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
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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
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Rmd | 2fd2b27 | brimittleman | 2019-06-13 | fix bug |
html | b907ac1 | brimittleman | 2019-06-12 | Build site. |
Rmd | 178c5dc | brimittleman | 2019-06-12 | new geno |
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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()
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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
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
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
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