Species Specific PAS Double Filter

Last updated: 2020-04-03

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffUsedIntronic.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/SpliceSiteStrength.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dAPAandapaQTL_DF.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/upsetter_DF.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	bf79d5e	brimittleman	2020-04-03	update catalot
html	332cbca	brimittleman	2020-03-19	Build site.
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_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)

HumanSpec= PAS %>% filter(Chimp==0)
nrow(HumanSpec)

[1] 316

ChimpSpec= PAS %>% filter(Human==0)
nrow(ChimpSpec)

[1] 359

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
332cbca	brimittleman	2020-03-19
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
332cbca	brimittleman	2020-03-19
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")

Version	Author	Date
332cbca	brimittleman	2020-03-19

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")

Version	Author	Date
332cbca	brimittleman	2020-03-19

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] 40174

nrow(PAS_exp)/nrow(PAS)

[1] 0.8640685

HumanSpecExp= PAS_exp %>% filter(Chimp==0)
nrow(HumanSpecExp)

[1] 232

nrow(HumanSpecExp)/nrow(HumanSpec)

[1] 0.7341772

ChimpSpecExp= PAS_exp %>% filter(Human==0)
nrow(ChimpSpecExp)

[1] 229

nrow(ChimpSpecExp)/nrow(ChimpSpec)

[1] 0.637883

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")

Version	Author	Date
332cbca	brimittleman	2020-03-19

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")

Version	Author	Date
332cbca	brimittleman	2020-03-19

Still a difference but not as strong.

Ask if signal sites account for this:

PASSS=read.table("../data/PAS_doubleFilter/PAS_5perc_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 
303  13

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

summary(HumanSpec_SS$OnlyChimp)

 No Yes 
311   5

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

           PAS  disc           gene    loc   chr     start       end Chimp
1   human83275 Human          RAB35   utr3 chr12 120095725 120095925     0
2   human92962 Human         GPR180   utr3 chr13  94632911  94633111     0
3  human117628 Human       C15orf40   utr3 chr15  83004841  83005041     0
4  human145598 Human        L3MBTL4 intron chr18   6034146   6034346     0
5  human155700 Human         ZNF333 intron chr19  14709065  14709265     0
6  human155707 Human         ZNF333   utr3 chr19  14723160  14723360     0
7  human161570 Human ZNF816-ZNF321P intron chr19  52932780  52932980     0
8  human175138 Human       ANKRD36C intron  chr2  95902665  95902865     0
9  human188421  Both          CFLAR intron  chr2 201125162 201125362     0
10 human210052 Human           ST13   utr3 chr22  40826487  40826687     0
11 human272855 Human   LOC102546294 intron  chr5 148282965 148283165     0
12 human295414 Human   LOC100507557 intron  chr6 145855927 145856127     0
13 human310265 Human         POLR2J intron  chr7 102477792 102477992     0
        Human strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
1  0.09000000         -     Yes      No       Yes        No
2  0.12333333         +     Yes      No       Yes        No
3  0.09833333         -     Yes      No       Yes        No
4  0.07000000         -     Yes      No       Yes        No
5  0.25833333         +     Yes      No       Yes        No
6  0.11166667         +     Yes      No       Yes        No
7  0.05000000         -     Yes      No       Yes        No
8  0.05000000         -     Yes      No       Yes        No
9  0.06333333         +     Yes      No       Yes        No
10 0.09666667         -     Yes      No       Yes        No
11 0.07500000         -     Yes      No       Yes        No
12 0.10166667         +     Yes      No       Yes        No
13 0.05166667         -     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")

Version	Author	Date
332cbca	brimittleman	2020-03-19

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

13 of 303

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

summary(ChimpSpec_SS$OnlyHuman)

 No Yes 
354   5

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

summary(ChimpSpec_SS$OnlyChimp)

