Differential Expression

Last updated: 2019-12-08

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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    Untracked:  data/Pheno_5perc/
    Untracked:  data/Pheno_5perc_nuclear/
    Untracked:  data/Pheno_5perc_total/
    Untracked:  data/RNASEQ_metadata.txt
    Untracked:  data/RNASEQ_metadata_2Removed.txt
    Untracked:  data/RNASEQ_metadata_2Removed.xlsx
    Untracked:  data/RNASEQ_metadata_stranded.txt
    Untracked:  data/RNASEQ_metadata_stranded.xlsx
    Untracked:  data/TwoBadSampleAnalysis/
    Untracked:  data/chainFiles/
    Untracked:  data/cleanPeaks_anno/
    Untracked:  data/cleanPeaks_byspecies/
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    Untracked:  data/metadata_HCpanel.txt
    Untracked:  data/metadata_HCpanel.xlsx
    Untracked:  data/primaryLift/
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    Untracked:  data/~$RNASEQ_metadata.xlsx
    Untracked:  data/~$metadata_HCpanel.xlsx
    Untracked:  data/.xlsx
    Untracked:  output/dtPlots/
    Untracked:  projectNotes.Rmd

Unstaged changes:
    Modified:   analysis/CorrbetweenInd.Rmd
    Modified:   analysis/PASnumperSpecies.Rmd
    Modified:   analysis/annotatePAS.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/diffSplicing.Rmd
    Modified:   analysis/liftoverPAS.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.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	f04a804	brimittleman	2019-12-08	add DE vs DS analysis
html	2c9b5b4	brimittleman	2019-12-06	Build site.
Rmd	6d93e4d	brimittleman	2019-12-06	update stranded
html	a288a29	brimittleman	2019-12-04	Build site.
Rmd	558b39f	brimittleman	2019-12-04	add current error for splice and write out DE genes
html	8fca47f	brimittleman	2019-11-22	Build site.
Rmd	f13781e	brimittleman	2019-11-22	fixed mapping and indivs
html	25971ed	brimittleman	2019-11-21	Build site.
Rmd	db0484c	brimittleman	2019-11-21	add PC corr
html	712106e	brimittleman	2019-11-19	Build site.
Rmd	8dc9ea0	brimittleman	2019-11-19	first pipeline for de
html	586c9ec	brimittleman	2019-11-13	Build site.
Rmd	bedfa41	brimittleman	2019-11-13	question PCA methods
html	a22bae9	brimittleman	2019-11-13	Build site.
Rmd	a52c26d	brimittleman	2019-11-13	look at pca and tech factors
html	da4bab0	brimittleman	2019-11-12	Build site.
Rmd	98d7f9b	brimittleman	2019-11-12	add cpm pca
html	32b435b	brimittleman	2019-11-12	Build site.
Rmd	1ce8433	brimittleman	2019-11-12	start normalization
html	2c02d70	brimittleman	2019-11-12	Build site.
Rmd	53642f7	brimittleman	2019-11-12	add mapp stats
html	dc91b0a	brimittleman	2019-11-11	Build site.
Rmd	b5ba82e	brimittleman	2019-11-11	add diff expression and diff splicing

library(workflowr)

This is workflowr version 1.5.0
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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✔ 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("scales")


Attaching package: 'scales'

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library("gplots")


Attaching package: 'gplots'

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library("edgeR")

Loading required package: limma

library("R.utils")

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Attaching package: 'R.oo'

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    getClasses, getMethods

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library("limma")
library("VennDiagram")

Loading required package: grid

Loading required package: futile.logger

library("RColorBrewer")
library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

For this analysis I do preprocessing with the Snakemake pipeline. The snakemake will map the RNA seq and quantify orthologous exons.

