Last updated: 2020-01-21

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
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    Modified:   analysis/speciesSpecific.Rmd

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File Version Author Date Message
Rmd 117fd63 brimittleman 2020-01-22 redo differential analysis with double filt 
library(workflowr)
This is workflowr version 1.5.0
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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I want to write a loop that will create boxplots for all of the PAS in each gene. I need a script that takes one gene and then outputs the plot.

I want PAS:Location as the x axis. Usage as the y axis. I will seperate the plots by species.

I need a long dataframe with species, individual, PASid, gene, usage.

Pull in the usage numbers:

../data/Pheno_5perc_nuclear/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Nuclear.txt.gz

I need to make these numeric.

mkdir ../data/files4viz_nuclear_DF/

Vector with humans:

humans=c('NA18498', 'NA18499', 'NA18502' ,'NA18504', 'NA18510', 'NA18523')

chimps=c( 'NA18358', 'NA3622' ,'NA3659', 'NA4973', 'NApt30', 'NApt91')

LOC100288069_+_utr3-Both-human23

LOC107984841_+_utr3-Chimp-chimp242477 utr3-Chimp-chimp242477

I will double filter here as well.

NuclearUsage=read.table("../data/Pheno_5perc_nuclear/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Nuclear.txt.gz", header = T)

PASMeta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::rename("PASnum" = PAS)

Nuclear_gather=NuclearUsage %>% gather(key="Ind", value="Frac", -chrom) %>% separate(Frac, into=c("num", "denom"), sep="/") %>% mutate(numUsage=as.integer(num)/as.integer(denom)) %>% replace_na(list(numUsage=0)) %>% separate(Ind, into=c("sample", "fraction"), by="_") %>% mutate(species=ifelse(sample %in% humans, "Human","Chimp")) %>% separate(chrom, into=c("chr","start","end","PASid"), sep=":")%>% separate(PASid, into=c("Gene", "strand", "PASinf"), sep="_") %>% separate(PASinf, into=c("loc", "disc", "PASnum"), sep="-") %>% filter(PASnum %in% PASMeta$PASnum) %>% mutate(PAS=paste(PASnum,loc,sep="_")) %>% dplyr::select(chr,start,end, strand, PAS, species,Gene, sample,numUsage)
Warning: attributes are not identical across measure variables;
they will be dropped

example gene:
DFFB

Nuclear_gather_DFFB=Nuclear_gather %>% filter(Gene=="DFFB")

Nuclear_gather_DFFB$start=as.integer(Nuclear_gather_DFFB$start)


ggplot(Nuclear_gather_DFFB,aes(x=(reorder(PAS, start)), y=numUsage, by=species, fill=species))+ geom_boxplot() + scale_fill_brewer(palette = "Dark2")+theme(axis.text.x = element_text(angle = 90)) + labs(y="Usage", title="Usage by species for DFFB",x="PAS")

write.table(Nuclear_gather,"../data/files4viz_nuclear_DF/NuclearPASUsage.txt", col.names = T, row.names = F, quote = F)

I will put this in an R script that takes a gene.

test on DFFB

Rscript PlotNuclearUsagebySpecies_DF.R -g DFFB

This works, now I can make it for every gene that was called as DAPA. I will make a list of all of the genes.

genes are in ../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt

sed '1d' ../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt > ../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear_noHead.txt

sbatch makeNuclearDapaplots_DF.sh

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_1.5.0

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