 No Yes 
338  21

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

           PAS  disc           gene    loc   chr     start       end
1   chimp42565 Chimp     ENTPD1-AS1 intron chr10  95876254  95876449
2   chimp43312 Chimp         ENTPD7   utr3 chr10  99705169  99705369
3   chimp76729 Chimp         GNPTAB    end chr12 101744014 101744214
4  human106806  Both           VRK1    end chr14  96882568  96882768
5  chimp106755 Chimp        MAPKBP1    end chr15  41831602  41831802
6  chimp124553 Chimp         KLHL36    end chr16  84672144  84672340
7  chimp128457 Chimp       LRRC37A2 intron chr17  46516506  46516706
8  chimp154225 Chimp  ZNF765-ZNF761 intron chr19  53406381  53406581
9  chimp185396 Chimp          RPL31    end  chr2 101006807 101007007
10 human178651  Both         POLR2D   utr3  chr2 127847671 127847871
11 chimp206436 Chimp           EXOG    end  chr3  38526562  38526763
12 chimp208929 Chimp TMEM110-MUSTN1 intron  chr3  52889869  52890069
13 chimp226080 Chimp         MAN2B2   utr3  chr4   6621293   6621496
14 chimp229702 Chimp       ABRAXAS1 intron  chr4  83480746  83480946
15 chimp241260 Chimp          CEP44   utr3  chr4 174317376 174317576
16 chimp242646 Chimp          EXOC3    end  chr5    469833    470033
17 chimp272664 Chimp         PM20D2   utr3  chr6  89162433  89162633
18 chimp281737 Chimp          GNA12    end  chr7   2726153   2726352
19 chimp298751 Chimp         ACTR3C intron  chr7 150268220 150268420
20 chimp300714 Chimp           TNKS   utr3  chr8   9777541   9777741
21 chimp325661 Chimp         GARNL3 intron  chr9 127345020 127345220
        Chimp Human strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
1  0.06333333     0         -      No     Yes        No       Yes
2  0.12750000     0         +      No     Yes        No       Yes
3  0.12000000     0         -      No     Yes        No       Yes
4  0.09416667     0         +      No     Yes        No       Yes
5  0.07083333     0         +      No     Yes        No       Yes
6  0.06083333     0         +      No     Yes        No       Yes
7  0.05666667     0         +      No     Yes        No       Yes
8  0.15833333     0         +      No     Yes        No       Yes
9  0.09666667     0         +      No     Yes        No       Yes
10 0.09833333     0         -      No     Yes        No       Yes
11 0.14000000     0         +      No     Yes        No       Yes
12 0.06083333     0         -      No     Yes        No       Yes
13 0.27500000     0         +      No     Yes        No       Yes
14 0.15833333     0         -      No     Yes        No       Yes
15 0.14000000     0         +      No     Yes        No       Yes
16 0.05500000     0         +      No     Yes        No       Yes
17 0.06083333     0         +      No     Yes        No       Yes
18 0.14583333     0         -      No     Yes        No       Yes
19 0.09166667     0         -      No     Yes        No       Yes
20 0.08666667     0         +      No     Yes        No       Yes
21 0.07333333     0         +      No     Yes        No       Yes

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")

Version	Author	Date
332cbca	brimittleman	2020-03-19

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  human18542 Human   VPS45 intron  chr1 150083405 150083605     0
2 human108342 Human  ATP10A intron chr15  25851409  25851609     0
3 chimp120785 Chimp    PHKB intron chr16  47629989  47630187     0
4 human207697 Human   ZNRF3 intron chr22  28803993  28804193     0
5 human313507 Human METTL2B   utr3  chr7 128501913 128502113     0
       Human strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
1 0.05000000         +      No     Yes        No       Yes
2 0.08833333         -      No     Yes        No       Yes
3 0.05500000         +      No     Yes        No       Yes
4 0.18500000         +      No     Yes        No       Yes
5 0.18500000         +      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 chimp171630 Chimp        TAF1B intron  chr2   9866907   9867104
5 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 0.05916667     0         +     Yes      No       Yes        No
5 0.05833333     0         -     Yes      No       Yes        No

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

Species Specific PAS Double Filter

Briana Mittleman

1/21/2020