Last updated: 2020-01-31

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Unstaged changes:
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    Deleted:    reads_graphs.Rmd

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File Version Author Date Message
Rmd 97fee76 brimittleman 2020-01-31 add var ribo and apap 
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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In this analysis I will compare the variance in the APA data to variance in ribosome occupancy data. I will look at the top used PAS for this analysis first. I can then explore more if necessaary. I will compare the across individual variance for genes we have data in both phenotypes for. I will use the normalized usage.

First establish top used PAS per gene.

PAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.bed", header = F,col.names = c("chr", "start","end","name", "score", "strand")) %>% separate(name, into=c("pasnum","gene", "loc"),sep=":") %>% group_by(gene) %>% arrange(desc(score)) %>% slice(1) %>% mutate(PAS=paste("peak",pasnum,sep=""))
Names=c("chr","start","end",colnames(read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz",header = T)))

NuclearPheno=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm.allChrom",col.names = Names) %>% 
  separate(chrom, into=c("c","s","e","peakgene"),sep=":") %>%
  separate(peakgene, into=c("gene", "loc","strand","PAS"),sep="_")  %>% 
  semi_join(PAS, by="PAS") %>% 
  select(-chr,-start,-end,-c,-s,-e,-loc, -strand, -PAS) %>%
  gather("Ind","value", -gene) %>%
  group_by(gene) %>% 
  summarise(VarAPA=var(value))

Ribosome occupancy normalized data:

Change gene names:

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F, header = T)  %>% select(gene_id, GeneName)
Rnames=colnames(read.table("../data/molPhenos/riboHead.txt", header = T))
Ribo=read.table("../data/molPhenos/fastqtl_qqnorm_ribo_phase2.fixed.noChr.txt.gz", col.names = Rnames) %>% 
  separate(ID,into=c("gene_id","extra"), sep="\\.") %>% 
  inner_join(geneNames,by = "gene_id") %>% 
  select(-Chr, -start,-end, -gene_id, -extra) %>% 
  rename("gene"=GeneName) %>%
  gather("Ind","value", -gene) %>%
  group_by(gene) %>% 
  summarise(VarRibo=var(value))

Join both together:

Both=Ribo %>% inner_join(NuclearPheno, by="gene")

We have 8780 genes with data in both.

cor.test(Both$VarRibo, Both$VarAPA,method="spearman")

    Spearman's rank correlation rho

data:  Both$VarRibo and Both$VarAPA
S = 9.5465e+10, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
     rho 
0.153722 
ggplot(Both, aes(x=VarRibo, y=VarAPA)) + geom_point() +geom_smooth(method = "lm") + geom_density2d(col="red") + labs(y="Variance across individuals in nuclear APA", x="Variance across individuals in ribosome occupancy", title="Relationship between APA variance and Ribosome occupancy variance")

summary(lm(Both$VarAPA~ Both$VarRibo))

Call:
lm(formula = Both$VarAPA ~ Both$VarRibo)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.87708 -0.09765  0.03518  0.14780  0.55117 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.020339   0.004768  214.00   <2e-16 ***
Both$VarRibo 0.060888   0.004840   12.58   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2272 on 8778 degrees of freedom
Multiple R-squared:  0.01771,   Adjusted R-squared:  0.0176 
F-statistic: 158.3 on 1 and 8778 DF,  p-value: < 2.2e-16

Significant positive correlation. This does not explain a lot of the variation.


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:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.5.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      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       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.2       promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         hms_0.4.2        digest_0.6.18   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.1.0       
[37] tools_3.5.1      magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  MASS_7.3-51.1    xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1