Categories from Khan

Last updated: 2020-04-10

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
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    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd	fbabfb7	brimittleman	2020-04-10	remove 18499
html	c2a0778	brimittleman	2020-04-06	Build site.
Rmd	9676d72	brimittleman	2020-04-06	updated anno
html	4f8fcd4	brimittleman	2020-03-06	Build site.
Rmd	be1ed38	brimittleman	2020-03-06	add exampels
html	8d7a76e	brimittleman	2020-03-06	Build site.
Rmd	8400ab6	brimittleman	2020-03-06	add enrichment results
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Rmd	a71e15c	brimittleman	2020-03-05	wflow_publish(c(“analysis/index.Rmd”, “analysis/DirSelectionKhan.Rmd”))

I will use the directional selection categories from Khan et al.

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()
✖ dplyr::lag()    masks stats::lag()

Model from Khan et al.

model.num.rna: : 1 = mRNA expression level pattern consistent with directional selection along human lineage, 2 = mRNA expression level pattern consistent with directional selection along chimpanzee lineage, 3 = undetermined pattern, 4 = patterns with no significant difference between mean expression levels; 5 = evidence for relaxation of constraint along human lineage, 6 = evidence of relaxation of constraint along chimpanzee lineage

model.num.protein: 1 = protein expression level pattern consistent with directional selection along human lineage, 2 = protein expression level pattern consistent with directional selection along chimpanzee lineage, 3 = undetermined pattern, 4 = patterns with no significant difference between mean expression levels; 5 = evidence for relaxation of constraint along human lineage, 6 = evidence of relaxation of constraint along chimpanzee lineage

KhanData=read.csv("../data/Khan_prot/Khan_TableS4.csv",stringsAsFactors = F)  %>% select(gene.symbol,contains("model") ) %>% rename("gene"=gene.symbol, "Protein"=model.num.protein, "RNA"=model.num.rna)

KhanData_g=KhanData %>% gather("Set", "Model", -gene)

KhanData_g$Model= as.factor(KhanData_g$Model)

ggplot(KhanData_g, aes(x=Model, by=Set, fill=Set)) +geom_bar(stat="count",position = "dodge")

Version	Author	Date
a1fd883	brimittleman	2020-03-05

directional human
directional in chimp 3, undetermined
no mean difference
relaxed in human
related in chimp

Proportion

KhanData_group=KhanData_g %>% group_by(Set, Model) %>% summarise(Nset=n(), Prortion=Nset/nrow(KhanData))

ggplot(KhanData_group, aes(x=Model, by=Set, fill=Set, y=Prortion)) +geom_bar(stat="identity",position = "dodge")

Version	Author	Date
a1fd883	brimittleman	2020-03-05

This is their results. I will overlap this with the genes I found differences in.

DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(KhanData, by="gene")

nrow(DiffIso)

[1] 12392

nrow(DiffIso %>% select(gene) %>% unique())

[1] 2532

We have data for 12392 PAS in 2532 genes.

I want to get this down to gene level

GenewDiffIso= DiffIso %>% group_by(gene,SigPAU2) %>% summarise(nEach=n()) %>% filter(SigPAU2=="Yes")

KhanData_withAPAinfo= KhanData %>% mutate(dAPA=ifelse(gene %in%GenewDiffIso$gene, "Yes", "No" ))

Plot the proportion with a dAPA in each

KhanData_gwAPA=KhanData_withAPAinfo %>% gather("Set", "Model", -gene, -dAPA)
KhanData_gwAPA$Model= as.factor(KhanData_gwAPA$Model)

KhanData_gwAPA$Set=factor(KhanData_gwAPA$Set, levels=c("RNA", "Protein"))
ggplot(KhanData_gwAPA, aes(x=Model, by=dAPA, fill=dAPA)) +geom_bar(stat="count", position = "stack") + facet_grid(~Set) + scale_fill_brewer(palette = "Dark2") + labs(y="Number of Genes") + scale_x_discrete( labels=c("Selection Human","Selection Chimp","Undetermined","No mean difference","Relaxation in Human","Relaxation in Chimp"))+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16))

Version	Author	Date
c2a0778	brimittleman	2020-04-06
8d7a76e	brimittleman	2020-03-06
a1fd883	brimittleman	2020-03-05

Look at the genes with directional selection:

DirSelectionRNA=KhanData_gwAPA %>% filter(Set=="RNA", Model %in% c(1,2))

DirSelectionRNA %>% group_by(Model,dAPA) %>% summarise(n())

# A tibble: 4 x 3
# Groups:   Model [2]
  Model dAPA  `n()`
  <fct> <chr> <int>
1 1     No      417
2 1     Yes      61
3 2     No      256
4 2     Yes      52

