Last updated: 2020-04-08
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Knit directory: Comparative_APA/analysis/
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Rmd | 87bde36 | brimittleman | 2020-04-08 | cons and dom in total |
html | a281849 | brimittleman | 2020-04-06 | Build site. |
Rmd | 637094a | brimittleman | 2020-04-06 | look at total dominance |
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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The dominant PAS when you look at the nuclear fraction show an interesting result. I want to refilter the PAS with 5% in total fraction then look to see if the genes with the different dominant PAS have the same pattern.
I am starting the lifted and annotated PAS before I filter for 5%. I will do this filter then the same gene expression filter.
mkdir ../data/TotalFractionPAS
HumanAnno=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::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("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH)
#seperate total and nuclear
HumanUsage_total= HumanUsage %>% select(contains("_T"))
HumanUsage_nuclear= HumanUsage %>% select(contains("_N"))
HumanMean=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsage_nuclear),HumanTot=rowMeans(HumanUsage_total) ))
HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage ))
ChimpAnno=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak")) %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndC=colnames(ChimpAnno)[9:ncol(ChimpAnno)]
ChimpUsage=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC)
ChimpUsage_total= ChimpUsage %>% select(contains("_T"))
ChimpUsage_nuclear= ChimpUsage %>% select(contains("_N"))
ChimpMean=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsage), ChimpTot=rowMeans(ChimpUsage_nuclear)))
ChimpUsage_anno=as.data.frame(cbind(ChimpAnno[,1:8],ChimpUsage ))
Both together:
BothMeanboth=ChimpMean %>% full_join(HumanMean, by=c("chr","start","end","gene" ,"strand", "loc", "disc","PAS" ))
BothMean =BothMeanboth %>% select(-Chimp, -Human)
BothMean_5= BothMean %>% filter(ChimpTot >=0.05 | HumanTot >= 0.05)
Filter passing gene
PassingGenes=read.table("../data/OverlapBenchmark/genesPassingCuttoff.txt", header = T, stringsAsFactors = F, col.names = c("gene"))
BothMean_5_genefilt= BothMean_5 %>% semi_join(PassingGenes,by="gene")
write.table(BothMean_5_genefilt, "../data/TotalFractionPAS/TotalFraction_PASmeta.txt",col.names = T, row.names = F, quote=F)
Get the dominant PAS:
Chimp_Dom= BothMean_5_genefilt %>%
group_by(gene) %>%
top_n(1,ChimpTot) %>%
mutate(nPer=n()) %>%
filter(nPer==1) %>%
select(gene,loc,PAS,ChimpTot) %>%
rename(ChimpLoc=loc, ChimpPAS=PAS, Chimp=ChimpTot)
Human_Dom= BothMean_5_genefilt %>%
group_by(gene) %>%
top_n(1, HumanTot) %>%
mutate(nPer=n()) %>%
filter(nPer==1) %>%
dplyr::select(gene,loc,PAS,HumanTot) %>%
rename(HumanLoc=loc, HumanPAS=PAS,Human=HumanTot)
BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")
SameDom=BothDom %>% filter(ChimpPAS==HumanPAS)
ggplot(SameDom, aes(x=HumanLoc))+ geom_histogram(stat="count") + labs(x="Location", y="Number of Genes", title="Dominant PAS for genes with matching by species \n Total Usage ")
Warning: Ignoring unknown parameters: binwidth, bins, pad
Version | Author | Date |
---|---|---|
a281849 | brimittleman | 2020-04-06 |
write.table(SameDom, "../data/TotalFractionPAS/TotalFraction_sameDominant.txt",col.names = T, row.names = F, quote=F)
DiffDom=BothDom %>% filter(ChimpPAS!=HumanPAS)
write.table(DiffDom, "../data/TotalFractionPAS/TotalFraction_DiffDominant.txt",col.names = T, row.names = F, quote=F)
DiffDom_g= DiffDom %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(DiffDom_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad
Proportion of genes with same and different:
of 9458
nrow(SameDom)
[1] 7154
nrow(SameDom)/nrow(BothDom)
[1] 0.7563967
nrow(DiffDom)
[1] 2304
nrow(DiffDom)/nrow(BothDom)
[1] 0.2436033
numbers in nuclear (of 9479):
[1] 6558 [1] 0.6918451 [1] 2921 [1] 0.3081549
Difference of proportion test for different dominance:
prop.test(x=c(2304,2921), n=c(9458,9479))
2-sample test for equality of proportions with continuity
correction
data: c(2304, 2921) out of c(9458, 9479)
X-squared = 98.419, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
-0.07735517 -0.05174797
sample estimates:
prop 1 prop 2
0.2436033 0.3081549
Difference of proportion test for same dominance:
prop.test(x=c(7154,6558), n=c(9458,9479))
2-sample test for equality of proportions with continuity
correction
data: c(7154, 6558) out of c(9458, 9479)
X-squared = 98.419, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
0.05174797 0.07735517
sample estimates:
prop 1 prop 2
0.7563967 0.6918451
Find the genes with the same dominant in total but different in nuclear.
NuclearDiffDom=read.table("../data/DominantPAS_DF/Nuclear_DiffDom.txt",header = T,stringsAsFactors = F)
NuclearSameDom=read.table("../data/DominantPAS_DF/Nuclear_SameDom.txt",header = T,stringsAsFactors = F)
I want genes in same dom for total and different for nuclear. First make sure it is in the tested set.
NuclearDiffDom_sameTot= NuclearDiffDom %>% semi_join(BothDom,by="gene") %>% anti_join(SameDom, by="gene")
nrow(NuclearDiffDom_sameTot)
[1] 1678
Plot location of these:
NuclearDiffDom_sameTot_g= NuclearDiffDom_sameTot %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(NuclearDiffDom_sameTot_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS in nuclear only") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad
Compare this to those that are different in both:
NuclearDiffDom_diffTot= NuclearDiffDom %>% semi_join(BothDom,by="gene") %>% semi_join(SameDom, by="gene")
nrow(NuclearDiffDom_diffTot)
[1] 1133
NuclearDiffDom_diffTot_g= NuclearDiffDom_diffTot %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(NuclearDiffDom_diffTot_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS in both fractions") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad
Seems pretty similar.
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] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1 workflowr_1.6.0
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] cellranger_1.1.0 rvest_0.3.2 evaluate_0.12
[22] labeling_0.3 knitr_1.20 httpuv_1.4.5
[25] broom_0.5.1 Rcpp_1.0.2 promises_1.0.1
[28] scales_1.0.0 backports_1.1.2 jsonlite_1.6
[31] fs_1.3.1 hms_0.4.2 digest_0.6.18
[34] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[37] cli_1.1.0 tools_3.5.1 magrittr_1.5
[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