Last updated: 2020-02-03
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/LDregress.Rmd
Modified: analysis/NuclearSpecIncludeNotTested.Rmd
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Modified: analysis/pQTLexampleplot.Rmd
Modified: analysis/propeQTLs_explained.Rmd
Modified: analysis/version15bpfilter.Rmd
Modified: code/DistPAS2Sig.py
Modified: code/apaQTLsnake.err
Deleted: code/test.txt
Deleted: reads_graphs.Rmd
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File | Version | Author | Date | Message |
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Rmd | 147dc38 | brimittleman | 2020-02-03 | add tpm cutoffs |
html | ddfe841 | brimittleman | 2020-01-31 | Build site. |
Rmd | a1607df | brimittleman | 2020-01-31 | look at tissue specifcity |
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(ggpubr)
Loading required package: ggplot2
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library(tidyverse)
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In this analysis I will answer the reviewer questions related to the number of PAS per gene.
First I want to get the number of PAS used at 5% per gene. I am doing this with the nuclear results.
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())
ggplot(PAS,aes(x=nPAS)) + geom_bar()
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
The data goes from 1-10 PAS per gene.
I will start with AllTranscriptsbyName.Grouped.bed. For this I will used the longest annotated transcript by transcription start and end.
genes=read.table("../../genome_anotation_data/RefSeq_annotations/Hg19_refseq_genes.txt",header = T,stringsAsFactors = F) %>%
mutate(Genelength=txEnd-txStart) %>%
group_by(name2) %>%
arrange(desc(Genelength)) %>%
dplyr::slice(1)%>%
dplyr::select(name2, Genelength) %>%
dplyr::rename("gene"= name2)
PAS_wLength= PAS %>% inner_join(genes, by="gene")
Check for correlation
cor.test(PAS_wLength$Genelength, PAS_wLength$nPAS)
Pearson's product-moment correlation
data: PAS_wLength$Genelength and PAS_wLength$nPAS
t = 22.118, df = 15041, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1619636 0.1929179
sample estimates:
cor
0.1774846
PAS_wLength$nPAS=as.factor(PAS_wLength$nPAS)
ggplot(PAS_wLength, aes(x=nPAS,y=log10(Genelength), fill=nPAS)) + geom_boxplot() + labs(x="Number of PAS", y="log10(Length of Gene)", title="Relationship between gene length and Number of PAS")
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
Seperate by only 1 pas vs multiple.
PAS_wLength$nPAS=as.numeric(as.character(PAS_wLength$nPAS))
PAS_wLength_apa= PAS_wLength %>% mutate(APA=ifelse(nPAS>1,"Yes","No"))
ggplot(PAS_wLength_apa,aes(x=APA, y=log10(Genelength))) + geom_boxplot() + stat_compare_means()
Version | Author | Date |
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ddfe841 | brimittleman | 2020-01-31 |
I also will look at length of the longest annotated 3’ UTR:
UTR=read.table("../../genome_anotation_data/RefSeq_annotations/ncbiRefSeq_UTR3.sort.bed",col.names = c('chr','start','end','utr','gene', 'score','strand'),stringsAsFactors = F) %>%
mutate(UTRlength=end-start) %>%
group_by(gene) %>%
arrange(desc(UTRlength)) %>%
dplyr::slice(1) %>%
select(gene, UTRlength)
PAS_wUTRLength= PAS %>% inner_join(UTR, by="gene")
Check for correlation
cor.test(PAS_wLength$Genelength, PAS_wLength$nPAS)
Pearson's product-moment correlation
data: PAS_wLength$Genelength and PAS_wLength$nPAS
t = 22.118, df = 15041, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1619636 0.1929179
sample estimates:
cor
0.1774846
PAS_wUTRLength$nPAS=as.factor(PAS_wUTRLength$nPAS)
ggplot(PAS_wUTRLength, aes(x=nPAS,y=log10(UTRlength), fill=nPAS)) + geom_boxplot() + labs(x="Number of PAS", y="log10(Length of UTR)", title="Relationship between UTR length and Number of PAS")
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
Seperate by only 1 pas vs multiple.
