Last updated: 2020-04-01
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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.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 7447f2d | brimittleman | 2020-04-01 | add norm dom and add filter comp plots |
html | 2d84fb1 | brimittleman | 2020-01-23 | Build site. |
Rmd | 908f02d | brimittleman | 2020-01-23 | add compare filter and write out for top SS |
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble 2.1.1 ✔ purrr 0.3.2
✔ tidyr 0.8.3 ✔ dplyr 0.8.0.1
✔ readr 1.3.1 ✔ stringr 1.3.1
✔ tibble 2.1.1 ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract() masks magrittr::extract()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()
Prefilter:
pre=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt",header = T, stringsAsFactors = F) %>% filter(loc != "008559")
nrow(pre)
[1] 73095
prebygene=pre %>% group_by(gene) %>% summarise(n=n())
nrow(prebygene)
[1] 20042
ggplot(pre,aes(x=loc,fill=loc) ) + geom_bar(stat="count") + scale_fill_brewer(palette = "Dark2")+ labs(y="Number of PAS", title="Pre filter location of PAS",x="")
Version | Author | Date |
---|---|---|
2d84fb1 | brimittleman | 2020-01-23 |
nrow(prebygene)
[1] 20042
ggplot(prebygene, aes(x=n))+ geom_bar() + labs(x="Number of PAS per gene", title="Number of PAS per gene before filter")
Version | Author | Date |
---|---|---|
2d84fb1 | brimittleman | 2020-01-23 |
Post:
post=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T, stringsAsFactors = F)
nrow(post)
[1] 42318
postbygene=post %>% group_by(gene) %>% summarise(n=n())
nrow(postbygene)
[1] 9810
ggplot(post,aes(x=loc,fill=loc) ) + geom_bar(stat="count")+ scale_fill_brewer(palette = "Dark2") + labs(y="Number of PAS", title="Post filter location of PAS", x="")
Version | Author | Date |
---|---|---|
2d84fb1 | brimittleman | 2020-01-23 |
ggplot(postbygene, aes(x=n))+ geom_bar() + labs(x="Number of PAS per gene", title="Number of PAS per gene after filter")
Version | Author | Date |
---|---|---|
2d84fb1 | brimittleman | 2020-01-23 |
Look at the usage distribution:
post_loc=post %>% select(PAS,loc,Chimp,Human) %>% gather("species", "MeanUsage",-PAS, -loc)
ggplot(post_loc, aes(x=loc, y=MeanUsage, by=species, fill=species)) + geom_boxplot() + stat_compare_means(label = 'p.signif') + scale_fill_brewer(palette = "Dark2") + labs(y="Mean PAS Usage", x="", title="Difference in mean usage for PAS\n 5% Usage and Expression filter")
Cummulative distribution plot:
ggplot(post_loc, aes(x=MeanUsage,col=species)) + stat_ecdf() +labs(x="Mean Usage", y="CDF", title="Cumulative distribution for filtered PAS") + scale_color_brewer(palette = "Dark2")
wilcox.test(post$Human, post$Chimp, paired = F, alternative = "greater")
Wilcoxon rank sum test with continuity correction
data: post$Human and post$Chimp
W = 911790000, p-value = 2.015e-06
alternative hypothesis: true location shift is greater than 0
Is it a problem with counts:
Indivdual Usage:
post_chrom=post%>% mutate(chrom=paste(chr,start,end,gene, sep=":")) %>% select(chrom,PAS)
human=read.table("../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc", stringsAsFactors = F, header = T) %>% rownames_to_column(var="chrom") %>% inner_join(post_chrom, by="chrom")
human_meanCount=human %>% gather("ind","count", -PAS,-chrom) %>% group_by(PAS) %>% summarise(MeanCountHuman=mean(count), VarCountHuman=var(count))
chimp=read.table("../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc", stringsAsFactors = F, header = T)%>% rownames_to_column(var="chrom")%>% inner_join(post_chrom, by="chrom")
chimp_meanCount=chimp %>% gather("ind","count", -PAS,-chrom) %>% group_by(PAS) %>% summarise(MeanCountChimp=mean(count), VarCountChimp=var(count))
BothMeanVar=human_meanCount %>% inner_join(chimp_meanCount, by="PAS")
BothMean= BothMeanVar %>% select(-contains("Var")) %>% gather("Species", "Mean", -PAS)
BothVar= BothMeanVar %>% select(-contains("Mean")) %>% gather("Species", "Var", -PAS)
ggplot(BothMean, aes(x=log10(Mean+1), by=Species, color=Species))+ stat_ecdf()+ scale_color_brewer(palette = "Dark2") + labs(title="log10(Mean Count) for filtered PAS",y="CDF")
wilcox.test(BothMeanVar$MeanCountHuman, BothMeanVar$MeanCountChimp, paired = F, alternative = "greater")
Wilcoxon rank sum test with continuity correction
data: BothMeanVar$MeanCountHuman and BothMeanVar$MeanCountChimp
W = 912300000, p-value = 9.989e-07
alternative hypothesis: true location shift is greater than 0
ggplot(BothVar, aes(x=log10(Var+1), by=Species, color=Species))+ stat_ecdf()+ scale_color_brewer(palette = "Dark2") + labs(title="log10(Mean Variance) for filtered PAS",y="CDF")
wilcox.test(BothMeanVar$VarCountHuman, BothMeanVar$VarCountChimp, paired = F, alternative = "greater")
Wilcoxon rank sum test with continuity correction
data: BothMeanVar$VarCountHuman and BothMeanVar$VarCountChimp
W = 950190000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0
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 tidyverse_1.2.1
[9] workflowr_1.6.0 ggpubr_0.2 magrittr_1.5 ggplot2_3.1.1
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 lazyeval_0.2.1
[40] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[43] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[46] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10
[49] R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[52] compiler_3.5.1