Last updated: 2020-06-02
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Knit directory: Comparative_APA/analysis/
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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.
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File | Version | Author | Date | Message |
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Rmd | 3c17955 | brimittleman | 2020-06-02 | add dAPA enrich |
html | e3d5f64 | brimittleman | 2020-06-01 | Build site. |
Rmd | 4e6fd67 | brimittleman | 2020-06-01 | add unlifted analysis |
Test if the non-lifted sites would effect the analysis. I will assign the unlifted human sites to genes with the lifted sites and filter for those that would pass the cutoff.
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
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggpubr':
get_legend
The following object is masked from 'package:ggplot2':
ggsave
mkdir ../data/UnliftedSites
First concatenate the unlifted human sites with the final lifted sties. This is important because I need to compare the final sites with those not lifted. I cannot just use the original sites.
final sites: ../data/cleanPeaks_lifted/AllPAS_postLift.bed
Unlifted primary:
../data/primaryLift/human_APApeaks_primarylift2Chimp_UNLIFTED.bed ../data/reverseLift/human_APApeaks_primarylift2Human_rev2Human_UNLIFTED.bed
PrimUnlifted=read.table("../data/primaryLift/human_APApeaks_primarylift2Chimp_UNLIFTED.bed", header = F, col.names = c("chr", 'start','end','PAS','score', 'strand'),stringsAsFactors = F)
SecUnlift=read.table("../data/reverseLift/human_APApeaks_primarylift2Human_rev2Human_UNLIFTED.bed", header = F, col.names = c("chr", 'start','end','PAS','score', 'strand'),stringsAsFactors = F)
AllUnlift=PrimUnlifted %>% bind_rows(SecUnlift) %>% mutate(name=paste("unlif", PAS, sep=":")) %>% select(chr, start,end, name,score, strand)
Passing=read.table("../data/cleanPeaks_lifted/AllPAS_postLift.bed",header = F, col.names = c("chr",'start','end','name','score','strand'),stringsAsFactors = F)
LiftandNot=AllUnlift %>% bind_rows(Passing)
First compare scores: (is is even before usage)
AllUnliftSet=AllUnlift %>% mutate(set='Unlifted')
PassingSet= Passing %>% mutate(set='lifted')
LiftandNotSet= AllUnliftSet %>% bind_rows(PassingSet)
LiftandNotSet %>% group_by(set) %>% summarise(mean=mean(score))
# A tibble: 2 x 2
set mean
<chr> <dbl>
1 lifted 36.5
2 Unlifted 23.2
ggplot(LiftandNotSet, aes(y=log10(score),x=set)) + geom_boxplot() + stat_compare_means()
Version | Author | Date |
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e3d5f64 | brimittleman | 2020-06-01 |
LiftandNotSet %>% group_by(set) %>% summarise(n())
# A tibble: 2 x 2
set `n()`
<chr> <int>
1 lifted 445944
2 Unlifted 10077
Write these out so I can assign to genes:
write.table(LiftandNot, "../data/UnliftedSites/PAS_liftedandNonLiftedHuman.bed", col.names = F, row.names = F, quote = F, sep = "\t")
Sort:
sort -k1,1 -k2,2n ../data/UnliftedSites/PAS_liftedandNonLiftedHuman.bed > ../data/UnliftedSites/PAS_liftedandNonLiftedHuman.sort.bed
bedtools map -a ../data/UnliftedSites/PAS_liftedandNonLiftedHuman.sort.bed -b ../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_Formatted_Allannotation_noSNO.Resort.bed -c 4 -S -o distinct > ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnno.bed
python chooseAnno2Bed.py ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnno.bed ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.bed
Look at how many of the unlifted are lost due to no gene annotation:
GeneLoc=read.table("../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.bed", header=F, col.names=c("chr",'start','end','id', 'score', 'strand'),stringsAsFactors = F) %>% separate(id, into=c("set", "PAS", "chr2",'start2','end2','strand2','geneloc' ),sep=":") %>% separate(geneloc, into=c("gene", "loc"),sep="_") %>% select(set, PAS, gene, loc)
Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [51056,
51057, 51058, 97083].
