dIC and other metrics

Last updated: 2020-05-04

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.Rmd
    Modified:   analysis/MMExpreiment.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/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.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	bc9be31	brimittleman	2020-05-04	add dIC overlap 10% and fix enrich dapa
html	f8b25dd	brimittleman	2020-05-01	Build site.
Rmd	c78e612	brimittleman	2020-05-01	add dic with others

I used simpson to call differences in information content.

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✔ 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()

I want to look at DE, dP, dt, dAPA, and dIC. I will do simple 2by2 tables at first.

For each set I will only consider the genes that I can test for those analylsis.

Load all set:

APA

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)  %>% dplyr::select(PAS, chr, start,end, loc)
DiffIso= read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end')) 

#gene level:  

SigGenesDI=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T,stringsAsFactors = F)
DiffIsoGene= DiffIso %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% SigGenesDI$gene, "Yes", "No"))


DiffIsoGene %>% group_by(dAPA) %>% summarise(n())

# A tibble: 2 x 2
  dAPA  `n()`
  <chr> <int>
1 No     6717
2 Yes    1705

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)

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


DiffExp %>% group_by(DE) %>% summarise(n())

# A tibble: 2 x 2
  DE    `n()`
  <chr> <int>
1 No     6356
2 Yes    3794

DiffExpSmall= DiffExp %>% select(gene,DE)

DTE

Ribo=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name) %>% mutate(dTE=ifelse(HvC.FDR <0.05, "Yes","No"))


Ribo %>% group_by(dTE) %>% summarise(n())

# A tibble: 2 x 2
  dTE   `n()`
  <chr> <int>
1 No     7236
2 Yes    1993

RiboSmall= Ribo %>% select(gene,dTE)

DP
(pval is adjusted already)

Prot= read.table("../data/Khan_prot/ProtData_effectSize.txt",header = T)  %>% mutate(dP=ifelse(pval<0.05, "Yes", "No"))

Prot %>% group_by(dP) %>% summarise(n())

# A tibble: 2 x 2
  dP    `n()`
  <chr> <int>
1 No     2004
2 Yes    1266

ProtSmall=Prot %>% select(gene, dP)

Simpson Info Content

dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F) %>% rename(dIC=sIC)

dICdata %>% group_by(dIC) %>% summarise(n())

# A tibble: 2 x 2
  dIC   `n()`
  <chr> <int>
1 No     7570
2 Yes     881

dICSmall=dICdata %>% select(gene, dIC)

dICdata10= read.table("../data/IndInfoContent/SimpsonMedianSignificance_10FDR.txt", header = T, stringsAsFactors = F) %>% rename(dIC10=sIC10)


dICdata10 %>% group_by(dIC10) %>% summarise(n())

# A tibble: 2 x 2
  dIC10 `n()`
  <chr> <int>
1 No     6697
2 Yes    1754

dIC10Small=dICdata10 %>% select(gene, dIC10)

5% FDR

dAPA and dIC

I will start with dIC and dAPA, I expect a pretty high overlap for this.

dICanddAPA= dICSmall %>% inner_join(DiffIsoGene, by="gene") 


dICanddAPA22=dICanddAPA %>% group_by(dIC, dAPA) %>% summarise(n=n()) %>% spread(dAPA, n) %>% column_to_rownames("dIC")
dICanddAPA22

      No  Yes
No  6291 1251
Yes  426  454

Do this with proportion:

dICanddAPA %>% group_by(dIC, dAPA) %>% summarise(n=n()) %>% mutate(nG=nrow(dICanddAPA),Prop=n/nG)  %>% select(dIC, dAPA, Prop) %>% spread(dAPA, Prop)

# A tibble: 2 x 3
# Groups:   dIC [2]
  dIC       No    Yes
  <chr>  <dbl>  <dbl>
1 No    0.747  0.149 
2 Yes   0.0506 0.0539

Enrichment:

x=nrow(dICanddAPA %>% filter(dIC=="Yes", dAPA=="Yes"))
m=nrow(dICanddAPA %>% filter(dAPA=="Yes"))
n=nrow(dICanddAPA %>% filter(dAPA=="No"))
k=nrow(dICanddAPA %>% filter(dIC=="Yes"))
N=nrow(dICanddAPA)
phyper(x-1,m,n,k,lower.tail=F)

[1] 1.812359e-108

(x/k)/(m/N)

[1] 2.548379

dE and dIC

I will start with dIC and dAPA, I expect a pretty high overlap for this.

dICanddE= dICSmall %>% inner_join(DiffExpSmall, by="gene") 


dICanddE22=dICanddE %>% group_by(dIC, DE) %>% summarise(n=n()) %>% spread(DE, n) %>% column_to_rownames("dIC")
dICanddE22

