Last updated: 2020-04-10

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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In this analysis I will use feature counts to count all the 3’ seq reads in each gene. I will then use a similar pipeline to the RNA seq to establish a cutoff for normalized expression. This will be used as a data filter on the pas for the humans and chimps.

library(workflowr)
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library("gplots")

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I will sum over all PAS for a psuedo gene count. I will use the full set of PAS before cutting to 5%.

Human nuclear only

humanPAS=read.table("../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 4 rows [50254,
50255, 50256, 95621].
chimpPAS=read.table("../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 4 rows [50254,
50255, 50256, 95621].

Join these together:

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[1:9], "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))

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

Look at the correlation between this and expression:

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F,col.names = c("Geneid","gene","source")) %>% dplyr::select(-source)


HumanCounts=read.table("../Human/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% dplyr::select(-Chr,-Start,-End,-Strand, -Length)

ChimpCounts=read.table("../Chimp/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% dplyr::select(-Chr,-Start,-End,-Strand, -Length)


counts_genes=HumanCounts %>% inner_join(ChimpCounts,by="Geneid") %>% inner_join(nameID, by="Geneid") %>% dplyr::select(-Geneid)

counts_genes_nog=counts_genes %>% dplyr::select(-gene)

ExpMean=as.data.frame(cbind(gene=counts_genes$gene, meanExp=rowMeans(counts_genes_nog)))


ThreeMean=as.data.frame(cbind(gene=BothbyGene$gene, meanThree=rowMeans(Genematrix)))


ExpandThree=ExpMean %>% inner_join(ThreeMean,by="gene")  
Warning: Column `gene` joining factors with different levels, coercing to
character vector

Plot this:

ExpandThree$meanExp=as.numeric(as.character(ExpandThree$meanExp))
ExpandThree$meanThree=as.numeric(as.character(ExpandThree$meanThree))


ggplot(ExpandThree,aes(x=log10(meanExp),y=log10(meanThree)))+ geom_point() + geom_smooth(method="lm")
Warning: Removed 100 rows containing non-finite values (stat_smooth).

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

This looks pretty good. I can treat the psuedo threeprime as expression to find an expression cuttoff. Next I will normalize and standardize the sum gene counts.

Log2

log_counts_genes <- as.data.frame(log2(Genematrix))
head(log_counts_genes)
         X18498_N X18502_N X18504_N X18510_N X18523_N X18358_N  X3622_N
A1BG     8.651052 7.826548 6.339850 7.098032 8.184875 9.348728 7.055282
A1BG-AS1 6.906891 6.686501 5.087463 6.491853 6.189825 7.257388 4.700440
A2M      4.807355 1.000000 4.169925 2.000000 2.000000 9.257388 9.881114
A4GALT   8.581201 5.357552 5.169925 6.658211 7.491853 6.629357 2.000000
AAAS     7.189825 7.179909 6.942515 7.169925 6.539159 7.044394 7.357552
AACS     9.221587 8.459432 9.044394 9.721099 9.541097 9.449149 8.614710
           X3659_N  X4973_N   pt30_N   pt91_N
A1BG      8.581201 6.044394 8.379378 7.693487
A1BG-AS1  6.658211 3.321928 4.643856 3.807355
A2M      10.090112 8.392317 8.588715 8.134426
A4GALT    6.714246 1.584963 2.321928 2.000000
AAAS      7.599913 6.965784 7.539159 7.139551
AACS      8.535275 8.426265 8.566054 8.487840
plotDensities(log_counts_genes, col=pal[as.numeric(metadata$Species)], legend="topright")

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

CPM

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

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

Use log2 cmp:

## 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  X18502_N X18504_N   X18510_N   X18523_N X18358_N
A1BG     5.335975  4.822457 3.906170  3.7427819  4.9692073 5.862578
A1BG-AS1 3.609631  3.695461 2.681919  3.1485736  3.0032387 3.788407
A2M      1.590742 -1.117158 1.806446 -0.7412478 -0.6410892 5.771584
A4GALT   5.266502  2.402040 2.761711  3.3111425  4.2822387 3.172490
AAAS     3.888063  4.181964 4.501799  3.8135554  3.3442842 3.578957
AACS     5.904016  5.451468 6.593208  6.3464373  6.3194213 5.962642
            X3622_N  X3659_N    X4973_N     pt30_N     pt91_N
A1BG      3.8054942 5.331074  2.9655868  5.0404705  4.3394436
A1BG-AS1  1.5386130 3.429301  0.4486671  1.4159232  0.6492242
A2M       6.6122699 6.835012  5.2814572  5.2485416  4.7763689
A4GALT   -0.6719056 3.484245 -0.8392159 -0.5052596 -0.7353850
AAAS      4.1035918 4.357224  3.8682362  4.2076320  3.7926153
AACS      5.3502862 5.285396  5.3152203  5.2260093  5.1273375