GeneLoc %>% group_by(set) %>% summarise(n())
# A tibble: 4 x 2
set `n()`
<chr> <int>
1 Both 165194
2 Chimp 99550
3 Human 130240
4 unlif 6264
3813 of the 10K lost due to no overlap.
make SAF for FC
python bed2SAF_gen.py ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.bed ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.SAF
featureCounts -O -a ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.SAF -F SAF -o ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human ../Human/data/sort_clean/*.bam -s 1
python fixFChead_bothfrac.py ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_fixed.fc
python makePheno.py ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_fixed.fc ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno.txt
Rscript pheno2countonly.R -I ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno.txt -O ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno_countOnly.txt
python convertNumeric.py ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno_countOnly.txt ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno_countOnlyNumeric.txt
HumanAnno=read.table("../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak")) %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndH=colnames(HumanAnno)[9:ncol(HumanAnno)]
HumanUsage=read.table("../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH)
HumanUsage_nuclear= HumanUsage %>% select(contains("_N"))
HumanMean=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsage_nuclear)))
HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage ))
Filter 5% then i can filter the low expressed genes:
HumanMean_5= HumanMean %>% filter(Human >=0.05)
HumanMean_5 %>% group_by(disc) %>% summarise(n())
# A tibble: 4 x 2
disc `n()`
<chr> <int>
1 Both 38111
2 Chimp 6992
3 Human 14766
4 unlif 1069
Now there are only 1K unlifted. Are any of these in the set of genes that pass the expression cutoff.
PassingGenes=read.table("../data/OverlapBenchmark/genesPassingCuttoff.txt", header = T, stringsAsFactors = F)
HumanMean_5_passing=HumanMean_5 %>% filter(gene %in% PassingGenes$genes)
HumanMean_5_passing%>% group_by(disc) %>% summarise(n())
# A tibble: 4 x 2
disc `n()`
<chr> <int>
1 Both 29253
2 Chimp 2486
3 Human 4473
4 unlif 386
Only 386 PAS.
HumanMean_5_passing_unlif= HumanMean_5_passing %>% filter(disc=="unlif")
HumanMean_5_passing_unlif %>% group_by(gene) %>% summarise(n()) %>% nrow()
[1] 353
353 genes.
ggplot(HumanMean_5_passing_unlif,aes(x=Human))+ stat_ecdf()
Version | Author | Date |
---|---|---|
e3d5f64 | brimittleman | 2020-06-01 |
Usage by loc:
ulifloc=ggplot(HumanMean_5_passing_unlif,aes(x=loc))+ geom_bar(stat="count") +geom_text(stat='count', aes(label=..count..), vjust=1, col="white") + labs(x="genic loc", y="Number of PAS", title="Location of Unlifted PAS")
Plot usage:
HumanMean_5_passing= HumanMean_5_passing %>% mutate(Lifted=ifelse(disc=="unlif", "No", "Yes"))
uselift=ggplot(HumanMean_5_passing, aes(y=Human,x=loc, fill=Lifted))+ geom_boxplot() + scale_fill_brewer(palette = "Set1") + labs(y="Human Usage",x="", title="PAS usage by gene location" )
useliftloc=ggplot(HumanMean_5_passing, aes(y=Human,x=Lifted, fill=Lifted))+ geom_boxplot() + scale_fill_brewer(palette = "Set1") + labs(y="Human Usage",x="", title="PAS usage" )
Unlifted origial: 10077 After Anno: 6264 5%: 1069 passing genes:386
liftStats=c(3813, 5195,683, 386)