      No  Yes
No  4233 2464
Yes  466  317

Do this with proportion:

dICanddE %>% group_by(dIC, DE) %>% summarise(n=n()) %>% mutate(nG=nrow(dICanddE),Prop=n/nG)  %>% select(dIC, DE, Prop) %>% spread(DE, Prop)

# A tibble: 2 x 3
# Groups:   dIC [2]
  dIC       No    Yes
  <chr>  <dbl>  <dbl>
1 No    0.566  0.329 
2 Yes   0.0623 0.0424

x=nrow(dICanddE %>% filter(dIC=="Yes", DE=="Yes"))
m=nrow(dICanddE %>% filter(DE=="Yes"))
n=nrow(dICanddE %>% filter(DE=="No"))
k=nrow(dICanddE %>% filter(dIC=="Yes"))
N=nrow(dICanddE)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.02402776

(x/k)/(m/N)

[1] 1.088925

dT and dIC

I will start with dIC and dAPA, I expect a pretty high overlap for this.

dICanddT= dICSmall %>% inner_join(RiboSmall, by="gene") 


dICanddT22=dICanddT %>% group_by(dIC, dTE) %>% summarise(n=n()) %>% spread(dTE, n) %>% column_to_rownames("dIC")
dICanddT22

      No  Yes
No  4557 1241
Yes  504  185

Do this with proportion:

dICanddT %>% group_by(dIC, dTE) %>% summarise(n=n()) %>% mutate(nG=nrow(dICanddT),Prop=n/nG)  %>% select(dIC, dTE, Prop) %>% spread(dTE, Prop)

# A tibble: 2 x 3
# Groups:   dIC [2]
  dIC       No    Yes
  <chr>  <dbl>  <dbl>
1 No    0.702  0.191 
2 Yes   0.0777 0.0285

x=nrow(dICanddT %>% filter(dIC=="Yes", dTE=="Yes"))
m=nrow(dICanddT %>% filter(dTE=="Yes"))
n=nrow(dICanddT %>% filter(dTE=="No"))
k=nrow(dICanddT %>% filter(dIC=="Yes"))
N=nrow(dICanddT)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.0008020285

(x/k)/(m/N)

[1] 1.221453

dP and dIC

I will start with dIC and dAPA, I expect a pretty high overlap for this.

dICanddP= dICSmall %>% inner_join(ProtSmall, by="gene")

Warning: Column `gene` joining character vector and factor, coercing into
character vector

dICanddP22=dICanddP %>% group_by(dIC, dP) %>% summarise(n=n()) %>% spread(dP, n) %>% column_to_rownames("dIC")
dICanddP22

      No Yes
No  1386 909
Yes  224 124

Do this with proportion:

dICanddP %>% group_by(dIC, dP) %>% summarise(n=n()) %>% mutate(nG=nrow(dICanddP),Prop=n/nG)  %>% select(dIC, dP, Prop) %>% spread(dP, Prop)

# A tibble: 2 x 3
# Groups:   dIC [2]
  dIC       No    Yes
  <chr>  <dbl>  <dbl>
1 No    0.524  0.344 
2 Yes   0.0848 0.0469

x=nrow(dICanddP %>% filter(dIC=="Yes", dP=="Yes"))
m=nrow(dICanddP %>% filter(dP=="Yes"))
n=nrow(dICanddP %>% filter(dP=="No"))
k=nrow(dICanddP %>% filter(dIC=="Yes"))
N=nrow(dICanddP)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.9304879

(x/k)/(m/N)

[1] 0.9116734

Not enough power for this one.

10%FDR

dAPA and dIC

I will start with dIC and dAPA, I expect a pretty high overlap for this.

dIC10anddAPA= dIC10Small %>% inner_join(DiffIsoGene, by="gene") 


dIC10anddAPA22=dIC10anddAPA %>% group_by(dIC10, dAPA) %>% summarise(n=n()) %>% spread(dAPA, n) %>% column_to_rownames("dIC10")
dIC10anddAPA22

      No Yes
No  5696 976
Yes 1021 729

Do this with proportion:

dIC10anddAPA %>% group_by(dIC10, dAPA) %>% summarise(n=n()) %>% mutate(nG=nrow(dIC10anddAPA),Prop=n/nG)  %>% select(dIC10, dAPA, Prop) %>% spread(dAPA, Prop)

# A tibble: 2 x 3
# Groups:   dIC10 [2]
  dIC10    No    Yes
  <chr> <dbl>  <dbl>
1 No    0.676 0.116 
2 Yes   0.121 0.0866

Enrichment:

x=nrow(dIC10anddAPA %>% filter(dIC10=="Yes", dAPA=="Yes"))
m=nrow(dIC10anddAPA %>% filter(dAPA=="Yes"))
n=nrow(dIC10anddAPA %>% filter(dAPA=="No"))
k=nrow(dIC10anddAPA %>% filter(dIC10=="Yes"))
N=nrow(dIC10anddAPA)
phyper(x-1,m,n,k,lower.tail=F)

