Different Dominant Length Differences

Last updated: 2020-04-21

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeESandDomgenes.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/NewdomanddAPA.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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html	fbc6599	brimittleman	2020-04-21	Build site.
Rmd	95685ef	brimittleman	2020-04-21	add length diff analysis

I found a strong enrichment for genes with different dominant PAS in the set of DE genes. ##.4 set
I will start with the 0.4 set. Then I can expand.

library(workflowr)

This is workflowr version 1.6.0
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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✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0

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

PAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
MetaCol=colnames(PAS)

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(gene,DE)
DE_yes= DE %>% filter(DE=="Yes")

HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)

ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)

BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")

BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)

ReswithDE= BothRes_40  %>% select(-Human_DiffDom,-Chimp_DiffDom) %>% gather("species","PAS", -gene, -Set, -cut) %>%  inner_join(DE, by="gene") 

ReswithDE_same= ReswithDE %>% filter(Set=="Same")
ReswithDE_diff= ReswithDE %>% filter(Set=="Different")

Pull in the gene annotation so I can get the length. In human first

genes=read.table("../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_GenesParsed_sort.bed", col.names = c("chr", "geneStart", "geneEnd", "gene", "score", "strand"), stringsAsFactors = F) %>% select(geneStart, geneEnd, gene)

I need to add the meta data for the different PAS.

ReswithDE_diffMeta=ReswithDE_diff %>% inner_join(PAS, by=c("gene","PAS")) %>% mutate(Center=start+100) %>% inner_join(genes, by="gene") %>% mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))

ReswithDE_diffMetaSM= ReswithDE_diffMeta %>% select(gene, species, DE, Dist2TSS) %>% spread(species, Dist2TSS)

Look at the difference in size by DE:

chimp-human

= chimp longer
= human longer

This is the difference in isoform length (not accounting for splce differences)

ReswithDE_diffMetaSM_diff= ReswithDE_diffMetaSM %>% mutate(Diff=Chimp_PAS-Human_PAS)

ggplot(ReswithDE_diffMetaSM_diff, aes(x=Diff,by=DE, col=DE) )+ stat_ecdf()

Version	Author	Date
fbc6599	brimittleman	2020-04-21

Look at the DE:

ReswithDE_diffMetaSM_diff_DE= ReswithDE_diffMetaSM_diff %>% filter(DE=="Yes") %>% mutate(longer=ifelse(Diff>0, "Chimp", "Human"))

ReswithDE_diffMetaSM_diff_DE %>% group_by(longer) %>% summarise(n=n())

# A tibble: 2 x 2
  longer     n
  <chr>  <int>
1 Chimp     44
2 Human     32

ggplot(ReswithDE_diffMetaSM_diff_DE, aes(y=abs(Diff),x=longer))+geom_boxplot() + stat_compare_means()

Version	Author	Date
fbc6599	brimittleman	2020-04-21

I want to add the information about which has higher expression:

DEeffect= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name) %>% select(gene, logFC) %>% mutate(directionDE=ifelse(logFC>=1, "Chimp", "Human"))

LengthDiffandDe=ReswithDE_diffMetaSM_diff_DE %>% inner_join(DEeffect, by="gene")

tableg=LengthDiffandDe %>% group_by(longer, directionDE) %>% summarise(n=n())
tableg

# A tibble: 4 x 3
# Groups:   longer [2]
  longer directionDE     n
  <chr>  <chr>       <int>
1 Chimp  Chimp          10
2 Chimp  Human          34
3 Human  Chimp           9
4 Human  Human          23

More of these are upregulated in human

to run a chi sq test i need to spread this:

tabler= tableg %>% spread(directionDE, n) %>% column_to_rownames("longer")

chisq.test(tabler)


    Pearson's Chi-squared test with Yates' continuity correction

data:  tabler
X-squared = 0.07197, df = 1, p-value = 0.7885

This is not outside of expectation.

