Last updated: 2020-03-17

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/upsetter_DF.Rmd

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.


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Rmd bec8ae9 brimittleman 2020-03-17 add mis filter annotation and pheno

library(tidyverse)
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Annotate and make phenotype

I am recreating the code from the annotation Like the PAS liftover, R code is from the compapa directory and bash code is done in the specific misprime directory.

mkdir ../data/cleanPeaks_anno
bedtools map -a ../data/cleanPeaks_lifted/AllPAS_postLift.sort.bed -b  /project2/gilad/briana/genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_Formatted_Allannotation.sort.bed -c 4 -S -o distinct > ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed 

cp ../../Comparative_APA/code/chooseAnno2Bed.py .

python chooseAnno2Bed.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed  ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed 

cp ../../Misprime4/code/LiftOrthoPAS2chimp.sh . #new dir  ../../Comparative_APA/data/chainFiles/

sbatch LiftOrthoPAS2chimp.sh

cp ../../Comparative_APA/code/bed2SAF_gen.py .

python bed2SAF_gen.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed  ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.SAF

python bed2SAF_gen.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED_chimpLoc.bed  ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED_chimpLoc.SAF


mkdir  ../Human/data/CleanLiftedPeaks_FC/
mkdir ../Chimp/data/CleanLiftedPeaks_FC/

cp ../../Comparative_APA/code/quantLiftedPAS.sh .


sbatch quantLiftedPAS.sh 


cp ../../Comparative_APA/code/fixFChead_bothfrac.py .


python fixFChead_bothfrac.py ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc


python fixFChead_bothfrac.py ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc

#make file ID
cp ../../Comparative_APA/code/makeFileID.py .

python makeFileID.py ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp ../Chimp/data/CleanLiftedPeaks_FC/ChimpFileID.txt

python makeFileID.py ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt


mkdir ../Human/data/phenotype/
mkdir ../Chimp/data/phenotype/

cp ../../Comparative_APA/code/makePheno.py .

python makePheno.py  ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt

python makePheno.py  ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc ../Chimp/data/CleanLiftedPeaks_FC/ChimpFileID.txt ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt


cp ../../Comparative_APA/code/pheno2countonly.R .

Rscript pheno2countonly.R -I ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt -O ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt

Rscript pheno2countonly.R -I ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt -O ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnly.txt

cp ../../Comparative_APA/code/convertNumeric.py .

python convertNumeric.py ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt

python convertNumeric.py ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnly.txt ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt

Expression cutoff.

I will use the same cutoff as I used in the original data.

humanPAS=read.table("../../Misprime6/Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc", header=T, stringsAsFactors = F) %>% 
  separate(Geneid, into=c("disc","PAS","chrom", "start","end","strand","geneid"), sep=":") %>%
  separate(geneid,into=c("gene","loc"),sep="_") %>%
  dplyr::select(gene,contains("_N")) %>%
  gather(key="ind", value="count", -gene) %>% 
  group_by(ind, gene) %>%
  summarize(GeneCount=sum(count)) %>% 
  spread(ind, GeneCount)
Warning: Expected 2 pieces. Additional pieces discarded in 2 rows [27653,
52819].
chimpPAS=read.table("../../Misprime6/Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc", header=T, stringsAsFactors = F) %>% 
  separate(Geneid, into=c("disc","PAS","chrom", "start","end","strand","geneid"), sep=":") %>%
  separate(geneid,into=c("gene","loc"),sep="_") %>%
  dplyr::select(gene,contains("_N")) %>%
  gather(key="ind", value="count", -gene) %>% 
  group_by(ind, gene) %>%
  summarize(GeneCount=sum(count)) %>% 
  spread(ind, GeneCount)
Warning: Expected 2 pieces. Additional pieces discarded in 2 rows [27653,
52819].
#can use the same meta becuase it is ordered the same  
metadata=read.table("../data/metadata_HCpanel.txt",header = T) %>% mutate(id2=ifelse(grepl("pt", ID), ID, paste("X", ID, sep=""))) %>% filter(Fraction=="Nuclear")

order=c(metadata$id2[1:10], "pt30_N", "pt91_N")

BothbyGene= chimpPAS %>% inner_join(humanPAS,by="gene") %>% dplyr::select(gene,order)

