrange<-colorRampPalette(c(white, green) ) 

p_NF_FN <- p_NF_FN + scale_fill_gradient2(low=RtoWrange(100), mid=WtoGrange(100), high="gray")

p_NF_FN + theme(text = element_text(size=20),
axis.text.x = element_text(angle=90, hjust=1))

We note a lower correlation (but still observable) with the clinical score and disease phenotypes in F/N and N/F RIXs.