From FastQC:

Does not look like there is adapter contamination
No reads tagged as bad quality

Assess mapping:

metaData=read.table("../data/RNASEQ_metadata_stranded.txt", header = T, stringsAsFactors = F)
metaData$Species=as.factor(metaData$Species)
metaData$Collection=as.factor(metaData$Collection)

readInfo=metaData %>% mutate(AAUnMapped= Reads-Mapped, ABNotOrtho= Mapped-AssignedOrtho) %>% select(Line, Species, AAUnMapped, ABNotOrtho, AssignedOrtho) %>%  gather(key="Category", value="Number", -Line, -Species)


ggplot(readInfo, aes(x=Line,y=Number, fill=Category)) + geom_bar(stat="identity") + scale_fill_brewer(palette = "Dark2",name = "Type", labels = c("Unmapped", "Mapped not ortho", "Assigned Ortho Exon"))+theme(axis.text.x = element_text( hjust = 0,vjust = 1, size = 6, angle = 90)) + labs(y="Reads", title="Human and chimp read statistics")

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
32b435b	brimittleman	2019-11-12
2c02d70	brimittleman	2019-11-12

Proportion of reads.

readProp=metaData %>% mutate(Aunmapped=1-percentMapped, MappednotOrtho=percentMapped-percentOrtho) %>% select(Line,Species, percentOrtho, MappednotOrtho, Aunmapped) %>%  gather(key="Category", value="Proportion", -Line, -Species)

ggplot(readProp, aes(x=Line,y=Proportion, fill=Category)) + geom_bar(stat="identity") + scale_fill_brewer(palette = "Dark2", name="", labels = c("Unmapped", "Mapped not ortho", "Assigned Ortho Exon"))+theme(axis.text.x = element_text( hjust = 0,vjust = 1, size = 6, angle = 90)) + labs(y="Reads", title="Human and chimp read proportions")

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
32b435b	brimittleman	2019-11-12
2c02d70	brimittleman	2019-11-12

By species:

ggplot(readInfo,aes(x=Category, y=Number, by=Species, fill=Species)) + geom_boxplot() +scale_x_discrete( breaks=c("AAUnMapped","ABNotOrtho","AssignedOrtho"),labels=c("Unmapped", "Not in OrthoExon", "Assigned to OrthoExon")) + scale_fill_brewer(palette = "Dark2") + labs(title="Mapped reads by Species", y="Reads", x="")

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
2c02d70	brimittleman	2019-11-12

ggplot(readProp,aes(x=Category, y=Proportion, by=Species, fill=Species)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + labs(title="Map Proportion by Species", y="Proportion", x="") + scale_x_discrete( breaks=c("Aunmapped","MappedNotOrtho","percentOrtho"),labels=c("Unmapped", "Not in OrthoExon", "Assigned to OrthoExon"))

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
2c02d70	brimittleman	2019-11-12

Diffferential Expression

Code originally from Lauren Blake (http://lauren-blake.github.io/Reg_Evo_Primates/analysis/Normalization_plots.html)

Raw Counts

Fix header for fc files:

python fixExonFC.py /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/ExonCounts/RNAseqOrthoExon.fc /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc

python fixExonFC.py /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/ExonCounts/RNAseqOrthoExon.fc /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc

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

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


counts_genes=HumanCounts %>% inner_join(ChimpCounts,by="Geneid") %>% column_to_rownames(var="Geneid")

head(counts_genes)

                NA18498 NA18504 NA18510 NA18523 NA18499 NA18502 NA4973
ENSG00000188976      48      24      50      31      34      58      2
ENSG00000188157     148     106      59     106     128      39      6
ENSG00000273443      69      40      43      54      48      11     24
ENSG00000217801      77      60      36     164      61      19     62
ENSG00000237330       1       0       1       0       1       1      0
ENSG00000223823       0       0       0       0       0       0      0
                NAPT30 NAPT91 NA3622 NA3659 NA18358
ENSG00000188976      2      1      1      1       0
ENSG00000188157      5      7      7      8       6
ENSG00000273443     21      2      3     78      16
ENSG00000217801     34      8     19    139      31
ENSG00000237330      0      0      0      2       0
ENSG00000223823      0      0      0      0       0

# Load colors 

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=" ")