#count matrix:
Genematrix=as.matrix(BothbyGene %>% column_to_rownames(var="gene"))
colors <- colorRampPalette(c(brewer.pal(9, "Blues")[1],brewer.pal(9, "Blues")[9]))(100)

pal <- c(brewer.pal(9, "Set1"), brewer.pal(8, "Set2"), brewer.pal(12, "Set3"))
labels <- paste(metadata$Species,metadata$Line, sep=" ")

# Clustering (original code from Julien Roux)
cors <- cor(Genematrix, method="spearman", use="pairwise.complete.obs")


heatmap.2( cors, scale="none", col = colors, margins = c(12, 12), trace='none', denscol="white", labCol=labels, ColSideColors=pal[as.integer(as.factor(metadata$Species))], RowSideColors=pal[as.integer(as.factor(metadata$Collection))+9], cexCol = 0.2 + 1/log10(15), cexRow = 0.2 + 1/log10(15))

log_counts_genes <- as.data.frame(log2(Genematrix))
plotDensities(log_counts_genes, col=pal[as.numeric(metadata$Species)], legend="topright")

cpm <- cpm(Genematrix, log=TRUE)
plotDensities(cpm, col=pal[as.numeric(metadata$Species)], legend="topright")

## Create edgeR object (dge) to calculate TMM normalization  
dge_original <- DGEList(counts=as.matrix(Genematrix), genes=rownames(Genematrix), group = as.character(t(labels)))
dge_original <- calcNormFactors(dge_original)

tmm_cpm <- cpm(dge_original, normalized.lib.sizes=TRUE, log=TRUE, prior.count = 0.25)
head(cpm)
         X18498_N   X18499_N   X18502_N X18504_N    X18510_N   X18523_N
A1BG     5.983536  5.4183778  5.4098495 4.663320  4.48324922 5.62423862
A1BG-AS1 4.071747  3.4978452  4.1234882 3.262501  3.72249613 3.57914708
A2M      2.142925 -1.5743299 -0.4993838 2.312121 -0.03058551 0.01319229
A4GALT   5.543397 -0.5366276  2.9226146 3.063796  3.91933502 4.74234267
AAAS     4.417342  3.6273229  4.6991559 5.099780  4.44115086 3.65822441
AACS     6.288806  7.4218057  5.7257025 7.208370  6.62098854 6.61684685
         X18358_N    X3622_N  X3659_N    X4973_N     pt30_N     pt91_N
A1BG     6.457101  4.3145111 5.870401  3.4115203  5.5275684  4.9061884
A1BG-AS1 4.355529  2.0046092 3.869027  0.9238313  1.8067853  1.2271781
A2M      6.023206  6.9373362 7.220784  5.6298885  5.4570591  5.0627709
A4GALT   3.710202 -0.1138492 3.898710 -0.3143677 -0.1748582 -0.1380633
AAAS     4.039505  4.4757803 4.758339  4.2295650  4.5165243  4.1312581
AACS     6.002210  5.3341952 5.224647  5.4353906  5.3247138  5.2857423

log2cpm plot

plotDensities(tmm_cpm, col=pal[as.numeric(metadata$Species)], legend="topright")

keep.exprs=rowSums(tmm_cpm>2) >8

counts_filtered= Genematrix[keep.exprs,]




plotDensities(counts_filtered, col=pal[as.numeric(metadata$Species)], legend="topright")

labels <- paste(metadata$Species, metadata$Line, sep=" ")
dge_in_cutoff <- DGEList(counts=as.matrix(counts_filtered), genes=rownames(counts_filtered), group = as.character(t(labels)))
dge_in_cutoff <- calcNormFactors(dge_in_cutoff)

