Last updated: 2020-03-19

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffUsedIntronic.Rmd
    Modified:   analysis/ExploredAPA.Rmd
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    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/upsetter_DF.Rmd

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File Version Author Date Message
Rmd 4e2a869 brimittleman 2020-03-19 add SS in opp
html 6c39a2a brimittleman 2020-01-21 Build site.
Rmd 010c249 brimittleman 2020-01-21 DF species specific

In this analysis I want to look at the PAS that are identified at at least 10% in one species but are not identified in the other species. I will work with avergage nuclear. I can then run the differential apa analysis with only the PAS identified in both.

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()
library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract
PAS=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)

HumanSpec= PAS %>% filter(Chimp==0)
nrow(HumanSpec)
[1] 233
ChimpSpec= PAS %>% filter(Human==0)
nrow(ChimpSpec)
[1] 226

This is a lot better. This is down from 2500 and 4000 respecively.

Look at the distribution of these accross the gene.

ggplot(HumanSpec,aes(x=loc,fill=loc)) + geom_bar(stat="count")  + labs(x="Genic location", y="Number of PAS", title="Location of Human Specific PAS") + scale_fill_brewer(palette = "Dark2")

Version Author Date
6c39a2a brimittleman 2020-01-21 
ggplot(HumanSpec,aes(x=loc, y=Human, ,fill=loc)) + geom_boxplot()  + labs(x="Genic location", y="Human Average Usage", title="Human Specific PAS") + scale_fill_brewer(palette = "Dark2")

Version Author Date
6c39a2a brimittleman 2020-01-21 
ggplot(ChimpSpec,aes(x=loc,fill=loc)) + geom_bar(stat="count")  + labs(x="Genic location", y="Number of PAS", title="Location of Chimp Specific PAS")+ scale_fill_brewer(palette = "Dark2")

ggplot(ChimpSpec,aes(x=loc, y=Chimp, ,fill=loc)) + geom_boxplot()  + labs(x="Genic location", y="Chimp Average Usage", title="Chimp Specific PAS") + scale_fill_brewer(palette = "Dark2")

Next I will see if these are due to low expression. I will pull in the average normalized expression and rerun the filter.

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID,Gene.name)
expr=read.table("../data/DiffExpression/NoramalizedExpression.txt",header = T,stringsAsFactors = F) %>% rename('Gene_stable_ID'=genes) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name,Chimp, Human) %>% rename("ChimpExp"=Chimp, "HumanExp"=Human, "gene"=Gene.name)

PAS_exp=PAS %>% inner_join(expr,by="gene")
nrow(PAS_exp)
[1] 36028
nrow(PAS_exp)/nrow(PAS)
[1] 0.8513635
HumanSpecExp= PAS_exp %>% filter(Chimp==0)
nrow(HumanSpecExp)
[1] 153
nrow(HumanSpecExp)/nrow(HumanSpec)
[1] 0.6566524
ChimpSpecExp= PAS_exp %>% filter(Human==0)
nrow(ChimpSpecExp)
[1] 143
nrow(ChimpSpecExp)/nrow(ChimpSpec)
[1] 0.6327434

We dont lose as many this way. This is evident the filter worked.

PAS_exp_spe=PAS_exp %>% mutate(HumanSpec=ifelse(gene %in%HumanSpecExp$gene, "yes", "no"), ChimpSpec=ifelse(gene %in% ChimpSpecExp$gene, "yes","no"))

ggplot(PAS_exp_spe,aes(x=HumanSpec,y=HumanExp)) + geom_boxplot() + stat_compare_means(method = "t.test") + labs(x="Presence of Human Specific PAS", y="Average Normalized Expression", title="Expression in Genes with Human Specific PAS")

ggplot(PAS_exp_spe,aes(x=ChimpSpec,y=ChimpExp)) + geom_boxplot() + stat_compare_means(method = "t.test") + labs(x="Presence of Chimp Specific PAS", y="Average Normalized Expression", title="Expression in Genes with Chimp Specific PAS")

Still a difference but not as strong.

