Correlation between Individuals

Last updated: 2019-04-25

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Knit directory: apaQTL/analysis/

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Rmd	eb89a25	brimittleman	2019-04-25	add color bar
html	c64d44b	brimittleman	2019-04-24	Build site.
Rmd	456baa5	brimittleman	2019-04-24	fix header
html	b2525c3	brimittleman	2019-04-24	Build site.
Rmd	9bbe437	brimittleman	2019-04-24	add corr analysis

In this analysis I will look at correlations heatmaps for the counts of all of the inidividuals.

library(tidyverse)

── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.0       ✔ purrr   0.3.2  
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✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(workflowr)

This is workflowr version 1.3.0
Run ?workflowr for help getting started

library(reshape2)


Attaching package: 'reshape2'

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library(gplots)


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library(gdata)

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Load metadata:

metadata=read.table("../data/MetaDataSequencing.txt",header = T)
meta_T=metadata %>% filter(grepl("T", Sample_ID)) %>% mutate(samp=paste("X", Sample_ID, sep=""))
meta_N=metadata %>% filter(grepl("N", Sample_ID)) %>%  mutate(samp=paste("X", Sample_ID, sep=""))

Correlation in counts:

Total

totCount=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Total.Quant.Fixed.fc", stringsAsFactors = F,header=T) %>% select(-Geneid, -Chr, -Start, -End, -Strand, -Length)

Correlation:

totCount_corr= round(cor(totCount),2)

totCount_corr_melt=melt(totCount_corr)

Plot heatmap:

ggplot(data = totCount_corr_melt, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile() +theme(axis.text.x = element_text(angle = 90))

Use heatmap2.

meta_TBatch=meta_T %>% select(samp,batch)

target=colnames(totCount_corr)
meta_TBatch$samp <- reorder.factor(meta_TBatch$samp, new.order=target)
meta_TBatch_order=meta_TBatch %>% arrange(samp)

meta_TBatch_order = meta_TBatch_order %>% mutate(color=ifelse(batch=="1", "blue", ifelse(batch=="2", "green", ifelse(batch=="3", "purple", "pink"))))


heatmap.2(as.matrix(totCount_corr),trace="none", dendrogram =c("col"), ColSideColors =meta_TBatch_order$color, key=T)

Nuclear

nucCount=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Nuclear.Quant.Fixed.fc", stringsAsFactors = F,header=T) %>% select(-Geneid, -Chr, -Start, -End, -Strand, -Length)

Correlation:

nucCount_corr= round(cor(nucCount),2)

nucCount_corr_melt=melt(nucCount_corr)

Plot heatmap:

ggplot(data = nucCount_corr_melt, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile() +theme(axis.text.x = element_text(angle = 90))

meta_NBatch=meta_N %>% select(samp,batch)
target=colnames(nucCount_corr)
meta_NBatch$samp <- reorder.factor(meta_NBatch$samp, new.order=target)
meta_NBatch_order=meta_NBatch %>% arrange(samp)

meta_NBatch_order = meta_NBatch_order %>% mutate(color=ifelse(batch=="1", "blue", ifelse(batch=="2", "green", ifelse(batch=="3", "purple", "pink"))))


heatmap.2(as.matrix(nucCount_corr),trace="none", dendrogram =c("col"), ColSideColors =meta_NBatch_order$color, key=T)

Version	Author	Date
b2525c3	brimittleman	2019-04-24

Correlation in Usage

Total

totUsage=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.CountsNumeric", stringsAsFactors = F,header=F, col.names = colnames(totCount))

Correlation:

totUsage_corr= round(cor(totUsage),2)

totUsage_corr_melt=melt(totUsage_corr)

Plot heatmap:

ggplot(data = totUsage_corr_melt, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile() +theme(axis.text.x = element_text(angle = 90))

Version	Author	Date
b2525c3	brimittleman	2019-04-24

Nuclear

nucUsage=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.CountsNumeric", stringsAsFactors = F,header=F, col.names = colnames(nucCount))

Correlation:

nucUsage_corr= round(cor(nucUsage),2)

nucUsage_corr_melt=melt(nucUsage_corr)

Plot heatmap:

ggplot(data = nucUsage_corr_melt, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile() +theme(axis.text.x = element_text(angle = 90))

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
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[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gdata_2.18.0    gplots_3.0.1    reshape2_1.4.3  workflowr_1.3.0
 [5] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [9] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.0  
[13] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] gtools_3.8.1       tidyselect_0.2.5   haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         rlang_0.3.1       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[16] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[19] rvest_0.3.2        caTools_1.17.1.1   evaluate_0.12     
[22] labeling_0.3       knitr_1.20         broom_0.5.1       
[25] Rcpp_1.0.0         KernSmooth_2.23-15 scales_1.0.0      
[28] backports_1.1.2    jsonlite_1.6       fs_1.2.6          
[31] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[34] grid_3.5.1         rprojroot_1.3-2    cli_1.0.1         
[37] tools_3.5.1        bitops_1.0-6       magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.23.0       compiler_3.5.1

Correlation between Individuals

Briana Mittleman

4/24/2019

Correlation in counts:

Total

Nuclear

Correlation in Usage

Total

Nuclear