#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)
  }
}

# Clustering (original code from Julien Roux)
cors <- cor(counts_genes, 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
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
32b435b	brimittleman	2019-11-12

select <- counts_genes
summary(apply(select, 1, var) == 0)

   Mode   FALSE    TRUE 
logical   32592   11533

# Perform PCA

pca_genes <- prcomp(t(counts_genes), scale = F)
scores <- pca_genes$x


#Make PCA plots with the factors colored by species

### PCs 1 and 2 Raw Data
for (n in 1:1){
  col.v <- pal[as.integer(metaData$Species)]
  plot_scores(pca_genes, scores, n, n+1, col.v)
}

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
32b435b	brimittleman	2019-11-12

### PCs 3 and 4 Raw Data

for (n in 3:3){
  col.v <- pal[as.integer(metaData$Species)]
  plot_scores(pca_genes, scores, n, n+1, col.v)
}

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
32b435b	brimittleman	2019-11-12

Plot density for raw data:

density_plot_18504 <- ggplot(counts_genes, aes(x = NA18504)) + geom_density() + labs(title = "Density plot of raw gene counts of NA18504") + labs(x = "Raw counts for each gene")

density_plot_18504

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
32b435b	brimittleman	2019-11-12

Convert to log2

log_counts_genes <- as.data.frame(log2(counts_genes))
head(log_counts_genes)

                 NA18498  NA18504  NA18510  NA18523  NA18499  NA18502
ENSG00000188976 5.584963 4.584963 5.643856 4.954196 5.087463 5.857981
ENSG00000188157 7.209453 6.727920 5.882643 6.727920 7.000000 5.285402
ENSG00000273443 6.108524 5.321928 5.426265 5.754888 5.584963 3.459432
ENSG00000217801 6.266787 5.906891 5.169925 7.357552 5.930737 4.247928
ENSG00000237330 0.000000     -Inf 0.000000     -Inf 0.000000 0.000000
ENSG00000223823     -Inf     -Inf     -Inf     -Inf     -Inf     -Inf
                  NA4973   NAPT30   NAPT91   NA3622   NA3659  NA18358
ENSG00000188976 1.000000 1.000000 0.000000 0.000000 0.000000     -Inf
ENSG00000188157 2.584963 2.321928 2.807355 2.807355 3.000000 2.584963
ENSG00000273443 4.584963 4.392317 1.000000 1.584963 6.285402 4.000000
ENSG00000217801 5.954196 5.087463 3.000000 4.247928 7.118941 4.954196
ENSG00000237330     -Inf     -Inf     -Inf     -Inf 1.000000     -Inf
ENSG00000223823     -Inf     -Inf     -Inf     -Inf     -Inf     -Inf

density_plot_18504 <- ggplot(log_counts_genes, aes(x = 18504)) + geom_density()

density_plot_18504 + labs(title = "Density plot of log2 counts of 18504") + labs(x = "Log2 counts for each gene") + geom_vline(xintercept = 1)

Version	Author	Date
32b435b	brimittleman	2019-11-12

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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
32b435b	brimittleman	2019-11-12

Convert to CPM

cpm <- cpm(counts_genes, log=TRUE)
head(cpm)

                  NA18498    NA18504   NA18510   NA18523   NA18499
ENSG00000188976  1.588820  0.9875278  1.619299  1.208070  1.067263
ENSG00000188157  3.172567  3.0589636  1.849178  2.925953  2.915718
ENSG00000273443  2.094172  1.6879541  1.411153  1.975405  1.539652
ENSG00000217801  2.248076  2.2542940  1.167830  3.547237  1.872256
ENSG00000237330 -2.441803 -3.0119508 -2.450600 -3.011951 -2.458357
ENSG00000223823 -3.011951 -3.0119508 -3.011951 -3.011951 -3.011951
                   NA18502     NA4973     NAPT30     NAPT91     NA3622
ENSG00000188976  1.8417218 -2.1227985 -2.0245732 -2.4061117 -2.4360702
ENSG00000188157  1.2932493 -1.1815889 -1.2226619 -0.7937756 -0.8633065
ENSG00000273443 -0.3579863  0.4766993  0.4884775 -1.9807482 -1.7064115
ENSG00000217801  0.3306162  1.7649277  1.1340383 -0.6404174  0.3555815
ENSG00000237330 -2.4451184 -3.0119508 -3.0119508 -3.0119508 -3.0119508
ENSG00000223823 -3.0119508 -3.0119508 -3.0119508 -3.0119508 -3.0119508
                   NA3659    NA18358
ENSG00000188976 -2.483011 -3.0119508
ENSG00000188157 -0.828322 -0.8091641
ENSG00000273443  2.139573  0.3953756
ENSG00000217801  2.955188  1.2822287
ENSG00000237330 -2.096822 -3.0119508
ENSG00000223823 -3.011951 -3.0119508

