Normalize Usage Phenotype

Last updated: 2019-10-10

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Knit directory: Comparative_APA/analysis/

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File	Version	Author	Date	Message
Rmd	9855436	brimittleman	2019-10-10	fix code for pve
html	d0c98c2	brimittleman	2019-10-09	Build site.
Rmd	14a3f66	brimittleman	2019-10-09	add pca and human v chimp in nuc analysis

I want to normalize the phenotypes with the leafcutter scripts. This can be used to perform a PCA and assess the data quality. I will include, total nuclear human and chimp.

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(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

These are the inclusive phenotypes. I will need to subset of the 5% pas.
../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt ../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt

The 5% pas are in ../data/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.txt

I will make a python script that will do this. I

python filter5percPAS.py ../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt  ../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Human_Pheno_5perc.txt

python filter5percPAS.py ../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt  ../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Chimp_Pheno_5perc.txt

Join these to normalize the phenotypes together:

humanPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Human_Pheno_5perc.txt",stringsAsFactors = F, header = T)
chimpPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Chimp_Pheno_5perc.txt",stringsAsFactors = F, header = T)


allPheno=humanPheno %>% full_join(chimpPheno,by="chrom")


write.table(allPheno, "../data/Pheno_5perc/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt", col.names = T, row.names = F, quote = F)

gzip ../data/Pheno_5perc/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt

#conda deactivate 
conda deactivate 
conda deactivate 
#python 2
source ~/activate_anaconda_python2.sh 
#go to directory ../data/Pheno_5perc/
python ../../code/prepare_phenotype_table.py ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt.gz
cat ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt.gz.phen_chr* > ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt.gz.phen_AllChrom

Use these normalized phenotypes for the PCA

metaData=read.table("../data/metadata_HCpanel.txt", header = T, stringsAsFactors = F)

normPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt.gz.phen_AllChrom", col.names = c('Chr', 'start',    'end',  'ID',   '18498_N',  '18498_T',  '18499_N',  '18499_T',  '18502_N',  '18502_T',  '18504_N',  '18504_T',  '18510_N',  '18510_T',  '18523_N',  '18523_T',  '18358_N',  '18358_T',  '3622_N',   '3622_T',   '3659_N',   '3659_T',   '4973_N',   '4973_T',   'pt30_N',   'pt30_T',   'pt91_N',   'pt91_T'))

normPheno_matrix=as.matrix(normPheno %>% select(-Chr, -start, -end, -ID))

Run PCA:

pca_Pheno=prcomp(normPheno_matrix, center=T,scale=T)
pca_df=as.data.frame(pca_Pheno$rotation) %>% rownames_to_column(var="ID")

eigs <- pca_Pheno$sdev^2
proportion = eigs/sum(eigs)

plot(proportion)

Version	Author	Date
d0c98c2	brimittleman	2019-10-09

Plot color as species and shape as total v nuclear

top5PC=pca_df %>% select(ID, PC1, PC2, PC3, PC4, PC5) %>%  inner_join(metaData, by="ID")
top5PC$Cycles=as.factor(top5PC$Cycles)

ggplot(top5PC,aes(x=PC1, y=PC2, col=Species, shape=Fraction)) + geom_point(size=3)

Version	Author	Date
d0c98c2	brimittleman	2019-10-09

ggplot(top5PC,aes(x=PC1, y=PC2, shape=Species,col=Cycles)) + geom_point(size=3) + geom_text(aes(label=Line), position = position_nudge(y = 0.03) )

PC1

Fraction1=summary(lm(top5PC$PC1 ~ top5PC$Fraction))$adj.r.squared
Cycles1=summary(lm(top5PC$PC1 ~ top5PC$Cycles))$adj.r.squared
Species1=summary(lm(top5PC$PC1 ~ top5PC$Species))$adj.r.squared
Concentration1=summary(lm(top5PC$PC1 ~ top5PC$Concentration))$adj.r.squared
Library_concentration1=summary(lm(top5PC$PC1 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean1=summary(lm(top5PC$PC1 ~ top5PC$PerMapClean))$adj.r.squared
Ratio1=summary(lm(top5PC$PC1 ~ top5PC$Ratio))$adj.r.squared



PC1Fac=c(Fraction1, Species1, Concentration1, Ratio1,Cycles1,Library_concentration1, PerMapClean1)

