Species Specific PAS Double Filter
Briana Mittleman
1/21/2020
Last updated: 2020-04-10
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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| 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
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✖ 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_namesThe following object is masked from 'package:tidyr':
extractPAS=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] 302ChimpSpec= PAS %>% filter(Human==0)
nrow(ChimpSpec)[1] 357This 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")
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")
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] 38771nrow(PAS_exp)/nrow(PAS)[1] 0.8725918HumanSpecExp= PAS_exp %>% filter(Chimp==0)
nrow(HumanSpecExp)[1] 231nrow(HumanSpecExp)/nrow(HumanSpec)[1] 0.7649007ChimpSpecExp= PAS_exp %>% filter(Human==0)
nrow(ChimpSpecExp)[1] 241nrow(ChimpSpecExp)/nrow(ChimpSpec)[1] 0.67507We 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")
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$OnlyHuman)No
44 HumanSpec_SS$OnlyChimp= as.factor(HumanSpec_SS$OnlyChimp)
summary(HumanSpec_SS$OnlyChimp) No Yes
43 1 HumanSpec_SS %>% filter(OnlyHuman=="Yes") [1] PAS disc gene loc chr start end
[8] Chimp Human strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
<0 rows> (or 0-length row.names)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 303
ChimpSpec_SS$OnlyHuman= as.factor(ChimpSpec_SS$OnlyHuman)
summary(ChimpSpec_SS$OnlyHuman) No Yes
301 5 ChimpSpec_SS$OnlyChimp= as.factor(ChimpSpec_SS$OnlyChimp)
summary(ChimpSpec_SS$OnlyChimp) No Yes
285 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 chimp106755 Chimp MAPKBP1 end chr15 41831602 41831802
5 chimp122174 Chimp CES2 intron chr16 66936202 66936402
6 chimp124553 Chimp KLHL36 end chr16 84672144 84672340
7 chimp128457 Chimp LRRC37A2 intron chr17 46516506 46516706
8 chimp152847 Chimp MYPOP cds chr19 45895629 45895829
9 chimp154225 Chimp ZNF765-ZNF761 intron chr19 53406381 53406581
10 chimp185396 Chimp RPL31 end chr2 101006807 101007007
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 0.12500000 0 + No Yes No Yes
3 0.11916667 0 - No Yes No Yes
4 0.06833333 0 + No Yes No Yes
5 0.12666667 0 + No Yes No Yes
6 0.06083333 0 + No Yes No Yes
7 0.05500000 0 + No Yes No Yes
8 0.08000000 0 - No Yes No Yes
9 0.15833333 0 + No Yes No Yes
10 0.09666667 0 + No Yes No Yes
11 0.14083333 0 + No Yes No Yes
12 0.06083333 0 - No Yes No Yes
13 0.27416667 0 + No Yes No Yes
14 0.15083333 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 0.09416667 0 - No Yes No Yes
20 0.08666667 0 + No Yes No Yes
21 0.07333333 0 + No Yes No YesLook 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 Human
1 chimp120785 Chimp PHKB intron chr16 47629989 47630187 0 0.05
strandFix HumanSS ChimpSS OnlyHuman OnlyChimp
1 + No Yes No YesChimpSpec_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 0.06750000 0 + Yes No Yes No
2 0.06583333 0 + Yes No Yes No
3 0.15083333 0 + Yes No Yes No
4 0.05916667 0 + Yes No Yes No
5 0.05000000 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