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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
32b435b	brimittleman	2019-11-12

Log2 CPM

TMM/log2(CPM)

## Create edgeR object (dge) to calculate TMM normalization  
dge_original <- DGEList(counts=as.matrix(counts_genes), genes=rownames(counts_genes), 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)

                  NA18498    NA18504   NA18510   NA18523   NA18499
ENSG00000188976  1.588820  0.9875278  1.619299  1.208070  1.067263
ENSG00000188157  3.172567  3.0589636  1.849178  2.925953  2.915718
ENSG00000273443  2.094172  1.6879541  1.411153  1.975405  1.539652
ENSG00000217801  2.248076  2.2542940  1.167830  3.547237  1.872256
ENSG00000237330 -2.441803 -3.0119508 -2.450600 -3.011951 -2.458357
ENSG00000223823 -3.011951 -3.0119508 -3.011951 -3.011951 -3.011951
                   NA18502     NA4973     NAPT30     NAPT91     NA3622
ENSG00000188976  1.8417218 -2.1227985 -2.0245732 -2.4061117 -2.4360702
ENSG00000188157  1.2932493 -1.1815889 -1.2226619 -0.7937756 -0.8633065
ENSG00000273443 -0.3579863  0.4766993  0.4884775 -1.9807482 -1.7064115
ENSG00000217801  0.3306162  1.7649277  1.1340383 -0.6404174  0.3555815
ENSG00000237330 -2.4451184 -3.0119508 -3.0119508 -3.0119508 -3.0119508
ENSG00000223823 -3.0119508 -3.0119508 -3.0119508 -3.0119508 -3.0119508
                   NA3659    NA18358
ENSG00000188976 -2.483011 -3.0119508
ENSG00000188157 -0.828322 -0.8091641
ENSG00000273443  2.139573  0.3953756
ENSG00000217801  2.955188  1.2822287
ENSG00000237330 -2.096822 -3.0119508
ENSG00000223823 -3.011951 -3.0119508

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
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12
32b435b	brimittleman	2019-11-12

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12

# Plot library size

boxplot_library_size <- ggplot(dge_original$samples, aes(x=metaData$Species, y = dge_original$samples$lib.size, fill = metaData$Species)) + geom_boxplot()
 
boxplot_library_size + labs(title = "Library size by Species") + labs(y = "Library size") + labs(x = "Species") + guides(fill=guide_legend(title="Species"))

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12
32b435b	brimittleman	2019-11-12

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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12
32b435b	brimittleman	2019-11-12

Filter low expressed gene

Filter based on log2 cpm

filter log2(cpm >1) in at least 10 of the samples (2/3)

#filter counts
keep.exprs=rowSums(tmm_cpm>1) >8

counts_filtered= counts_genes[keep.exprs,]




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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12

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)