Does not seem like length and direction are confounded.

All cutoffs

Expand to larger set:

I need the different ones, then i get the distance for each from the gene file and get the longer isoform, then add in the DE direction

Probably easier to do it one at a time rather than a loop.

BothRes_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10) 
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
#BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)

10

ReswithDE10= BothRes_10  %>% 
  filter(Set=="Different") %>%
  select(-Human_DiffDom,-Chimp_DiffDom) %>% 
  gather("species","PAS", -gene, -Set, -cut) %>% 
  inner_join(DE, by="gene") %>% 
  inner_join(PAS, by=c("gene","PAS")) %>%
  mutate(Center=start+100) %>% 
  inner_join(genes, by="gene") %>% 
  mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))%>% 
  select(gene, species, DE, Dist2TSS) %>%
  spread(species, Dist2TSS)%>% 
  mutate(Diff=Chimp_PAS-Human_PAS) %>% 
  filter(DE=="Yes") %>% 
  mutate(longer=ifelse(Diff>0, "Chimp", "Human"))%>% 
  inner_join(DEeffect, by="gene")

tableg10=ReswithDE10 %>% group_by(longer, directionDE) %>% summarise(n=n()) %>% spread(directionDE, n) %>% column_to_rownames("longer")
tableg10

      Chimp Human
Chimp    53   247
Human    31    99

chisq.test(tableg10)


    Pearson's Chi-squared test with Yates' continuity correction

data:  tableg10
X-squared = 1.8278, df = 1, p-value = 0.1764

20

ReswithDE20= BothRes_20  %>% 
  filter(Set=="Different") %>%
  select(-Human_DiffDom,-Chimp_DiffDom) %>% 
  gather("species","PAS", -gene, -Set, -cut) %>% 
  inner_join(DE, by="gene") %>% 
  inner_join(PAS, by=c("gene","PAS")) %>%
  mutate(Center=start+100) %>% 
  inner_join(genes, by="gene") %>% 
  mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))%>% 
  select(gene, species, DE, Dist2TSS) %>%
  spread(species, Dist2TSS)%>% 
  mutate(Diff=Chimp_PAS-Human_PAS) %>% 
  filter(DE=="Yes") %>% 
  mutate(longer=ifelse(Diff>0, "Chimp", "Human"))%>% 
  inner_join(DEeffect, by="gene")

tableg20=ReswithDE20 %>% group_by(longer, directionDE) %>% summarise(n=n()) %>% spread(directionDE, n) %>% column_to_rownames("longer")
tableg20

      Chimp Human
Chimp    26   127
Human    17    51

chisq.test(tableg20)


    Pearson's Chi-squared test with Yates' continuity correction

data:  tableg20
X-squared = 1.4487, df = 1, p-value = 0.2287

30

ReswithDE30= BothRes_30  %>% 
  filter(Set=="Different") %>%
  select(-Human_DiffDom,-Chimp_DiffDom) %>% 
  gather("species","PAS", -gene, -Set, -cut) %>% 
  inner_join(DE, by="gene") %>% 
  inner_join(PAS, by=c("gene","PAS")) %>%
  mutate(Center=start+100) %>% 
  inner_join(genes, by="gene") %>% 
  mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))%>% 
  select(gene, species, DE, Dist2TSS) %>%
  spread(species, Dist2TSS)%>% 
  mutate(Diff=Chimp_PAS-Human_PAS) %>% 
  filter(DE=="Yes") %>% 
  mutate(longer=ifelse(Diff>0, "Chimp", "Human"))%>% 
  inner_join(DEeffect, by="gene")

tableg30=ReswithDE30 %>% group_by(longer, directionDE) %>% summarise(n=n()) %>% spread(directionDE, n) %>% column_to_rownames("longer")
tableg30

      Chimp Human
Chimp    14    54
Human    14    34

chisq.test(tableg30)