val=c("Not Annotated","Low Usage", "Not in Gene filter","PassedFilters")
statDF=as.data.frame(cbind(val, liftStats))
statDF$liftStats=as.numeric(as.character(statDF$liftStats))
statDF$val=as.character(statDF$val)
statDF=statDF%>% mutate(sum=sum(liftStats), Prop=liftStats/sum)
propplot=ggplot(statDF,aes(by=val, y=Prop, x="", fill=val)) + geom_bar(stat="identity",width=1, color="white")+ coord_polar("y", start=0) +theme_void() + scale_fill_brewer(palette = "Set1",name="") +labs(title="Of the 10,077 Unlifted PAS") + theme(legend.position = "bottom")
propplot
Version | Author | Date |
---|---|---|
e3d5f64 | brimittleman | 2020-06-01 |
plot_grid(propplot,ulifloc,uselift, useliftloc)
Version | Author | Date |
---|---|---|
e3d5f64 | brimittleman | 2020-06-01 |
Test if these sites are in differential APA genes
PASlevel=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)
IsoDiv=read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F) %>% filter(sIC=="Yes")
Genes with unlifted sites:
HumanMean_5_passing_unlif_g= HumanMean_5_passing_unlif %>% select(gene) %>% unique()
nrow(HumanMean_5_passing_unlif_g)
[1] 353
PASdiffUnlif=PASlevel %>% inner_join(HumanMean_5_passing_unlif_g, by="gene")
nrow(PASdiffUnlif)/nrow(PASlevel)
[1] 0.05043988
nrow(PASdiffUnlif)
[1] 86
IsoDivUnif=IsoDiv%>% inner_join(HumanMean_5_passing_unlif_g, by="gene")
nrow(IsoDivUnif)/nrow(PASlevel)
[1] 0.02580645
nrow(IsoDivUnif)
[1] 44
Are these enriched:
IsoDivUn=read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F) %>% mutate(Unif=ifelse(gene %in%HumanMean_5_passing_unlif_g$gene, "Yes", "No" ))
x=nrow(IsoDivUn %>% filter(Unif=="Yes", sIC=="Yes"))
m=nrow(IsoDivUn %>% filter( sIC=="Yes"))
n=nrow(IsoDivUn %>% filter( sIC=="No"))
k=nrow(IsoDivUn %>% filter(Unif=="Yes"))
N=nrow(IsoDivUn)
phyper(x,m,n,k,lower.tail=F)
[1] 0.06813518
(x/k)/(m/N)
[1] 1.219857
Not a significant enrichment.
PASleveltest=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(sigAPA=ifelse(gene %in% PASlevel$gene, "Yes", "No"),Unif=ifelse(gene %in%HumanMean_5_passing_unlif_g$gene, "Yes", "No" ))
x=nrow(PASleveltest %>% filter(Unif=="Yes", sigAPA=="Yes"))
m=nrow(PASleveltest %>% filter( sigAPA=="Yes"))
n=nrow(PASleveltest %>% filter( sigAPA=="No"))
k=nrow(PASleveltest %>% filter(Unif=="Yes"))
N=nrow(PASleveltest)
phyper(x,m,n,k,lower.tail=F)
[1] 0.01379169
(x/k)/(m/N)
[1] 1.227759
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] cowplot_0.9.4 ggpubr_0.2 magrittr_1.5 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[9] tidyr_0.8.3 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 utf8_1.1.4 rlang_0.4.0
[10] later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.2
[16] readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 workflowr_1.6.0 cellranger_1.1.0
[22] rvest_0.3.2 evaluate_0.12 labeling_0.3
[25] knitr_1.20 httpuv_1.4.5 fansi_0.4.0
[28] broom_0.5.1 Rcpp_1.0.4.6 promises_1.0.1
[31] scales_1.0.0 backports_1.1.2 jsonlite_1.6
[34] fs_1.3.1 hms_0.4.2 digest_0.6.18
[37] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[40] cli_1.1.0 tools_3.5.1 lazyeval_0.2.1
[43] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[46] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[49] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10
[52] R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[55] compiler_3.5.1