Last updated: 2020-03-05
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
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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
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✔ readr 1.3.1 ✔ forcats 0.3.0
── 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
[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
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[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