    Pearson's Chi-squared test with Yates' continuity correction

data:  tableg30
X-squared = 0.71084, df = 1, p-value = 0.3992

50

ReswithDE50= BothRes_50  %>% 
  filter(Set=="Different") %>%
  select(-Human_DiffDom,-Chimp_DiffDom) %>% 
  gather("species","PAS", -gene, -Set, -cut) %>% 
  inner_join(DE, by="gene") %>% 
  inner_join(PAS, by=c("gene","PAS")) %>%
  mutate(Center=start+100) %>% 
  inner_join(genes, by="gene") %>% 
  mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))%>% 
  select(gene, species, DE, Dist2TSS) %>%
  spread(species, Dist2TSS)%>% 
  mutate(Diff=Chimp_PAS-Human_PAS) %>% 
  filter(DE=="Yes") %>% 
  mutate(longer=ifelse(Diff>0, "Chimp", "Human"))%>% 
  inner_join(DEeffect, by="gene")

tableg50=ReswithDE50 %>% group_by(longer, directionDE) %>% summarise(n=n()) %>% spread(directionDE, n) %>% column_to_rownames("longer")
tableg50

      Chimp Human
Chimp     4    23
Human     7    17

chisq.test(tableg50)


    Pearson's Chi-squared test with Yates' continuity correction

data:  tableg50
X-squared = 0.81499, df = 1, p-value = 0.3667

60

ReswithDE60= BothRes_60  %>% 
  filter(Set=="Different") %>%
  select(-Human_DiffDom,-Chimp_DiffDom) %>% 
  gather("species","PAS", -gene, -Set, -cut) %>% 
  inner_join(DE, by="gene") %>% 
  inner_join(PAS, by=c("gene","PAS")) %>%
  mutate(Center=start+100) %>% 
  inner_join(genes, by="gene") %>% 
  mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))%>% 
  select(gene, species, DE, Dist2TSS) %>%
  spread(species, Dist2TSS)%>% 
  mutate(Diff=Chimp_PAS-Human_PAS) %>% 
  filter(DE=="Yes") %>% 
  mutate(longer=ifelse(Diff>0, "Chimp", "Human"))%>% 
  inner_join(DEeffect, by="gene")

tableg60=ReswithDE60 %>% group_by(longer, directionDE) %>% summarise(n=n()) %>% spread(directionDE, n) %>% column_to_rownames("longer")
tableg60

      Chimp Human
Chimp     3    16
Human     3    14

chisq.test(tableg60)

Warning in chisq.test(tableg60): Chi-squared approximation may be incorrect


    Pearson's Chi-squared test with Yates' continuity correction

data:  tableg60
X-squared = 1.3188e-31, df = 1, p-value = 1

70

ReswithDE70= BothRes_70  %>% 
  filter(Set=="Different") %>%
  select(-Human_DiffDom,-Chimp_DiffDom) %>% 
  gather("species","PAS", -gene, -Set, -cut) %>% 
  inner_join(DE, by="gene") %>% 
  inner_join(PAS, by=c("gene","PAS")) %>%
  mutate(Center=start+100) %>% 
  inner_join(genes, by="gene") %>% 
  mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))%>% 
  select(gene, species, DE, Dist2TSS) %>%
  spread(species, Dist2TSS)%>% 
  mutate(Diff=Chimp_PAS-Human_PAS) %>% 
  filter(DE=="Yes") %>% 
  mutate(longer=ifelse(Diff>0, "Chimp", "Human"))%>% 
  inner_join(DEeffect, by="gene")

tableg70=ReswithDE70 %>% group_by(longer, directionDE) %>% summarise(n=n()) %>% spread(directionDE, n) %>% column_to_rownames("longer") %>% replace_na(list(Chimp=0, Human=0))
tableg70

      Chimp Human
Chimp     2    10
Human     0     6

chisq.test(tableg70)