cpm_in_cutoff <- cpm(dge_in_cutoff, normalized.lib.sizes=TRUE, log=TRUE, prior.count = 0.25)
head(cpm_in_cutoff)
         X18498_N X18499_N X18502_N X18504_N X18510_N X18523_N X18358_N
A1BG     5.957948 5.443869 5.490546 4.616405 4.354045 5.783591 6.492871
A1BG-AS1 4.027659 3.495425 4.189664 3.187911 3.580187 3.711004 4.375357
AAAS     4.378646 3.628186 4.773447 5.057221 4.311390 3.792034 4.054206
AACS     6.264487 7.454726 5.808372 7.175235 6.506229 6.780525 6.036193
AAGAB    6.732123 5.570277 5.833618 6.075778 5.897792 5.243105 6.296532
AAK1     7.239137 7.075132 7.262894 6.601016 6.875455 7.462096 7.348393
          X3622_N  X3659_N   X4973_N   pt30_N   pt91_N
A1BG     4.517128 5.929125 3.5521872 5.756699 5.035251
A1BG-AS1 2.117826 3.905766 0.8602056 1.916882 1.175998
AAAS     4.680711 4.808601 4.3880096 4.736102 4.250081
AACS     5.547867 5.279265 5.6068936 5.552428 5.418120
AAGAB    6.048714 6.023814 5.8432555 5.820213 5.804474
AAK1     6.456939 6.907735 7.8154504 6.295812 6.384961
GenesCutoff=rownames(cpm_in_cutoff)
NormalizedGenesCuttoff=as.data.frame(cbind(Gene_stable_ID=GenesCutoff, cpm_in_cutoff))
hist(cpm_in_cutoff, xlab = "Log2(CPM)", main = "Log2(CPM) values for genes meeting the filtering criteria", breaks = 100 )

Species <- factor(metadata$Species)
design <- model.matrix(~ 0 + Species)
head(design)
  SpeciesChimp SpeciesHuman
1            0            1
2            0            1
3            0            1
4            0            1
5            0            1
6            0            1
colnames(design) <- gsub("Species", "", dput(colnames(design)))
c("SpeciesChimp", "SpeciesHuman")
cpm.voom<- voom(counts_filtered, design, normalize.method="quantile", plot=T)

boxplot(cpm.voom$E, col = pal[as.numeric(metadata$Species)],las=2)

plotDensities(cpm.voom, col =  pal[as.numeric(metadata$Species)], legend = "topleft") 

length(GenesCutoff)
[1] 9428
GenesCutoffDF=as.data.frame(GenesCutoff) %>% rename("genes"=GenesCutoff)
#mkdir ../data/OverlapBenchmark
write.table(GenesCutoffDF,"../../Misprime6/data/OverlapBenchmark/genesPassingCuttoff.txt", col.names = T, row.names = F,quote = F)

Filter PAS on these genes and 5%

HumanAnno=read.table("../../Misprime6/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("../../Misprime6/Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH)

HumanMean=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsage)))

HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage ))
ChimpAnno=read.table("../../Misprime6/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("../../Misprime6/Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC)

ChimpMean=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsage)))

ChimpUsage_anno=as.data.frame(cbind(ChimpAnno[,1:8],ChimpUsage ))
BothMean=ChimpMean %>% full_join(HumanMean, by=c("chr","start","end","gene"   ,"strand", "loc", "disc","PAS" )) 

BothMeanM=melt(BothMean,id.vars =c("chr","start","end","gene"   ,"strand", "loc", "disc","PAS" ),variable.name = "Species", value.name = "MeanUsage" ) %>% filter(loc !="008559", loc != "009911")
ggplot(BothMeanM, aes(x=loc, y=MeanUsage,by=Species,fill=Species)) + geom_boxplot()  + scale_fill_brewer(palette = "Dark2")

ggplot(BothMeanM, aes(x=MeanUsage,by=Species,col=Species)) + stat_ecdf(geom = "point", alpha=.25)  + scale_color_brewer(palette = "Dark2") + labs(title="Cumulative Distribution plot for PAS Usage", x="Mean Usage- both fractions", y="F(Mean Usage)")

Implement cutoffs for gene expression and usage.

BothMean_5= BothMean %>% filter(Chimp >=0.05 | Human >= 0.05,gene %in% GenesCutoffDF$genes)  
BothMean_5M=melt(BothMean_5,id.vars =c("chr","start","end","gene"   ,"strand", "loc", "disc","PAS" ),variable.name = "Species", value.name = "MeanUsage" ) %>% filter(loc !="008559")

ggplot(BothMean_5M, aes(x=loc, y=MeanUsage,by=Species,fill=Species)) + geom_boxplot()  + scale_fill_brewer(palette = "Dark2")

ggplot(BothMean_5M, aes(x=MeanUsage,by=Species,col=Species)) + stat_ecdf(geom = "point", alpha=.25)  + scale_color_brewer(palette = "Dark2") + labs(title="Cumulative Distribution plot for PAS Usage at 5%", x="Mean Usage- both fractions", y="F(Mean Usage)") 

ggplot(BothMean_5M, aes(x=MeanUsage,by=Species,fill=Species)) + geom_histogram(alpha=.5, bins=30, position = "dodge")  + scale_fill_brewer(palette = "Dark2")

mkdir ../data/Peaks_5perc
mkdir ../data/Pheno_5perc
BothMean_5_out=BothMean_5 %>% dplyr::select(PAS,disc, gene, loc,chr, start, end,Chimp, Human)
write.table(BothMean_5_out, "../../Misprime6/data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", row.names = F, col.names = T, quote = F)