Ask if signal sites account for this:

PASSS=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSSTop2.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS,HumanTopSS ,ChimpTopSS)

HumanSpec_SS=HumanSpec %>% inner_join(PASSS, by="PAS")  %>% rename("HumanSS"= HumanTopSS, "ChimpSS"=ChimpTopSS) %>% mutate(OnlyHuman=ifelse(HumanSS=="Yes" & ChimpSS=="No", "Yes", "No"), OnlyChimp=ifelse(HumanSS=="No" & ChimpSS=="Yes", "Yes", "No"))
ChimpSpec_SS=ChimpSpec %>% inner_join(PASSS, by="PAS")%>% rename("HumanSS"= HumanTopSS, "ChimpSS"=ChimpTopSS) %>%  mutate(OnlyHuman=ifelse(HumanSS=="Yes" & ChimpSS=="No", "Yes", "No"), OnlyChimp=ifelse(HumanSS =="No" & ChimpSS=="Yes", "Yes", "No"))

Summary:

HumanSpec_SS$OnlyHuman= as.factor(HumanSpec_SS$OnlyHuman)

summary(HumanSpec_SS$OnlyHuman)
 No Yes 
220  13 
HumanSpec_SS$OnlyChimp= as.factor(HumanSpec_SS$OnlyChimp)

summary(HumanSpec_SS$OnlyChimp)
 No Yes 
231   2 
HumanSpec_SS %>% filter(OnlyHuman=="Yes") 
           PAS  disc         gene    loc   chr     start       end Chimp
1   human77129 Human        CAPS2 intron chr12  75359873  75360073     0
2   human83275 Human        RAB35   utr3 chr12 120095725 120095925     0
3   human92962 Human       GPR180   utr3 chr13  94632911  94633111     0
4  human117628 Human     C15orf40   utr3 chr15  83004841  83005041     0
5  human145598 Human      L3MBTL4 intron chr18   6034146   6034346     0
6  human155700 Human       ZNF333 intron chr19  14709065  14709265     0
7  human155707 Human       ZNF333   utr3 chr19  14723160  14723360     0
8  human195030 Human        RNF24   utr3 chr20   3931407   3931607     0
9  human210052 Human         ST13   utr3 chr22  40826487  40826687     0
10 human237433 Human         LMLN intron  chr3 197962257 197962457     0
11 human269418 Human    LINC01184   utr3  chr5 127966440 127966640     0
12 human272855 Human LOC102546294 intron  chr5 148282965 148283165     0
13 human295414 Human LOC100507557 intron  chr6 145855927 145856127     0
        Human strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
1  0.09000000         -     Yes      No       Yes        No
2  0.07250000         -     Yes      No       Yes        No
3  0.13166667         +     Yes      No       Yes        No
4  0.13166667         -     Yes      No       Yes        No
5  0.05083333         -     Yes      No       Yes        No
6  0.33500000         +     Yes      No       Yes        No
7  0.12250000         +     Yes      No       Yes        No
8  0.06833333         -     Yes      No       Yes        No
9  0.10916667         -     Yes      No       Yes        No
10 0.05750000         +     Yes      No       Yes        No
11 0.08166667         -     Yes      No       Yes        No
12 0.07333333         -     Yes      No       Yes        No
13 0.07666667         +     Yes      No       Yes        No

Look at usage of these:

HumanSpec_SSexp=HumanSpec_SS  %>% filter(OnlyHuman=="Yes") 

ggplot(HumanSpec_SSexp,aes(x=loc, y=Human,fill=loc))+ geom_boxplot() + scale_fill_brewer(palette = "Dark2")

13 sites where there is a gain of a signal site in the human specific. I can look at them in IGV

