Last updated: 2020-03-05

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Knit directory: Comparative_APA/analysis/

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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  
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── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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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")

1. directional human 2. directional in chimp 3, undetermined 4. no mean difference 5. relaxed in human 6. 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")

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] 11245
nrow(DiffIso %>% select(gene) %>% unique())
[1] 2495

We have data for 11,203 PAS in 2,485 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)


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))

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      394
2 1     Yes      84
3 2     No      243
4 2     Yes      65
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      157
2 1     Yes      32
3 2     No      121
4 2     Yes      28

RNA -84 genes directional to human
-64 genes directional to chimp

Protein
-32 genes directional in human
-27 genes directional in chimp

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       57
2 5     Yes       9
3 6     No       14
4 6     Yes       5
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       12
3 6     Yes       6

9/57, 5/13

RNA -9 genes relax to human (9/66) 13% -5 genes relax to chimp (5/18) 27%

Protein
-0 genes relax in human
-6 genes relax in chimp

I need a way to relate this to something about APA

Examples:


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    
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[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