Annotate Misprime 5

Last updated: 2020-03-17

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffUsedIntronic.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/SpliceSiteStrength.Rmd
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    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dAPA_Conservation.Rmd
    Modified:   analysis/dAPAandapaQTL_DF.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/speciesSpecific_DF.Rmd
    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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
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("../../Misprime5/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 3 rows [15638,
15639, 29662].

chimpPAS=read.table("../../Misprime5/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 3 rows [15638,
15639, 29662].

#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     6.443764  6.05819475  5.82015611 5.205365 5.0439112 6.0912578
A1BG-AS1 3.353314  3.78668815  3.81073174 3.707961 3.6513639 3.8497508
A2M      2.607938 -1.14092782 -0.07816457 2.899468 0.4889356 0.4692655
A4GALT   5.981302  0.01379802  3.29800674 3.544280 4.3166717 5.2000647
AAAS     4.715913  3.95133641  4.93427642 4.988961 4.6795877 3.5391225
AACS     6.343223  7.48809094  5.78828425 7.405831 6.6569218 6.6812210
         X18358_N   X3622_N  X3659_N    X4973_N    pt30_N    pt91_N
A1BG     6.899221 4.6773733 6.260313 3.73015308 5.8807040 5.3194387
A1BG-AS1 4.464245 2.0158040 4.084674 0.85826135 1.9258047 1.3569853
A2M      6.462972 7.2817428 7.577862 5.93175804 5.8054717 5.4261728
A4GALT   4.158615 0.2742697 4.289366 0.05110918 0.2115019 0.2869079
AAAS     4.202347 4.6450485 4.921127 4.35444076 4.5667845 4.2655914
AACS     5.889165 5.2855900 5.219599 5.42568689 5.3436104 5.2557383

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     6.370902 6.257451 5.929396 5.158670 4.904017 6.281830 6.902487
A1BG-AS1 3.230779 3.952103 3.888678 3.632762 3.483316 4.008156 4.446334
AAAS     4.627953 4.121401 5.034817 4.939758 4.534757 3.687583 4.179217
AACS     6.269892 7.692831 5.897296 7.371196 6.528527 6.874654 5.887587
AAGAB    6.672972 5.789335 5.803418 6.081565 5.933779 4.956627 6.485470
AAK1     7.011049 6.754618 7.178833 6.314863 6.749323 7.175921 7.198713
          X3622_N  X3659_N   X4973_N   pt30_N   pt91_N
A1BG     4.899988 6.303489 3.8448870 6.103834 5.427732
A1BG-AS1 2.110061 4.101315 0.6581398 1.997471 1.236156
AAAS     4.867140 4.952822 4.4848105 4.775110 4.358174
AACS     5.516113 5.254852 5.5710316 5.562264 5.363376
AAGAB    6.178375 6.114760 5.8991585 5.898914 5.895488
AAK1     6.314552 7.009573 7.6682811 6.256145 6.405569

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] 8883

GenesCutoffDF=as.data.frame(GenesCutoff) %>% rename("genes"=GenesCutoff)
#mkdir ../data/OverlapBenchmark
write.table(GenesCutoffDF,"../../Misprime5/data/OverlapBenchmark/genesPassingCuttoff.txt", col.names = T, row.names = F,quote = F)

Filter PAS on these genes and 5%

HumanAnno=read.table("../../Misprime5/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("../../Misprime5/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("../../Misprime5/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("../../Misprime5/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, "../../Misprime5/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, "../../Misprime5/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,"../../Misprime5/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 613 rows containing non-finite values (stat_boxplot).

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

write.table(ChimpUsage_anno_5perc, "../../Misprime5/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, "../../Misprime5/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

Annotate Misprime 5

Briana Mittleman

3/17/2020

Annotate and make phenotype

Expression cutoff.

Filter PAS on these genes and 5%