PAS_wUTRLength$nPAS=as.numeric(as.character(PAS_wUTRLength$nPAS))
PAS_wUTRLength_apa= PAS_wUTRLength %>% mutate(APA=ifelse(nPAS>1,"Yes","No"))
ggplot(PAS_wUTRLength_apa,aes(x=APA, y=log10(UTRlength))) + geom_boxplot() + stat_compare_means()
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
ggplot(PAS_wUTRLength_apa,aes(by=APA, fill=APA, x=log10(UTRlength))) + geom_density(alpha=.5)
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
Only the UTR pas:
PAS_Utr= 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=":") %>%
filter(loc=="utr3") %>%
group_by(gene) %>%
summarise(nUTRPAS=n())
UTRPAS_wUTRLength= PAS_Utr %>% inner_join(UTR, by="gene")
Check for correlation
cor.test(UTRPAS_wUTRLength$UTRlength, UTRPAS_wUTRLength$nUTRPAS)
Pearson's product-moment correlation
data: UTRPAS_wUTRLength$UTRlength and UTRPAS_wUTRLength$nUTRPAS
t = 52.33, df = 12406, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.4107098 0.4395393
sample estimates:
cor
0.4252324
summary(lm(log10(UTRPAS_wUTRLength$UTRlength) ~ UTRPAS_wUTRLength$nUTRPAS))
Call:
lm(formula = log10(UTRPAS_wUTRLength$UTRlength) ~ UTRPAS_wUTRLength$nUTRPAS)
Residuals:
Min 1Q Median 3Q Max
-1.80819 -0.28373 0.03181 0.31534 1.40264
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.579931 0.008362 308.54 <2e-16 ***
UTRPAS_wUTRLength$nUTRPAS 0.269648 0.004808 56.09 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4431 on 12406 degrees of freedom
Multiple R-squared: 0.2023, Adjusted R-squared: 0.2022
F-statistic: 3146 on 1 and 12406 DF, p-value: < 2.2e-16
UTRPAS_wUTRLength$nUTRPAS=as.factor(UTRPAS_wUTRLength$nUTRPAS)
ggplot(UTRPAS_wUTRLength, aes(x=nUTRPAS,y=log10(UTRlength), fill=nUTRPAS)) + geom_boxplot() + labs(x="Number of 3' UTR PAS", y="log10(Length of UTR)", title="Relationship between UTR length and Number of PAS")
Version | Author | Date |
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ddfe841 | brimittleman | 2020-01-31 |
Expression level by number of PAS
Calculate mean normalized gene expression values per gene.
geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'gene', 'source' ),stringsAsFactors = F, header = T) %>% select(gene_id, gene)
Rnames=colnames(read.table("../data/molPhenos/RNAhead.txt", header = T))
Expression=read.table("../data/molPhenos/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt.gz",col.names = Rnames) %>%
separate(ID,into=c("gene_id","extra"), sep="\\.") %>%
inner_join(geneNames,by = "gene_id") %>%
select(-Chr,-start,-end,-gene_id, -extra) %>%
gather("ind", "exp", -gene) %>%
group_by(gene) %>%
summarise(MeanExp=mean(exp))
PAS_wExp= PAS %>% inner_join(Expression, by="gene")
cor.test(PAS_wExp$MeanExp, PAS_wExp$nPAS)
Pearson's product-moment correlation
data: PAS_wExp$MeanExp and PAS_wExp$nPAS
t = 5.7763, df = 10337, p-value = 7.859e-09
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.03748661 0.07591472
sample estimates:
cor
0.05672167
summary(lm(PAS_wExp$MeanExp ~ PAS_wExp$nPAS))
Call:
lm(formula = PAS_wExp$MeanExp ~ PAS_wExp$nPAS)
Residuals:
Min 1Q Median 3Q Max
-0.15218 -0.03221 0.00099 0.03222 0.15212
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0032918 0.0008791 -3.744 0.000182 ***
PAS_wExp$nPAS 0.0015122 0.0002618 5.776 7.86e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04714 on 10337 degrees of freedom
Multiple R-squared: 0.003217, Adjusted R-squared: 0.003121
F-statistic: 33.37 on 1 and 10337 DF, p-value: 7.859e-09
PAS_wExp$nPAS=as.factor(PAS_wExp$nPAS)
ggplot(PAS_wExp, aes(x=nPAS,y=MeanExp, fill=nPAS)) + geom_boxplot() + labs(x="Number of PAS", y="Mean normalized expression", title="Relationship between expression and Number of PAS")
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
No aparent difference here. I will remove the 12 and test correlation again.
PAS_wExpFilt= PAS %>% inner_join(Expression, by="gene") %>% filter(nPAS <10)
cor.test(PAS_wExpFilt$MeanExp, PAS_wExpFilt$nPAS)
Pearson's product-moment correlation
data: PAS_wExpFilt$MeanExp and PAS_wExpFilt$nPAS
t = 5.4617, df = 10320, p-value = 4.825e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.03442997 0.07290264
sample estimates:
cor
0.05368623
summary(lm(PAS_wExpFilt$MeanExp ~ PAS_wExpFilt$nPAS))
Call:
lm(formula = PAS_wExpFilt$MeanExp ~ PAS_wExpFilt$nPAS)
Residuals:
Min 1Q Median 3Q Max
-0.151852 -0.032263 0.000969 0.032277 0.152032
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0031448 0.0008866 -3.547 0.000391 ***
PAS_wExpFilt$nPAS 0.0014524 0.0002659 5.462 4.82e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04715 on 10320 degrees of freedom
Multiple R-squared: 0.002882, Adjusted R-squared: 0.002786
F-statistic: 29.83 on 1 and 10320 DF, p-value: 4.825e-08
PAS_wExpFilt$nPAS=as.numeric(as.character(PAS_wExpFilt$nPAS))
PAS_wExpFilt_apa= PAS_wExpFilt %>% mutate(APA=ifelse(nPAS>1,"Yes","No"))
ggplot(PAS_wExpFilt_apa,aes(x=APA, y=MeanExp)) + geom_boxplot() + stat_compare_means()
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
ggplot(PAS_wExpFilt_apa,aes(by=APA, fill=APA, x=MeanExp)) + geom_density(alpha=.5)
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
It looks like there is a significant difference here between genes with APA and those without, but visualy it doesnt look like number of PAS is driven by expression.
I will write out seperate lists for genes with 1 PAS and those with more than one. I will use GOrilla to test for gene set inforamtion
PAS_noapa= PAS %>% filter(nPAS==1) %>% select(gene)
PAS_apa= PAS %>% filter(nPAS>1)%>%arrange(desc(nPAS)) %>% select(gene)
I will use 1 PAS as backgroun and with APA as the set.
mkdir ../data/nPAS/
write.table(PAS_noapa,"../data/nPAS/GenesNoAPA.txt", col.names = F, row.names = F, quote = F)
write.table(PAS_apa,"../data/nPAS/GenesAPA.txt", col.names = F, row.names = F, quote = F)
Significant processes : FDR q <10^-9:
regulation of nucleobase-containing compound metabolic process
regulation of cellular macromolecule biosynthetic process
nucleic acid metabolic process
regulation of macromolecule biosynthetic process
regulation of cellular biosynthetic process regulation of nucleic acid-templated transcription
regulation of RNA biosynthetic process
regulation of transcription, DNA-templated
regulation of biosynthetic process
regulation of nitrogen compound metabolic process
regulation of primary metabolic process regulation of cellular metabolic process
RNA processing
Significant function : FDR q <10^-9:
heterocyclic compound binding
organic cyclic compound binding nucleic acid binding
DNA binding
Significant component : FDR q <10^-9:
intracellular part nucleoplasm nuclear part
intracellular organelle nucleus intracellular membrane-bounded organelle
intracellular organelle part
nucleoplasm part
organelle part
organelle
Not really sure what to do with this. I don’t have an expectation for this. These are key ceullualar processes, functions, and regions. Most genes in this analysis have APA.
Median gene-level TPM by tissue. Median expression was calculated from the file GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz.
I will download information from gtex. I can then set a TPM cutoff and look at for each gene how many tissues it is expressed.
GTEX_test<-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)
ggplot(GTEX_test,aes(y=log10(TPM), by=tissue, fill=tissue)) + geom_boxplot()+theme(legend.position = "none")
Warning: Removed 1456429 rows containing non-finite values (stat_boxplot).
Version | Author | Date |
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ddfe841 | brimittleman | 2020-01-31 |
Try logTPM of 2 - 100
Filter genes that come up with more than 54 due to gene name issues.
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 with the PAS info:
PAS_tissue=PAS %>% inner_join(GTEX,by="gene")
cor.test(PAS_tissue$nPAS, PAS_tissue$nTissue)
Pearson's product-moment correlation
data: PAS_tissue$nPAS and PAS_tissue$nTissue
t = -6.8873, df = 3589, p-value = 6.685e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.14637420 -0.08180869
sample estimates:
cor
-0.114212
PAS_tissue$nPAS= as.factor(PAS_tissue$nPAS)
ggplot(PAS_tissue, aes(x=nPAS,y=nTissue, fill=nPAS)) + geom_boxplot() + labs(x="Number of PAS", y="Number of tissues median TPM>100", title="Relationship tissue specificity and Number of PAS")
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
With and without APA
PAS_tissue$nPAS=as.numeric(as.character(PAS_tissue$nPAS))
PAS_tissue_apa= PAS_tissue %>% mutate(APA=ifelse(nPAS>1,"Yes","No"))
ggplot(PAS_tissue_apa,aes(by=APA, x=nTissue,fill=APA)) + geom_density(alpha=.4)
Version | Author | Date |
---|---|---|
ddfe841 | brimittleman | 2020-01-31 |
Looks like genes with apa are a bit more specific.
Try log(TPM)>1
GTEX_10=read.table("../data/nPAS/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct", header = T, skip=2, sep = '\t') %>%
separate(Name,into=c("gene_id","extra"), sep="\\.") %>%
inner_join(geneNames, by="gene_id") %>%
select(-gene_id,-Description,-extra) %>%
gather("tissue", "TPM",-gene) %>%
filter(TPM >=10 )%>%
group_by(gene) %>%
summarise(nTissue=n()) %>%
filter(nTissue<=54)
Join this with the PAS info:
PAS_tissue10=PAS %>% inner_join(GTEX_10,by="gene")
cor.test(PAS_tissue10$nPAS, PAS_tissue10$nTissue)
Pearson's product-moment correlation
data: PAS_tissue10$nPAS and PAS_tissue10$nTissue
t = -8.7503, df = 11916, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.09771705 -0.06203879
sample estimates:
cor
-0.07990351
PAS_tissue10$nPAS= as.factor(PAS_tissue10$nPAS)
ggplot(PAS_tissue10, aes(x=nPAS,y=nTissue, fill=nPAS)) + geom_boxplot() + labs(x="Number of PAS", y="Number of tissues median TPM>10", title="Relationship tissue specificity and Number of PAS")
With and without APA
PAS_tissue10$nPAS=as.numeric(as.character(PAS_tissue10$nPAS))
PAS_tissue10_apa= PAS_tissue10 %>% mutate(APA=ifelse(nPAS>1,"Yes","No"))
ggplot(PAS_tissue10_apa,aes(by=APA, x=nTissue,fill=APA)) + geom_density(alpha=.4)
Try log(TPM)>3
GTEX_1000=read.table("../data/nPAS/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct", header = T, skip=2, sep = '\t') %>%
separate(Name,into=c("gene_id","extra"), sep="\\.") %>%
inner_join(geneNames, by="gene_id") %>%
select(-gene_id,-Description,-extra) %>%
gather("tissue", "TPM",-gene) %>%
filter(TPM >=1000 )%>%
group_by(gene) %>%
summarise(nTissue=n()) %>%
filter(nTissue<=54)
Join this with the PAS info:
PAS_tissue1000=PAS %>% inner_join(GTEX_1000,by="gene")
cor.test(PAS_tissue1000$nPAS, PAS_tissue1000$nTissue)
Pearson's product-moment correlation
data: PAS_tissue1000$nPAS and PAS_tissue1000$nTissue
t = -2.2996, df = 278, p-value = 0.02221
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.24984963 -0.01972422
sample estimates:
cor
-0.1366298
PAS_tissue1000$nPAS= as.factor(PAS_tissue1000$nPAS)
ggplot(PAS_tissue1000, aes(x=nPAS,y=nTissue, fill=nPAS)) + geom_boxplot() + labs(x="Number of PAS", y="Number of tissues median TPM>1000", title="Relationship tissue specificity and Number of PAS")
With and without APA
PAS_tissue1000$nPAS=as.numeric(as.character(PAS_tissue1000$nPAS))
PAS_tissue1000_apa= PAS_tissue1000 %>% mutate(APA=ifelse(nPAS>1,"Yes","No"))
ggplot(PAS_tissue1000_apa,aes(by=APA, x=nTissue,fill=APA)) + geom_density(alpha=.4)
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 tidyverse_1.2.1
[9] ggpubr_0.2 magrittr_1.5 ggplot2_3.1.1 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 lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[41] pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[45] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10 R6_2.3.0
[49] nlme_3.1-137 git2r_0.26.1 compiler_3.5.1