[1] 1.177584e-122

(x/k)/(m/N)

[1] 2.057692

dE and dIC

I will start with dIC and dAPA, I expect a pretty high overlap for this.

dIC10anddE= dIC10Small %>% inner_join(DiffExpSmall, by="gene") 


dIC10anddE22=dIC10anddE %>% group_by(dIC10, DE) %>% summarise(n=n()) %>% spread(DE, n) %>% column_to_rownames("dIC10")
dIC10anddE22

      No  Yes
No  3731 2165
Yes  968  616

Do this with proportion:

dIC10anddE %>% group_by(dIC10, DE) %>% summarise(n=n()) %>% mutate(nG=nrow(dIC10anddE),Prop=n/nG)  %>% select(dIC10, DE, Prop) %>% spread(DE, Prop)

# A tibble: 2 x 3
# Groups:   dIC10 [2]
  dIC10    No    Yes
  <chr> <dbl>  <dbl>
1 No    0.499 0.289 
2 Yes   0.129 0.0824

x=nrow(dIC10anddE %>% filter(dIC10=="Yes", DE=="Yes"))
m=nrow(dIC10anddE %>% filter(DE=="Yes"))
n=nrow(dIC10anddE %>% filter(DE=="No"))
k=nrow(dIC10anddE %>% filter(dIC10=="Yes"))
N=nrow(dIC10anddE)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.06001166

(x/k)/(m/N)

[1] 1.045987

dT and dIC

I will start with dIC and dAPA, I expect a pretty high overlap for this.

dIC10andT= dIC10Small %>% inner_join(RiboSmall, by="gene") 


dIC10andT22=dIC10andT %>% group_by(dIC10, dTE) %>% summarise(n=n()) %>% spread(dTE, n) %>% column_to_rownames("dIC10")
dIC10andT22

      No  Yes
No  4033 1084
Yes 1028  342

Do this with proportion:

dIC10andT %>% group_by(dIC10, dTE) %>% summarise(n=n()) %>% mutate(nG=nrow(dIC10andT),Prop=n/nG)  %>% select(dIC10, dTE, Prop) %>% spread(dTE, Prop)

# A tibble: 2 x 3
# Groups:   dIC10 [2]
  dIC10    No    Yes
  <chr> <dbl>  <dbl>
1 No    0.622 0.167 
2 Yes   0.158 0.0527

x=nrow(dIC10andT %>% filter(dIC10=="Yes", dTE=="Yes"))
m=nrow(dIC10andT %>% filter(dTE=="Yes"))
n=nrow(dIC10andT %>% filter(dTE=="No"))
k=nrow(dIC10andT %>% filter(dIC10=="Yes"))
N=nrow(dIC10andT)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.001669686

(x/k)/(m/N)

[1] 1.135612

dP and dIC

I will start with dIC and dAPA, I expect a pretty high overlap for this.

dIC10anddP= dIC10Small %>% inner_join(ProtSmall, by="gene")

Warning: Column `gene` joining character vector and factor, coercing into
character vector

dIC10anddP22=dIC10anddP %>% group_by(dIC10, dP) %>% summarise(n=n()) %>% spread(dP, n) %>% column_to_rownames("dIC10")
dIC10anddP22

      No Yes
No  1200 783
Yes  410 250

Do this with proportion:

dIC10anddP %>% group_by(dIC10, dP) %>% summarise(n=n()) %>% mutate(nG=nrow(dIC10anddP),Prop=n/nG)  %>% select(dIC10, dP, Prop) %>% spread(dP, Prop)

# A tibble: 2 x 3
# Groups:   dIC10 [2]
  dIC10    No    Yes
  <chr> <dbl>  <dbl>
1 No    0.454 0.296 
2 Yes   0.155 0.0946

x=nrow(dIC10anddP %>% filter(dIC10=="Yes", dP=="Yes"))
m=nrow(dIC10anddP %>% filter(dP=="Yes"))
n=nrow(dIC10anddP %>% filter(dP=="No"))
k=nrow(dIC10anddP %>% filter(dIC10=="Yes"))
N=nrow(dIC10anddP)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.7817668

(x/k)/(m/N)

[1] 0.9691543

Not enough power for this one.

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    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 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    knitr_1.20       httpuv_1.4.5     fansi_0.4.0     
[25] broom_0.5.1      Rcpp_1.0.4.6     promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      magrittr_1.5     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

dIC and other metrics

Briana Mittleman

5/1/2020

Load all set:

5% FDR

dAPA and dIC

dE and dIC

dT and dIC

dP and dIC

10%FDR

dAPA and dIC

dE and dIC

dT and dIC

dP and dIC