Proportion eQTLs explained

Last updated: 2019-06-13

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rna_netseq_h3k12ac.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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	2fd2b27	brimittleman	2019-06-13	fix bug
html	b907ac1	brimittleman	2019-06-12	Build site.
Rmd	178c5dc	brimittleman	2019-06-12	new geno
html	6b164c8	brimittleman	2019-06-07	Build site.
Rmd	b39620d	brimittleman	2019-06-07	add bonfor results
html	458e494	brimittleman	2019-06-07	Build site.
Rmd	32091ee	brimittleman	2019-06-07	more prop explained to new analysis

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(workflowr)

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

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

I need to fix the explained_FDR10.sort.txt and unexplained_FDR10.sort.txt files because right now this file has multiple genes per snp.

python fixExandUnexeQTL.py ../data/Li_eQTLs/explained_FDR10.sort.txt ../data/Li_eQTLs/explained_FDR10.sort_FIXED.txt
python fixExandUnexeQTL.py ../data/Li_eQTLs/unexplained_FDR10.sort.txt ../data/Li_eQTLs/unexplained_FDR10.sort_FIXED.txt

There are 1195 explained and 814 unexplained eQTLs. I will next look at each of these in my apadata.

Convert nominal results to have snps rather than rsids:

python convertNominal2SNPLOC.py Total
python convertNominal2SNPLOC.py Nuclear

mkdir ../data/overlapeQTL_try2
sbatch run_getapafromeQTL.sh

total

I can group the unexplained by gene and snp then I can ask if there is at least 1 significat peak for each of these.

I will use the bonforoni correction here and multiply the pvalue by the number of peaks in the gene:snp association.

nomnames=c("peakID", 'snp','dist', 'pval', 'slope')
totalapaUnexplained=read.table("../data/overlapeQTL_try2/apaTotal_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames)
totalapaUnexplained=totalapaUnexplained %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>% mutate(nPeaks=n(), adjPval=pval* nPeaks)%>%  dplyr::slice(which.min(adjPval))

totalapaUnexplained_sig= totalapaUnexplained %>% filter(adjPval<.05)

Look at distribution of these pvals:

ggplot(totalapaUnexplained, aes(x=adjPval)) + geom_histogram(bins=50)

Version	Author	Date
b907ac1	brimittleman	2019-06-12

Proportion explained:

nrow(totalapaUnexplained_sig)/nrow(totalapaUnexplained)

[1] 0.1632653

I tested 588 unexplained eQTLs in the total fraction and 96 have a bonforoni corrected significant peak.

Compare to explained eQTLS:

totalapaexplained=read.table("../data/overlapeQTL_try2/apaTotal_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))

totalapaexplained_sig= totalapaexplained %>% filter(adjPval<.05)

nrow(totalapaexplained_sig)/nrow(totalapaexplained)

[1] 0.1304878

I am testing 820 explained eQTLs and of those 107 have a bonforoni corrected significant peak.

difference of proportions:

prop.test(x=c(nrow(totalapaUnexplained_sig),nrow(totalapaexplained_sig)), n=c(nrow(totalapaUnexplained),nrow(totalapaexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(totalapaUnexplained_sig), nrow(totalapaexplained_sig)) out of c(nrow(totalapaUnexplained), nrow(totalapaexplained))
X-squared = 2.722, df = 1, p-value = 0.09898
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.00641871  0.07197371
sample estimates:
   prop 1    prop 2 
0.1632653 0.1304878

ggplot(totalapaUnexplained_sig,aes(x=loc)) + geom_histogram(stat="count",aes(y=..count../sum(..count..))) + labs(y="Proportion", title = "Total apaQTLs explaining eQTLs")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
b907ac1	brimittleman	2019-06-12

totalapaUnexplained_sig_loc= totalapaUnexplained_sig %>% group_by(loc) %>% summarise(nLocTotalUn=n()) %>% mutate(propTotalUn=nLocTotalUn/nrow(totalapaUnexplained_sig))
totalapaexplained_sig_loc= totalapaexplained_sig %>% group_by(loc) %>% summarise(nLocTotalEx=n()) %>% mutate(propTotalEx=nLocTotalEx/nrow(totalapaexplained_sig))

BothTotalLoc=totalapaUnexplained_sig_loc %>% full_join(totalapaexplained_sig_loc,by="loc") %>%  replace_na(list(propTotalUn = 0, nLocTotalUn = 0,propTotalEx=0,nLocTotalEx=0  ))

BothTotalLoc

# A tibble: 5 x 5
  loc    nLocTotalUn propTotalUn nLocTotalEx propTotalEx
  <chr>        <dbl>       <dbl>       <dbl>       <dbl>
1 cds              7      0.0729           8      0.0748
2 end              9      0.0938           7      0.0654
3 intron          17      0.177           20      0.187 
4 utr3            59      0.615           70      0.654 
5 utr5             4      0.0417           2      0.0187

nuclear

nuclearapaUnexplained=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))

nuclearapaUnexplained_sig= nuclearapaUnexplained %>% filter(adjPval<.05)

nrow(nuclearapaUnexplained_sig)/nrow(nuclearapaUnexplained)

