Last updated: 2020-01-18

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd 5c24c0c brimittleman 2020-01-16 add cutoff code files

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)
This is workflowr version 1.5.0
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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 [48532,
48533, 48534, 92439].
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 [48532,
48533, 48534, 92439].

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[1:10], "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))

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 85 rows containing non-finite values (stat_smooth).

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 X18499_N X18502_N X18504_N X18510_N X18523_N X18358_N
A1BG     8.651052 6.906891 7.826548 6.339850 7.098032 8.184875 9.348728
A1BG-AS1 6.906891 5.209453 6.686501 5.087463 6.491853 6.189825 7.257388
A2M      4.807355 1.584963 1.000000 4.169925 2.000000 2.000000 9.257388
A4GALT   8.348728 3.000000 5.426265 4.807355 6.643856 7.434628 6.629357
AAAS     7.189825 5.285402 7.149747 6.930737 7.169925 6.539159 7.044394
AACS     9.211888 9.084808 8.455327 9.038919 9.721099 9.539159 9.449149
          X3622_N   X3659_N  X4973_N   pt30_N   pt91_N
A1BG     7.055282  8.581201 6.044394 8.379378 7.693487
A1BG-AS1 4.700440  6.658211 3.321928 4.643856 3.807355
A2M      9.881114 10.090112 8.392317 8.588715 8.134426
A4GALT   2.000000  6.714246 1.584963 2.000000 2.000000
AAAS     7.357552  7.599913 6.965784 7.539159 7.139551
AACS     8.607330  8.535275 8.422065 8.562242 8.483816
plotDensities(log_counts_genes, col=pal[as.numeric(metadata$Species)], legend="topright")

CPM

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

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   X18499_N  X18502_N X18504_N  X18510_N   X18523_N
A1BG     5.341373  4.5987121  4.825047 3.911045  3.750998  4.9771400
A1BG-AS1 3.615681  2.9306634  3.698555 2.687829  3.157205  3.0121784
A2M      1.599639 -0.2819609 -1.089067 1.813867 -0.715739 -0.6166667
A4GALT   5.040892  0.8661457  2.472380 2.418347  3.305608  4.2338209
AAAS     3.893949  3.0044537  4.155002 4.494750  3.821733  3.3529391
AACS     5.899659  6.7662826  5.449823 6.591965  6.353974  6.3252109
         X18358_N    X3622_N  X3659_N    X4973_N     pt30_N     pt91_N
A1BG     5.864370  3.8051158 5.332094  2.9593924  5.0306360  4.3368318
A1BG-AS1 3.790872  1.5417267 3.431163  0.4511016  1.4111872  0.6543926
A2M      5.773389  6.6111053 6.835833  5.2737979  5.2386466  4.7735860
A4GALT   3.175424 -0.6529054 3.486064 -0.8218303 -0.7078122 -0.7167300
AAAS     3.581559  4.1030422 4.358541  3.8611894  4.1981495  3.7903055
AACS     5.964420  5.3419658 5.286426  5.3033760  5.2123316  5.1204515

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)
}

Plot the log2cpm

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

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")

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 X18499_N X18502_N X18504_N X18510_N X18523_N X18358_N
A1BG  5.326957 4.625557 4.905208 3.905606 3.662176 5.078928 5.911955
AAAS  3.867579 3.007628 4.229253 4.495543 3.733906 3.435877 3.610390
AACS  5.887453 6.802137 5.533487 6.602282 6.282438 6.432448 6.012328
AAGAB 6.894374 6.534390 6.284803 6.705440 6.723586 5.850635 6.568594
AAK1  7.160975 7.023337 7.174254 6.449108 6.732369 7.292880 7.128880
AAMDC 3.916063 4.446814 4.586804 4.372328 3.306549 3.993492 4.208672
       X3622_N  X3659_N  X4973_N   pt30_N   pt91_N
A1BG  3.935890 5.404934 3.062305 5.215744 4.471971
AAAS  4.237636 4.424620 3.981300 4.376425 3.918928
AACS  5.486113 5.359042 5.435875 5.398472 5.261494
AAGAB 6.133469 6.204851 5.953094 5.993237 5.919381
AAK1  6.450074 6.671772 7.759541 6.213006 6.256908
AAMDC 4.833520 4.743179 5.401851 4.875587 5.343671
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 )

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            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")

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

length(GenesCutoff)
[1] 9819
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_1.5.0   

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