Last updated: 2019-12-17
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Knit directory: Comparative_APA/analysis/
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Rmd | 0edf719 | brimittleman | 2019-12-17 | update pantro6 |
html | f4bcae9 | brimittleman | 2019-10-15 | Build site. |
Rmd | 25a8b1e | brimittleman | 2019-10-15 | fix name bug add number PAS analysis |
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(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
In this analysis I will look at thenumber of PAS per species at a gene level. I am only including PAS in chr1-22. These results use mean usage accross fraction.
PAS=read.table("../data/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.txt", stringsAsFactors = F, header = T)
I want to look at the number of PAS at 5% in each gene by human and chimp.
PAS_sm=PAS %>% dplyr::select(gene, Chimp, Human)
PAS_m= melt(PAS_sm, id.var="gene", variable.name="species", value.name="meanUsage") %>% filter(meanUsage >=0.05) %>% group_by(species, gene) %>% summarise(nPAS=n())
#pos = more human, neg = more chimp
PAS_spread=PAS_m %>% spread(species, nPAS, fill=0) %>% mutate(DiffPAS=Human-Chimp)
summary(PAS_spread$DiffPAS)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-8.00000 -1.00000 0.00000 0.04132 1.00000 8.00000
#more human
nrow(PAS_spread[PAS_spread$DiffPAS>0,])
[1] 5783
#more chimp
nrow(PAS_spread[PAS_spread$DiffPAS<0,])
[1] 5082
#same
nrow(PAS_spread[PAS_spread$DiffPAS==0,])
[1] 9175
#all
nrow(PAS_spread)
[1] 20040
prop.test(x=c(5783, 5082), n=c(20040,20040), alternative ="greater")
2-sample test for equality of proportions with continuity
correction
data: c(5783, 5082) out of c(20040, 20040)
X-squared = 61.871, df = 1, p-value = 1.833e-15
alternative hypothesis: greater
95 percent confidence interval:
0.02763141 1.00000000
sample estimates:
prop 1 prop 2
0.2885729 0.2535928
ggplot(PAS_spread, aes(x=DiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_spread$DiffPAS),col="red") + labs(title="Difference in number of PAS at 5% Human vs Chimp", y="Genes", x="N Human PAS - N Chimp PAS")
Version | Author | Date |
---|---|---|
f4bcae9 | brimittleman | 2019-10-15 |
Plot distribution of N pas by species:
Wilcoxan test to see if there is a difference in this distribution.
ChimpNPAS=PAS_m %>% filter(species=="Chimp")
HumanNPAS=PAS_m %>% filter(species=="Human")
wilcox.test(HumanNPAS$nPAS,ChimpNPAS$nPAS ,alternative = "greater")
Wilcoxon rank sum test with continuity correction
data: HumanNPAS$nPAS and ChimpNPAS$nPAS
W = 190470000, p-value = 2.033e-08
alternative hypothesis: true location shift is greater than 0
ggplot(PAS_m,aes(x=nPAS, by=species, fill=species)) + geom_density(stat="count",alpha=.5) + scale_fill_brewer(palette = "Dark2") + labs(title="Distribution for number of PAS >= 5%", x="Number of PAS",y="Genes")
Version | Author | Date |
---|---|---|
f4bcae9 | brimittleman | 2019-10-15 |
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) %>% dplyr::select(contains("_T"))
HumanMeanTotal=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(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) %>% dplyr::select(contains("_T"))
ChimpMeanTotal=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsage)))
Filter 5% and group by gene
BothMean_total=HumanMeanTotal %>% inner_join(ChimpMeanTotal,by=c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene")) %>% filter(Chimp >=.05 | Human >=0.05)
BothMean_total_M=melt(BothMean_total, id.vars = c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene"), value.name = "Total_Usage", variable.name = "Species" )
BothMean_total_gene=BothMean_total_M %>% filter(Total_Usage>=0.05) %>% group_by(Species, gene) %>% summarise(nPASTotal=n())
#pos = more human, neg = more chimp
PAS_Total_spread=BothMean_total_gene %>% spread(Species, nPASTotal, fill=0) %>% mutate(TotalDiffPAS=Human-Chimp)
ggplot(PAS_Total_spread, aes(x=TotalDiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_Total_spread$TotalDiffPAS),col="red") + labs(title="Difference in number of PAS at 5% Human vs Chimp \n Total Fraction", y="Genes", x="N Human PAS - N Chimp PAS")
Version | Author | Date |
---|---|---|
f4bcae9 | brimittleman | 2019-10-15 |
ggplot(BothMean_total_gene,aes(x=nPASTotal, by=Species, fill=Species)) + geom_density(stat="count",alpha=.5) + scale_fill_brewer(palette = "Dark2") + labs(title="Distribution for number of PAS >= 5%\n Total Fraction", x="Number of PAS",y="Genes")
Version | Author | Date |
---|---|---|
f4bcae9 | brimittleman | 2019-10-15 |
HumanUsageNuclear=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH) %>% dplyr::select(contains("_N"))
HumanMeanNuclear=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsageNuclear)))
ChimpUsageNuclear=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC) %>% dplyr::select(contains("_N"))
ChimpMeanNuclear=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsageNuclear)))
Filter 5% and group by gene
BothMean_nuclear=HumanMeanNuclear %>% inner_join(ChimpMeanNuclear,by=c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene")) %>% filter(Chimp >=.05 | Human >=0.05)
BothMean_nuclear_M=melt(BothMean_nuclear, id.vars = c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene"), value.name = "Nuclear_Usage", variable.name = "Species" )
BothMean_nuclear_gene=BothMean_nuclear_M %>% filter(Nuclear_Usage>=0.05) %>% group_by(Species, gene) %>% summarise(nPASNuclear=n())
#pos = more human, neg = more chimp
PAS_Nuclear_spread=BothMean_nuclear_gene %>% spread(Species, nPASNuclear, fill=0) %>% mutate(NuclearDiffPAS=Human-Chimp)
ggplot(PAS_Nuclear_spread, aes(x=NuclearDiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_Nuclear_spread$NuclearDiffPAS),col="red") + labs(title="Difference in number of PAS at 5% Human vs Chimp \n Nuclear Fraction", y="Genes", x="N Human PAS - N Chimp PAS")
Version | Author | Date |
---|---|---|
f4bcae9 | brimittleman | 2019-10-15 |
ggplot(BothMean_nuclear_gene,aes(x=nPASNuclear, by=Species, fill=Species)) + geom_density(stat="count",alpha=.5) + scale_fill_brewer(palette = "Dark2") + labs(title="Distribution for number of PAS >= 5%\n Nuclear Fraction", x="Number of PAS",y="Genes")
Version | Author | Date |
---|---|---|
f4bcae9 | brimittleman | 2019-10-15 |
Compare total and nuclear.
mean(PAS_Total_spread$TotalDiffPAS)
[1] -0.02378543
mean(PAS_Nuclear_spread$NuclearDiffPAS)
[1] 0.05957276
t.test(PAS_Total_spread$TotalDiffPAS, PAS_Nuclear_spread$NuclearDiffPAS,alternative = "greater")
Welch Two Sample t-test
data: PAS_Total_spread$TotalDiffPAS and PAS_Nuclear_spread$NuclearDiffPAS
t = -5.8039, df = 39509, p-value = 1
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
-0.1069829 Inf
sample estimates:
mean of x mean of y
-0.02378543 0.05957276
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] workflowr_1.5.0 reshape2_1.4.3 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] 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