DirSelectionProt=KhanData_gwAPA %>% filter(Set=="Protein", Model %in% c(1,2))

DirSelectionProt %>% group_by(Model,dAPA) %>% summarise(n())

# A tibble: 4 x 3
# Groups:   Model [2]
  Model dAPA  `n()`
  <fct> <chr> <int>
1 1     No      165
2 1     Yes      24
3 2     No      125
4 2     Yes      24

Relaxed selection:

RelSelectionRNA=KhanData_gwAPA %>% filter(Set=="RNA", Model %in% c(5,6))

RelSelectionRNA %>% group_by(Model,dAPA) %>% summarise(n())

# A tibble: 4 x 3
# Groups:   Model [2]
  Model dAPA  `n()`
  <fct> <chr> <int>
1 5     No       55
2 5     Yes      11
3 6     No       16
4 6     Yes       3

RelSelectionProt=KhanData_gwAPA %>% filter(Set=="Protein", Model %in% c(5,6))

RelSelectionProt %>% group_by(Model,dAPA) %>% summarise(n())

# A tibble: 3 x 3
# Groups:   Model [2]
  Model dAPA  `n()`
  <fct> <chr> <int>
1 5     No        2
2 6     No       13
3 6     Yes       5

Enrichement:

Write a loop that gets the pvalue and enrichment for each of these:

Model=seq(1,6)
EnrichmentRNA=c()
PvalueRNA=c()
for (i in seq(1:6)){
  x=nrow(KhanData_gwAPA %>% filter(Set=="RNA", dAPA=="Yes", Model==i))
  m=nrow(KhanData_gwAPA %>% filter(Set=="RNA", Model==i))
  n=nrow(KhanData_gwAPA %>% filter(Set=="RNA", Model!=i))
  k=nrow(KhanData_gwAPA %>% filter(Set=="RNA", dAPA=="Yes"))
  N=nrow(KhanData_gwAPA %>% filter(Set=="RNA"))
  PvalueRNA=c(PvalueRNA, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichmentRNA=c(EnrichmentRNA, enrich)
}

EnrichProt=c()
PvalueProt=c()

for (i in seq(1:6)){
  x=nrow(KhanData_gwAPA %>% filter(Set=="Protein", dAPA=="Yes", Model==i))
  m=nrow(KhanData_gwAPA %>% filter(Set=="Protein", Model==i))
  n=nrow(KhanData_gwAPA %>% filter(Set=="Protein", Model!=i))
  k=nrow(KhanData_gwAPA %>% filter(Set=="Protein", dAPA=="Yes"))
  N=nrow(KhanData_gwAPA %>% filter(Set=="Protein"))
  PvalueProt=c(PvalueProt, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichProt=c(EnrichProt, enrich)
}

EnrichmentResults=as.data.frame(cbind(Model, EnrichmentRNA,EnrichProt,PvalueRNA, PvalueProt))

EnrichmentG= EnrichmentResults %>% select(Model, EnrichmentRNA, EnrichProt) %>% rename("RNA"=EnrichmentRNA, "Protein"=EnrichProt) %>% gather("Set", "Enrichment", -Model)
PvalG= EnrichmentResults %>% select(Model, PvalueRNA, PvalueProt) %>% rename("RNA"=PvalueRNA, "Protein"=PvalueProt) %>% gather("Set", "Pvalue", -Model)

Alldata=EnrichmentG %>% inner_join(PvalG, by=c("Model","Set"))

Alldata$Set=factor(Alldata$Set, levels=c("RNA", "Protein"))
Alldata$Model=factor(Alldata$Model)

ggplot(Alldata,aes(x=Model, y=Enrichment,fill=Set)) + geom_bar(stat = "identity") + geom_hline(yintercept = 1) + geom_text(aes(label=round(Pvalue,2), vjust=0))+ facet_grid(~Set) + scale_fill_brewer(palette = "Dark2")  + scale_x_discrete( labels=c("Selection Human","Selection Chimp","Undetermined","No mean difference","Relaxation in Human","Relaxation in Chimp"))+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16)) +labs(title="Enrichment of dAPA genes in directional selection sets")

Version	Author	Date
c2a0778	brimittleman	2020-04-06
8d7a76e	brimittleman	2020-03-06

I need a way to relate this to something about APA

Examples:

Look at all of the relaxed

RelSelectionRNA %>% filter(dAPA=="Yes", Model=="6")

   gene dAPA Set Model
1  CUL1  Yes RNA     6
2 PTCD3  Yes RNA     6
3  STOM  Yes RNA     6

RelSelectionProt%>% filter(dAPA=="Yes", Model=="6")

   gene dAPA     Set Model
1  IRF5  Yes Protein     6
2 FABP5  Yes Protein     6
3 OTUB1  Yes Protein     6
4  ECI1  Yes Protein     6
5 LYRM7  Yes Protein     6