                 NA18498  NA18504    NA18510  NA18523  NA18499   NA18502
ENSG00000217801 2.256506 2.234115  1.0781627 3.660723 1.851408 0.2044122
ENSG00000186891 6.449667 5.009505  5.0323421 4.887082 5.704206 3.3563261
ENSG00000186827 5.245211 3.093190  4.6496753 1.700807 3.965561 1.8966473
ENSG00000078808 7.201153 6.859702  7.0260235 7.463228 6.617502 6.7324642
ENSG00000176022 4.796263 4.753597  4.6761916 4.790014 4.605178 4.6953254
ENSG00000184163 1.692492 1.418453 -0.1695685 1.630775 1.054722 0.3468831
                  NA4973    NAPT30    NAPT91    NA3622   NA3659  NA18358
ENSG00000217801 1.778862 1.1607241 -0.791047 0.2757475 2.998563 1.300872
ENSG00000186891 6.669085 4.6336365  3.159521 4.7270793 6.447786 6.482146
ENSG00000186827 1.778862 0.2523816  2.006912 3.1506928 0.646502 2.175264
ENSG00000078808 7.024471 6.6878664  6.765572 6.7760951 6.783817 7.061063
ENSG00000176022 5.263508 4.8384442  5.550276 4.8453842 5.276059 5.561721
ENSG00000184163 1.381411 2.8988501  2.195679 1.4096039 1.766196 3.222359

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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12

Voom transformation:

Species <- factor(metaData$Species)
design <- model.matrix(~ 0 + Species)
head(design)

  SpeciesChimp SpeciesHuman
1            1            0
2            1            0
3            1            0
4            1            0
5            1            0
6            1            0

colnames(design) <- gsub("Species", "", dput(colnames(design)))

c("SpeciesChimp", "SpeciesHuman")

Voom creates a random effect.

# Voom with individual as a random variable

cpm.voom<- voom(counts_filtered, design, normalize.method="quantile", plot=T)

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12
32b435b	brimittleman	2019-11-12

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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12
32b435b	brimittleman	2019-11-12

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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12
32b435b	brimittleman	2019-11-12

Looks like i still have a skew on the lower side of the distribution.

# PCA 

pca_genes <- prcomp(t(cpm.voom$E), scale = T)
scores <- pca_genes$x


eigsGene <- pca_genes$sdev^2
proportionG = eigsGene/sum(eigsGene)

plot(proportionG)

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13
da4bab0	brimittleman	2019-11-12
32b435b	brimittleman	2019-11-12

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
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13
da4bab0	brimittleman	2019-11-12

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

#Clustering (original code from Julien Roux)
cors <- cor(cpm.voom$E, 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$Species))], RowSideColors=pal[as.integer(as.factor(metaData$Species))+9], cexCol = 0.2 + 1/log10(15), cexRow = 0.2 + 1/log10(15))

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
da4bab0	brimittleman	2019-11-12

One thing I can do is look at the correlation between the PCs and other factors in the data.

# PCA 

pca_genes <- prcomp(t(cpm.voom$E), scale = F)
scores <- pca_genes$x

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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

metaData$Extraction=as.factor(metaData$Extraction)

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

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

It does not look like batch (who collected or extraction date batch)

cols = brewer.pal(9, "Blues")
palC = colorRampPalette(cols)


metaData$UndilutedAverageorder = findInterval(metaData$UndilutedAverage, sort(metaData$UndilutedAverage))
for (n in 1:2){
  col.v <- palC(nrow(metaData))[metaData$UndilutedAverageorder]
  plot_scores(pca_genes, scores, n, n+1, col.v)
}

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

metaData$BioAConcorder = findInterval(metaData$BioAConc, sort(metaData$BioAConc))
for (n in 1:2){
  col.v <- palC(nrow(metaData))[metaData$BioAConcorder]
  plot_scores(pca_genes, scores, n, n+1, col.v)
}

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

metaData$RinConcorder = findInterval(metaData$Rin, sort(metaData$Rin))
for (n in 1:2){
  col.v <- palC(nrow(metaData))[metaData$RinConcorder]
  plot_scores(pca_genes, scores, n, n+1, col.v)
}

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

The samples do not cluster by collection concentration, RNA rin score or RNA concentration.

metaData$AssignedOrthoorder = findInterval(metaData$AssignedOrtho, sort(metaData$AssignedOrtho))
for (n in 1:2){
  col.v <- palC(nrow(metaData))[metaData$AssignedOrthoorder]
  plot_scores(pca_genes, scores, n, n+1, col.v)
}

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

They also do not cluster by number of reads mapping to ortho exons.