PC2

Fraction2=summary(lm(top5PC$PC2 ~ top5PC$Fraction))$adj.r.squared
Cycles2=summary(lm(top5PC$PC2 ~ top5PC$Cycles))$adj.r.squared
Species2=summary(lm(top5PC$PC2 ~ top5PC$Species))$adj.r.squared
Concentration2=summary(lm(top5PC$PC2 ~ top5PC$Concentration))$adj.r.squared
Library_concentration2=summary(lm(top5PC$PC2 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean2=summary(lm(top5PC$PC2 ~ top5PC$PerMapClean))$adj.r.squared
Ratio2=summary(lm(top5PC$PC2 ~ top5PC$Ratio))$adj.r.squared



PC2Fac=c(Fraction2, Species2, Concentration2, Ratio2,Cycles2,Library_concentration2, PerMapClean2)

PC3

Fraction3=summary(lm(top5PC$PC3 ~ top5PC$Fraction))$adj.r.squared
Cycles3=summary(lm(top5PC$PC3 ~ top5PC$Cycles))$adj.r.squared
Species3=summary(lm(top5PC$PC3 ~ top5PC$Species))$adj.r.squared
Concentration3=summary(lm(top5PC$PC3 ~ top5PC$Concentration))$adj.r.squared
Library_concentration3=summary(lm(top5PC$PC3 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean3=summary(lm(top5PC$PC3 ~ top5PC$PerMapClean))$adj.r.squared
Ratio3=summary(lm(top5PC$PC3 ~ top5PC$Ratio))$adj.r.squared



PC3Fac=c(Fraction3, Species3, Concentration3, Ratio3,Cycles3,Library_concentration3, PerMapClean3)

PC4

Fraction4=summary(lm(top5PC$PC4 ~ top5PC$Fraction))$adj.r.squared
Cycles4=summary(lm(top5PC$PC4 ~ top5PC$Cycles))$adj.r.squared
Species4=summary(lm(top5PC$PC4 ~ top5PC$Species))$adj.r.squared
Concentration4=summary(lm(top5PC$PC4 ~ top5PC$Concentration))$adj.r.squared
Library_concentration4=summary(lm(top5PC$PC4 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean4=summary(lm(top5PC$PC4 ~ top5PC$PerMapClean))$adj.r.squared
Ratio4=summary(lm(top5PC$PC4 ~ top5PC$Ratio))$adj.r.squared



PC4Fac=c(Fraction4,Species4, Concentration4, Ratio4,Cycles4,Library_concentration4, PerMapClean4)

PC5

Fraction5=summary(lm(top5PC$PC5 ~ top5PC$Fraction))$adj.r.squared
Cycles5=summary(lm(top5PC$PC5 ~ top5PC$Cycles))$adj.r.squared
Species5=summary(lm(top5PC$PC5 ~ top5PC$Species))$adj.r.squared
Concentration5=summary(lm(top5PC$PC5 ~ top5PC$Concentration))$adj.r.squared
Library_concentration5=summary(lm(top5PC$PC5 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean5=summary(lm(top5PC$PC5 ~ top5PC$PerMapClean))$adj.r.squared
Ratio5=summary(lm(top5PC$PC5 ~ top5PC$Ratio))$adj.r.squared



PC5Fac=c(Fraction5,Species5, Concentration5, Ratio5,Cycles5,Library_concentration5, PerMapClean5)

Make DF and plot:

Exp=c("Fraction",'Species', 'Concentration', 'Ratio', 'Cycles','Library_concentration', 'PerMapClean')

pcandEx=as.data.frame(cbind(Experiment=Exp,pc1=PC1Fac, pc2=PC2Fac,  pc3=PC3Fac,  pc4=PC4Fac,  pc5=PC5Fac))

pcandExM=melt(pcandEx, id.var="Experiment",variable.name = "PC", value.name = "PVE")

Warning: attributes are not identical across measure variables; they will
be dropped

pcandExM$PVE=as.numeric(pcandExM$PVE)

ggplot(pcandExM, aes(x=PC, fill=PVE, y=Experiment))+ geom_tile() + labs(title="Proportion of variation explained in PCs by Experimental Faction", y="")+ scale_fill_distiller(palette = "Blues", direction=1)

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] ggpubr_0.2      magrittr_1.5    reshape2_1.4.3  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] Rcpp_1.0.2         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] pillar_1.3.1       compiler_3.5.1     git2r_0.25.2      
 [7] plyr_1.8.4         workflowr_1.4.0    tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] modelr_0.1.2       whisker_0.3-2      backports_1.1.2   
[40] scales_1.0.0       htmltools_0.3.6    rvest_0.3.2       
[43] assertthat_0.2.0   colorspace_1.3-2   labeling_0.3      
[46] stringi_1.2.4      lazyeval_0.2.1     munsell_0.5.0     
[49] broom_0.5.1        crayon_1.3.4

Normalize Usage Phenotype

Briana Mittleman

10/9/2019