QTL gene tissue specificity

Last updated: 2020-02-12

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	b8d5787	brimittleman	2020-02-12	add tissue var and conservation
html	ddfe841	brimittleman	2020-01-31	Build site.
Rmd	a1607df	brimittleman	2020-01-31	look at tissue specifcity

Number of tissues

I want to ask if genes with a apaQTL are more tissue specific. I will use the GTEX tissue data, similar to how I did for the number of PAS analysis.

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(tidyverse)

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

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(ggpubr)

Loading required package: magrittr


Attaching package: 'magrittr'

The following object is masked from 'package:purrr':

    set_names

The following object is masked from 'package:tidyr':

    extract

The cutoff of 100 was chosen in a previous analysis.

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'gene', 'source' ),stringsAsFactors = F, header = T)  %>% select(gene_id, gene)


GTEX=read.table("../data/nPAS/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct", header = T, skip=2, sep = '\t') %>% 
  separate(Name,into=c("gene_id","extra"), sep="\\.") %>% 
  inner_join(geneNames, by="gene_id") %>% 
  select(-gene_id,-Description,-extra) %>% 
  gather("tissue", "TPM",-gene) %>% 
  filter(TPM >= 100) %>%
  group_by(gene) %>% 
  summarise(nTissue=n()) %>% 
  filter(nTissue<=54)

Join this information with my genes with and without QTLs.

NuclearQTLs=read.table("../data/apaQTLs/NuclearapaQTLGenes.txt", col.names = "gene", stringsAsFactors = F)
NuclearQTLTested=read.table("../data/apaQTLs/TestedNuclearapaQTLGenes.txt",col.names = "gene",stringsAsFactors = F) %>% mutate(apaQTL=ifelse(gene %in%NuclearQTLs$gene, "Yes", "No" ))

NuclearQTLTested$apaQTL=as.factor(NuclearQTLTested$apaQTL)

Join:

NuclearQTLTested_tissue= NuclearQTLTested %>% inner_join(GTEX, by="gene")

Plot:

ggplot(NuclearQTLTested_tissue, aes(x=apaQTL,fill=apaQTL,y=nTissue)) +geom_boxplot()+ stat_compare_means()

Version	Author	Date
ddfe841	brimittleman	2020-01-31

ggplot(NuclearQTLTested_tissue, aes(by=apaQTL,fill=apaQTL,x=nTissue)) +geom_density(alpha=.4)

Version	Author	Date
ddfe841	brimittleman	2020-01-31

NuclearQTLTested_tissue_apa= NuclearQTLTested_tissue %>% filter(apaQTL=="Yes")
NuclearQTLTested_tissue_noapa= NuclearQTLTested_tissue %>% filter(apaQTL=="No")
wilcox.test(NuclearQTLTested_tissue_apa$nTissue, NuclearQTLTested_tissue_noapa$nTissue)


    Wilcoxon rank sum test with continuity correction

data:  NuclearQTLTested_tissue_apa$nTissue and NuclearQTLTested_tissue_noapa$nTissue
W = 122200, p-value = 0.13
alternative hypothesis: true location shift is not equal to 0

No difference in tissue expression.

Variance in expresssion

I want to look at the variance in expression vs the number of PAS.

GTEXvar=read.table("../data/nPAS/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct", header = T, skip=2, sep = '\t',stringsAsFactors = F) %>% 
  separate(Name,into=c("gene_id","extra"), sep="\\.") %>% 
  inner_join(geneNames, by="gene_id") %>% 
  select(-gene_id, -extra, -Description) %>% 
  gather("Tissue", "TPM", -gene) %>%
  group_by(gene) %>% 
  summarise(TissueVar=var(TPM))


PAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed",col.names = c("chr","start","end","name","score","strand")) %>% separate(name,into=c("pas", 'gene','loc'), sep=":") %>% group_by(gene) %>% summarise(nPAS=n()) %>% inner_join(GTEXvar, by="gene")

nrow(PAS)

[1] 13872

ggplot(PAS,aes(x=nPAS, y=TissueVar))+ geom_point() + geom_smooth(method="lm") + geom_density2d(col="red")

cor.test(PAS$nPAS,PAS$TissueVar)


    Pearson's product-moment correlation

data:  PAS$nPAS and PAS$TissueVar
t = -3.1626, df = 13870, p-value = 0.001567
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.04346574 -0.01020726
sample estimates:
        cor 
-0.02684393

cor.test(PAS$nPAS,log10(PAS$TissueVar+1))


    Pearson's product-moment correlation

data:  PAS$nPAS and log10(PAS$TissueVar + 1)
t = -19.737, df = 13870, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1814216 -0.1490480
sample estimates:
       cor 
-0.1652793

ggplot(PAS,aes(x=nPAS, y=log10(TissueVar+1)))+ geom_point() + geom_smooth(method="lm") + geom_density2d(col="red") + labs(x="Number of PAS", y="log10(GTEX TPM variance + 1)", title="Negative correlation between tissue expression variance and PAS number")

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] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.6.0

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      lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  MASS_7.3-51.1    xml2_1.2.0       lubridate_1.7.4 
[45] assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10 
[49] R6_2.3.0         nlme_3.1-137     git2r_0.26.1     compiler_3.5.1

QTL gene tissue specificity

Briana Mittleman

1/31/2020

Number of tissues

Variance in expresssion