[1] 0.1649832

I tested 594 unexplained eQTLs in the nuclear fraction and 98 have a bonforoni corrected significant peak.

nuclearapaexplained=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))

nuclearapaexplained_sig= nuclearapaexplained %>% filter(adjPval<.05)

nrow(nuclearapaexplained_sig)/nrow(nuclearapaexplained)

[1] 0.13269

I tested 829 explained eQTLs in the nuclear fraction and 110 have a nominally significant peak. difference of proportions:

prop.test(x=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)), n=c(nrow(nuclearapaUnexplained),nrow(nuclearapaexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig)) out of c(nrow(nuclearapaUnexplained), nrow(nuclearapaexplained))
X-squared = 2.6386, df = 1, p-value = 0.1043
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.006890426  0.071476780
sample estimates:
   prop 1    prop 2 
0.1649832 0.1326900

ggplot(nuclearapaUnexplained_sig,aes(x=loc))  + geom_histogram(stat="count",aes(y=..count../sum(..count..))) + labs(title = "Nuclear apaQTLs explaining eQTLs", y="Proportion")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
b907ac1	brimittleman	2019-06-12

nuclearapaUnexplained_sig_loc= nuclearapaUnexplained_sig %>% group_by(loc) %>% summarise(nLocnuclearUn=n()) %>% mutate(propnuclearUn=nLocnuclearUn/nrow(nuclearapaUnexplained_sig))
nuclearapaexplained_sig_loc= nuclearapaexplained_sig %>% group_by(loc) %>% summarise(nLocnuclearEx=n()) %>% mutate(propnuclearEx=nLocnuclearEx/nrow(nuclearapaexplained_sig))

BothnuclearLoc=nuclearapaUnexplained_sig_loc %>% full_join(nuclearapaexplained_sig_loc,by="loc") %>%  replace_na(list(propnuclearUn = 0, nLocnuclearUn = 0,propnuclearEx=0,nLocnuclearEx=0  ))

BothnuclearLoc

# A tibble: 5 x 5
  loc    nLocnuclearUn propnuclearUn nLocnuclearEx propnuclearEx
  <chr>          <dbl>         <dbl>         <dbl>         <dbl>
1 cds                4        0.0408             3        0.0273
2 end               10        0.102              9        0.0818
3 intron            18        0.184             33        0.3   
4 utr3              66        0.673             63        0.573 
5 utr5               0        0                  2        0.0182

prop.test(x=c(18,33), n=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(18, 33) out of c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig))
X-squared = 3.1869, df = 1, p-value = 0.07423
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.240913267  0.008260206
sample estimates:
   prop 1    prop 2 
0.1836735 0.3000000

prop.test(x=c(66,63), n=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(66, 63) out of c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig))
X-squared = 1.8258, df = 1, p-value = 0.1766
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.03992433  0.24140856
sample estimates:
   prop 1    prop 2 
0.6734694 0.5727273

total v nuclear

prop.test(x=c(nrow(nuclearapaUnexplained_sig),nrow(totalapaUnexplained_sig)), n=c(nrow(nuclearapaUnexplained),nrow(totalapaUnexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nuclearapaUnexplained_sig), nrow(totalapaUnexplained_sig)) out of c(nrow(nuclearapaUnexplained), nrow(totalapaUnexplained))
X-squared = 1.4301e-06, df = 1, p-value = 0.999
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.04220475  0.04564046
sample estimates:
   prop 1    prop 2 
0.1649832 0.1632653

Differences in proportion by location

allLocProp=BothnuclearLoc %>% full_join(BothTotalLoc, by="loc") %>% select(loc,propnuclearUn,propnuclearEx,propTotalUn,propTotalEx )

allLocPropmelt= melt(allLocProp, id.vars = "loc") %>% mutate(Fraction=ifelse(grepl("Total", variable), "Total", "Nuclear"),eQTL=ifelse(grepl("Un", variable), "Unexplained", "Explained"))


ggplot(allLocPropmelt,aes(x=loc, fill=eQTL, y=value)) + geom_histogram(stat="identity", position = "dodge") + facet_grid(~Fraction)+ labs(y="Proportion of PAS", title="apaQTLs overlaping eQTLs by PAS location")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
b907ac1	brimittleman	2019-06-12

This is a very stringent test. A less stringent way to get an upper bound would be to make an informed decision about which peak to use. This will make it so I am only testing one PAS per gene.

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] reshape2_1.4.3  workflowr_1.3.0 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

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] fansi_0.4.0      readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     utf8_1.1.4       stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Proportion eQTLs explained

Briana Mittleman

6/7/2019

total

nuclear

total v nuclear