BothMean_5_out_noUN=BothMean_5 %>% dplyr::select(PAS,disc, gene, loc,chr, start, end,Chimp, Human) %>% filter(!grepl("Un",chr))

write.table(BothMean_5_out_noUN, "../../Misprime6/data/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.txt", row.names = F, col.names = T, quote = F)
#write bed with human coord for igv
BothMean_5_bed=BothMean_5 %>% dplyr::select(chr, start, end, PAS, Human, strand)
write.table(BothMean_5_bed,"../../Misprime6/data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed", row.names = F, col.names = T, quote = F)

ggplot(BothMean_5_out, aes(x=disc, fill=disc))+  geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2")

BothMean_5_outmean= BothMean_5_out %>% mutate(meanUsage=(Human+Chimp)/2)
ggplot(BothMean_5M, aes(x=disc, by= Species, fill=Species, y=MeanUsage)) + geom_boxplot() + scale_y_log10()+ scale_fill_brewer(palette = "Dark2")
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Removed 516 rows containing non-finite values (stat_boxplot).

ChimpUsage_anno_5perc= ChimpUsage_anno %>% filter(PAS %in% BothMean_5$PAS)

write.table(ChimpUsage_anno_5perc, "../../Misprime6/data/Pheno_5perc/Chimp_Pheno_5perc.txt", row.names = F, col.names = T, quote = F)

HumaUsage_anno_5perc= HumanUsage_anno %>% filter(PAS %in% BothMean_5$PAS)

write.table(HumaUsage_anno_5perc, "../../Misprime6/data/Pheno_5perc/Human_Pheno_5perc.txt", row.names = F, col.names = T, quote = F)

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] reshape2_1.4.3     RColorBrewer_1.1-2 scales_1.0.0      
 [4] edgeR_3.24.0       limma_3.38.2       R.utils_2.7.0     
 [7] R.oo_1.22.0        R.methodsS3_1.7.1  gplots_3.0.1      
[10] forcats_0.3.0      stringr_1.3.1      dplyr_0.8.0.1     
[13] purrr_0.3.2        readr_1.3.1        tidyr_0.8.3       
[16] tibble_2.1.1       ggplot2_3.1.1      tidyverse_1.2.1   

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2         locfit_1.5-9.1     lubridate_1.7.4   
 [4] lattice_0.20-38    gtools_3.8.1       assertthat_0.2.0  
 [7] rprojroot_1.3-2    digest_0.6.18      R6_2.3.0          
[10] cellranger_1.1.0   plyr_1.8.4         backports_1.1.2   
[13] evaluate_0.12      httr_1.3.1         pillar_1.3.1      
[16] rlang_0.4.0        lazyeval_0.2.1     readxl_1.1.0      
[19] rstudioapi_0.10    gdata_2.18.0       whisker_0.3-2     
[22] rmarkdown_1.10     labeling_0.3       munsell_0.5.0     
[25] broom_0.5.1        compiler_3.5.1     httpuv_1.4.5      
[28] modelr_0.1.2       pkgconfig_2.0.2    htmltools_0.3.6   
[31] tidyselect_0.2.5   workflowr_1.6.0    crayon_1.3.4      
[34] withr_2.1.2        later_0.7.5        bitops_1.0-6      
[37] grid_3.5.1         nlme_3.1-137       jsonlite_1.6      
[40] gtable_0.2.0       git2r_0.26.1       magrittr_1.5      
[43] KernSmooth_2.23-15 cli_1.1.0          stringi_1.2.4     
[46] fs_1.3.1           promises_1.0.1     xml2_1.2.0        
[49] generics_0.0.2     tools_3.5.1        glue_1.3.0        
[52] hms_0.4.2          yaml_2.2.0         colorspace_1.3-2  
[55] caTools_1.17.1.1   rvest_0.3.2        knitr_1.20        
[58] haven_1.1.2