IRF5 OTUB1

RelSelectionRNA %>% filter(dAPA=="Yes", Model=="5")

      gene dAPA Set Model
1     PYGB  Yes RNA     5
2     CDK6  Yes RNA     5
3    EIF4H  Yes RNA     5
4      MTR  Yes RNA     5
5  SNRNP27  Yes RNA     5
6    STX17  Yes RNA     5
7    APPL1  Yes RNA     5
8    ADPGK  Yes RNA     5
9    ARIH1  Yes RNA     5
10   LIMS1  Yes RNA     5
11    IARS  Yes RNA     5

DirSelectionRNA %>% filter(dAPA=="Yes", Model=="2")

       gene dAPA Set Model
1     RECQL  Yes RNA     2
2     PPP5C  Yes RNA     2
3   SMARCD1  Yes RNA     2
4   DYNC1I2  Yes RNA     2
5     EPS15  Yes RNA     2
6     TMED2  Yes RNA     2
7      GLG1  Yes RNA     2
8     CDC23  Yes RNA     2
9     PSMC1  Yes RNA     2
10     ADNP  Yes RNA     2
11    GOSR2  Yes RNA     2
12     DDX6  Yes RNA     2
13   EIF2B1  Yes RNA     2
14    HDDC2  Yes RNA     2
15    RPL22  Yes RNA     2
16   CAPZA1  Yes RNA     2
17 KIAA2013  Yes RNA     2
18     CBX3  Yes RNA     2
19    PLCG1  Yes RNA     2
20  TUBGCP3  Yes RNA     2
21     IRF3  Yes RNA     2
22    HERC2  Yes RNA     2
23      DUT  Yes RNA     2
24  SYNCRIP  Yes RNA     2
25    SCYL2  Yes RNA     2
26     FLNB  Yes RNA     2
27     RAC1  Yes RNA     2
28      RB1  Yes RNA     2
29     SORD  Yes RNA     2
30   SERBP1  Yes RNA     2
31     TPM3  Yes RNA     2
32   RPL7L1  Yes RNA     2
33   CC2D1B  Yes RNA     2
34    UBE2Z  Yes RNA     2
35     GFM2  Yes RNA     2
36    RPL13  Yes RNA     2
37    STX18  Yes RNA     2
38   POLR1C  Yes RNA     2
39   BCL2L1  Yes RNA     2
40    DCAKD  Yes RNA     2
41    NOC3L  Yes RNA     2
42  DENND4A  Yes RNA     2
43   TMEM70  Yes RNA     2
44    AP3S1  Yes RNA     2
45    RIC8A  Yes RNA     2
46    SNRPE  Yes RNA     2
47     ADI1  Yes RNA     2
48  MORF4L1  Yes RNA     2
49     MYO6  Yes RNA     2
50   MRPL42  Yes RNA     2
51  ANKRD28  Yes RNA     2
52   SEC22B  Yes RNA     2

DirSelectionProt %>% filter(dAPA=="Yes", Model=="2")

      gene dAPA     Set Model
1     CUL1  Yes Protein     2
2    GNAI3  Yes Protein     2
3  DYNC1I2  Yes Protein     2
4     CPOX  Yes Protein     2
5   SAMM50  Yes Protein     2
6     PYGB  Yes Protein     2
7    EIF4H  Yes Protein     2
8   TRIM38  Yes Protein     2
9     KYNU  Yes Protein     2
10   WDR77  Yes Protein     2
11    CUTC  Yes Protein     2
12     DUT  Yes Protein     2
13   YARS2  Yes Protein     2
14  GALNT2  Yes Protein     2
15    CCT5  Yes Protein     2
16  CC2D1B  Yes Protein     2
17   SUGT1  Yes Protein     2
18    NFU1  Yes Protein     2
19    MFN1  Yes Protein     2
20   PGAM1  Yes Protein     2
21  BCL2L1  Yes Protein     2
22   DCAKD  Yes Protein     2
23    MYO6  Yes Protein     2
24  SEC22B  Yes Protein     2

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

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   reshape2_1.4.3     haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         utf8_1.1.4        
[10] rlang_0.4.0        later_0.7.5        pillar_1.3.1      
[13] glue_1.3.0         withr_2.1.2        RColorBrewer_1.1-2
[16] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[19] munsell_0.5.0      gtable_0.2.0       workflowr_1.6.0   
[22] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[25] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[28] fansi_0.4.0        broom_0.5.1        Rcpp_1.0.2        
[31] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[34] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[37] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[40] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[43] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[46] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[49] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[52] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[55] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1

Categories from Khan

Briana Mittleman

3/5/2020