13 of 233

ChimpSpec_SS$OnlyHuman= as.factor(ChimpSpec_SS$OnlyHuman)

summary(ChimpSpec_SS$OnlyHuman)
 No Yes 
222   4 
ChimpSpec_SS$OnlyChimp= as.factor(ChimpSpec_SS$OnlyChimp)

summary(ChimpSpec_SS$OnlyChimp)
 No Yes 
215  11 
ChimpSpec_SS %>% filter(OnlyChimp=="Yes")
           PAS  disc           gene    loc   chr     start       end
1  chimp106755 Chimp        MAPKBP1    end chr15  41831602  41831802
2  chimp128457 Chimp       LRRC37A2 intron chr17  46516506  46516706
3  chimp154225 Chimp  ZNF765-ZNF761 intron chr19  53406381  53406581
4  chimp185396 Chimp          RPL31    end  chr2 101006807 101007007
5  chimp206436 Chimp           EXOG    end  chr3  38526562  38526763
6  chimp208929 Chimp TMEM110-MUSTN1 intron  chr3  52889869  52890069
7  chimp226080 Chimp         MAN2B2   utr3  chr4   6621293   6621496
8  chimp242646 Chimp          EXOC3    end  chr5    469833    470033
9  chimp281737 Chimp          GNA12    end  chr7   2726153   2726352
10 chimp298751 Chimp         ACTR3C intron  chr7 150268220 150268420
11 chimp325661 Chimp         GARNL3 intron  chr9 127345020 127345220
        Chimp Human strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
1  0.07083333     0         +      No     Yes        No       Yes
2  0.05666667     0         +      No     Yes        No       Yes
3  0.15833333     0         +      No     Yes        No       Yes
4  0.09666667     0         +      No     Yes        No       Yes
5  0.14000000     0         +      No     Yes        No       Yes
6  0.06083333     0         -      No     Yes        No       Yes
7  0.27500000     0         +      No     Yes        No       Yes
8  0.05500000     0         +      No     Yes        No       Yes
9  0.14583333     0         -      No     Yes        No       Yes
10 0.09166667     0         -      No     Yes        No       Yes
11 0.07333333     0         +      No     Yes        No       Yes

11 sites where there is a gain of a signal site in the chimp specific.

11 of 226

Look at usage of these:

ChimpSpec_SSexp=ChimpSpec_SS  %>% filter(OnlyChimp=="Yes") 

ggplot(ChimpSpec_SSexp,aes(x=loc, y=Chimp,fill=loc))+ geom_boxplot() + scale_fill_brewer(palette = "Dark2") + labs(title="Chimp location for Chimp specific PAS with added Signal",x="Genic Location", y="Chimp Mean Usage")

About 5 percent in both.

interesting chimp specific in MAN2b2, there is an AATAAA

Number with opposite relationship.

 HumanSpec_SS  %>% filter(OnlyChimp=="Yes")
          PAS  disc    gene    loc   chr     start       end Chimp
1 human207697 Human   ZNRF3 intron chr22  28803993  28804193     0
2 human313507 Human METTL2B   utr3  chr7 128501913 128502113     0
      Human strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
1 0.1516667         +      No     Yes        No       Yes
2 0.1791667         +      No     Yes        No       Yes
ChimpSpec_SS %>% filter(OnlyHuman=="Yes")
          PAS  disc         gene    loc   chr     start       end
1  chimp56104 Chimp        SPCS2   utr3 chr11  74976869  74977069
2 chimp107271 Chimp         ADAL    cds chr15  43345803  43346003
3 chimp118114 Chimp       SHISA9    end chr16  13565898  13566099
4 chimp192139 Chimp LOC101929947 intron  chr2 172543729 172543929
       Chimp Human strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
1 0.07666667     0         +     Yes      No       Yes        No
2 0.06416667     0         +     Yes      No       Yes        No
3 0.15083333     0         +     Yes      No       Yes        No
4 0.05833333     0         -     Yes      No       Yes        No

2 in the opposite for human, 4 opposite for chimp.


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] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

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