Last updated: 2020-03-19
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Knit directory: Comparative_APA/analysis/
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Rmd | 4e2a869 | brimittleman | 2020-03-19 | add SS in opp |
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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
✔ 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 |
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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 |
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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