Warning in chisq.test(tableg70): Chi-squared approximation may be incorrect


    Pearson's Chi-squared test with Yates' continuity correction

data:  tableg70
X-squared = 0.070312, df = 1, p-value = 0.7909

80

ReswithDE80= BothRes_80  %>% 
  filter(Set=="Different") %>%
  select(-Human_DiffDom,-Chimp_DiffDom) %>% 
  gather("species","PAS", -gene, -Set, -cut) %>% 
  inner_join(DE, by="gene") %>% 
  inner_join(PAS, by=c("gene","PAS")) %>%
  mutate(Center=start+100) %>% 
  inner_join(genes, by="gene") %>% 
  mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))%>% 
  select(gene, species, DE, Dist2TSS) %>%
  spread(species, Dist2TSS)%>% 
  mutate(Diff=Chimp_PAS-Human_PAS) %>% 
  filter(DE=="Yes") %>% 
  mutate(longer=ifelse(Diff>0, "Chimp", "Human"))%>% 
  inner_join(DEeffect, by="gene")

tableg80=ReswithDE80 %>% group_by(longer, directionDE) %>% summarise(n=n()) %>% spread(directionDE, n) %>% column_to_rownames("longer")%>% replace_na(list(Chimp=0, Human=0))
tableg80

      Human
Chimp     8
Human     3

chisq.test(tableg80)


    Chi-squared test for given probabilities

data:  tableg80
X-squared = 2.2727, df = 1, p-value = 0.1317

90

ReswithDE90= BothRes_90  %>% 
  filter(Set=="Different") %>%
  select(-Human_DiffDom,-Chimp_DiffDom) %>% 
  gather("species","PAS", -gene, -Set, -cut) %>% 
  inner_join(DE, by="gene") %>% 
  inner_join(PAS, by=c("gene","PAS")) %>%
  mutate(Center=start+100) %>% 
  inner_join(genes, by="gene") %>% 
  mutate(Dist2TSS=ifelse(strandFix=="+", Center-geneStart, geneEnd-Center))%>% 
  select(gene, species, DE, Dist2TSS) %>%
  spread(species, Dist2TSS)%>% 
  mutate(Diff=Chimp_PAS-Human_PAS) %>% 
  filter(DE=="Yes") %>% 
  mutate(longer=ifelse(Diff>0, "Chimp", "Human"))%>% 
  inner_join(DEeffect, by="gene")

tableg90=ReswithDE90 %>% group_by(longer, directionDE) %>% summarise(n=n()) %>% spread(directionDE, n) %>% column_to_rownames("longer")%>% replace_na(list(Chimp=0, Human=0))
tableg90

      Human
Chimp     5
Human     1

chisq.test(tableg90)

Warning in chisq.test(tableg90): Chi-squared approximation may be incorrect


    Chi-squared test for given probabilities

data:  tableg90
X-squared = 2.6667, df = 1, p-value = 0.1025

All pvalue:

pvalues=c(chisq.test(tableg90)$p.value,chisq.test(tableg80)$p.value,chisq.test(tableg80)$p.value,chisq.test(tableg70)$p.value,chisq.test(tableg60)$p.value,chisq.test(tableg50)$p.value,chisq.test(tabler)$p.value,chisq.test(tableg30)$p.value,chisq.test(tableg20)$p.value, chisq.test(tableg10)$p.value)

Warning in chisq.test(tableg90): Chi-squared approximation may be incorrect

Warning in chisq.test(tableg70): Chi-squared approximation may be incorrect

Warning in chisq.test(tableg60): Chi-squared approximation may be incorrect

pvalues

 [1] 0.1024704 0.1316680 0.1316680 0.7908823 1.0000000 0.3666503 0.7884902
 [8] 0.3991644 0.2287363 0.1763932

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_1.6.0

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       utf8_1.1.4      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.2       promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.1.0        tools_3.5.1      lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1

Different Dominant Length Differences

Briana Mittleman

4/21/2020

All cutoffs

10

20

30

50

60

70

80

90

All pvalue: