Test by number of PAS

Last updated: 2020-02-03

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/LDregress.Rmd
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    Modified:   code/apaQTLsnake.err
    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	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

Loading required package: magrittr

library(tidyverse)

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

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✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0

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

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.

length of 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
ddfe841	brimittleman	2020-01-31

UTR length

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
ddfe841	brimittleman	2020-01-31

Expression level

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.

Go terms

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.

Tissue specificity

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
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

Test by number of PAS

Briana Mittleman

1/31/2020

length of gene

UTR length

Expression level

Go terms

Tissue specificity