Look at a PCA plot of the log2cpm

#PCA function (original code from Julien Roux)
#Load in the plot_scores function
plot_scores <- function(pca, scores, n, m, cols, points=F, pchs =20, legend=F){
  xmin <- min(scores[,n]) - (max(scores[,n]) - min(scores[,n]))*0.05
  if (legend == T){ ## let some room (35%) for a legend                                                                                                                                                 
    xmax <- max(scores[,n]) + (max(scores[,n]) - min(scores[,n]))*0.50
  }
  else {
    xmax <- max(scores[,n]) + (max(scores[,n]) - min(scores[,n]))*0.05
  }
  ymin <- min(scores[,m]) - (max(scores[,m]) - min(scores[,m]))*0.05
  ymax <- max(scores[,m]) + (max(scores[,m]) - min(scores[,m]))*0.05
  plot(scores[,n], scores[,m], xlab=paste("PC", n, ": ", round(summary(pca)$importance[2,n],3)*100, "% variance explained", sep=""), ylab=paste("PC", m, ": ", round(summary(pca)$importance[2,m],3)*100, "% variance explained", sep=""), xlim=c(xmin, xmax), ylim=c(ymin, ymax), type="n")
  if (points == F){
    text(scores[,n],scores[,m], rownames(scores), col=cols, cex=1)
  }
  else {
    points(scores[,n],scores[,m], col=cols, pch=pchs, cex=1.3)
  }
}
pca_genes <- prcomp(t(tmm_cpm), scale = F)
scores <- pca_genes$x

for (n in 1:2){
  col.v <- pal[as.integer(metadata$Species)]
  plot_scores(pca_genes, scores, n, n+1, col.v)
}

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

Plot the log2cpm

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

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

I will need to filter out the lowly expressed genes. This will also be the gene filter I use for the PAS.

Start with log2cpm>2 for 8 of the 12 indiv. Filter the counts

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

counts_filtered= Genematrix[keep.exprs,]




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

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

Make a new dge list and filter:

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 X18502_N X18504_N X18510_N X18523_N X18358_N  X3622_N
A1BG  5.333295 4.866579 3.912586 3.665085 5.113862 5.926445 3.952668
AAAS  3.873846 4.220733 4.514323 3.736821 3.470657 3.624757 4.254438
AACS  5.903499 5.498965 6.614830 6.285449 6.469363 6.026820 5.510350
AAGAB 6.857755 6.171053 6.587252 6.622233 5.844396 6.512657 6.116919
AAK1  7.195236 7.153959 6.480370 6.795415 7.366844 7.197543 6.502524
AAMDC 3.996676 4.617724 4.379339 3.375127 4.038819 4.223092 4.861933
       X3659_N  X4973_N   pt30_N   pt91_N
A1BG  5.390761 3.056792 5.223283 4.480390
AAAS  4.410423 3.975860 4.383929 3.927312
AACS  5.344867 5.434685 5.409826 5.273967
AAGAB 6.151236 5.921039 5.964998 5.889048
AAK1  6.668522 7.780800 6.237815 6.297343
AAMDC 4.728992 5.405045 4.899463 5.363492
GenesCutoff=rownames(cpm_in_cutoff)
NormalizedGenesCuttoff=as.data.frame(cbind(Gene_stable_ID=GenesCutoff, cpm_in_cutoff))

Plot the historgram:

hist(cpm_in_cutoff, xlab = "Log2(CPM)", main = "Log2(CPM) values for genes meeting the filtering criteria", breaks = 100 )

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

This looks relatively normal. I will next look at the voom transformed values with quantile normalization.

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            1            0
colnames(design) <- gsub("Species", "", dput(colnames(design)))
c("SpeciesChimp", "SpeciesHuman")
cpm.voom<- voom(counts_filtered, design, normalize.method="quantile", plot=T)

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18 
boxplot(cpm.voom$E, col = pal[as.numeric(metadata$Species)],las=2)

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18 
plotDensities(cpm.voom, col =  pal[as.numeric(metadata$Species)], legend = "topleft")

Version Author Date
065ffdc brimittleman 2020-04-03
f161386 brimittleman 2020-01-18

This looks like a good cuttoff. I will make a list of the genes that pass the cutoff.

length(GenesCutoff)
[1] 9531
GenesCutoffDF=as.data.frame(GenesCutoff) %>% rename("genes"=GenesCutoff)
#mkdir ../data/OverlapBenchmark
write.table(GenesCutoffDF,"../data/OverlapBenchmark/genesPassingCuttoff.txt", col.names = T, row.names = F,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] RColorBrewer_1.1-2 scales_1.0.0       edgeR_3.24.0      
 [4] limma_3.38.2       forcats_0.3.0      stringr_1.3.1     
 [7] dplyr_0.8.0.1      purrr_0.3.2        readr_1.3.1       
[10] tidyr_0.8.3        tibble_2.1.1       ggplot2_3.1.1     
[13] tidyverse_1.2.1    R.utils_2.7.0      R.oo_1.22.0       
[16] R.methodsS3_1.7.1  gplots_3.0.1       workflowr_1.6.0   

loaded via a namespace (and not attached):
 [1] locfit_1.5-9.1     Rcpp_1.0.2         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   crayon_1.3.4       withr_2.1.2       
[34] later_0.7.5        bitops_1.0-6       grid_3.5.1        
[37] nlme_3.1-137       jsonlite_1.6       gtable_0.2.0      
[40] git2r_0.26.1       magrittr_1.5       KernSmooth_2.23-15
[43] cli_1.1.0          stringi_1.2.4      fs_1.3.1          
[46] promises_1.0.1     xml2_1.2.0         generics_0.0.2    
[49] tools_3.5.1        glue_1.3.0         hms_0.4.2         
[52] yaml_2.2.0         colorspace_1.3-2   caTools_1.17.1.1  
[55] rvest_0.3.2        knitr_1.20         haven_1.1.2