PCA heatmap: Code from Michelle Ward:

x.pca <- pca_genes

tech_factors <- metaData
tech_factors_sum <- tech_factors[,c(2:15)] %>% select(-CollectionDate)

p_comps <- 1:6
pc_cov_cor <- matrix(nrow = ncol(tech_factors_sum), ncol = length(p_comps),
                     dimnames = list(colnames(tech_factors_sum), colnames(x.pca$x)[p_comps]))
for (pc in p_comps) {
  for (covariate in 1:ncol(tech_factors_sum)) {
    lm_result <- lm(x.pca$x[, pc] ~ tech_factors_sum[, covariate])
    r2 <- summary(lm_result)$r.squared
    pc_cov_cor[covariate, pc] <- r2
  }
}

pc_cov_pval <- matrix(nrow = ncol(tech_factors_sum), ncol = length(p_comps),
                      dimnames = list(colnames(tech_factors_sum), colnames(x.pca$x)[p_comps]))

for (pc in p_comps) {
  for (covariate_2 in 1:ncol(tech_factors_sum)) {
    lm_result_2 <- lm(x.pca$x[, pc] ~ tech_factors_sum[, covariate_2])
    pval <- anova(lm_result_2)$'Pr(>F)'[1]
    pc_cov_pval[covariate_2, pc] <- pval
  }
}

PCs <- c("PC1", "PC2", "PC3", "PC4", "PC5", "PC6")
Tech_fac <- colnames(tech_factors_sum)
#Tech_fac <- c("Species",   "Individual", "O2.",  "Condition" , "Sex", "RIN" , "CO2", "Purity_high", "Purity_med" ,
              #"Expt_Batch", "RNA_Batch", "Library_Batch", "Seq_pool", "Episomal_integration" )

heatmap.2(as.matrix(pc_cov_cor[Tech_fac,PCs]),col=brewer.pal(4, "Greens"), trace="none",
          Rowv=FALSE, Colv=FALSE, key=T, main="Cor. PCs & tech factors", dendrogram="none",
          key.title=NA, cexRow=0.9, cexCol=0.9)

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

log10_pc_cov_pval <- -log(pc_cov_pval)
heatmap.2(as.matrix(log10_pc_cov_pval[Tech_fac,PCs]), col=brewer.pal(9, "Greens"), trace="none",
          Rowv=FALSE, Colv=FALSE, key=T, main="-log10 pval of cor. PCs & tech factors", dendrogram="none",
          key.title=NA, cexRow=0.9, cexCol=0.9)

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21

Test for DE

fit.cpm.voom = lmFit(cpm.voom, design, plot=T)
head(coef(fit.cpm.voom))

                    Chimp    Human
ENSG00000217801 1.8181277 1.090565
ENSG00000186891 5.0847804 5.358102
ENSG00000186827 3.4803867 1.530705
ENSG00000078808 6.9813729 6.845371
ENSG00000176022 4.7019733 5.222465
ENSG00000184163 0.8714861 2.121312

contr <- makeContrasts(Chimp - Human, levels = colnames(coef(fit.cpm.voom)))
contr

       Contrasts
Levels  Chimp - Human
  Chimp             1
  Human            -1

tmp <- contrasts.fit(fit.cpm.voom, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
head(top.table, 20)

                    logFC  AveExpr         t      P.Value    adj.P.Val
ENSG00000133112  6.030526 8.579365  53.43162 6.149849e-16 4.099890e-12
ENSG00000204463 -6.207223 5.290526 -51.05863 1.072784e-15 4.099890e-12
ENSG00000205531 -5.980793 5.093652 -49.92171 1.413391e-15 4.099890e-12
ENSG00000105372  4.929862 7.949106  49.41909 1.599801e-15 4.099890e-12
ENSG00000142937  5.675315 8.225710  46.60155 3.281606e-15 6.727948e-12
ENSG00000145741  4.945314 6.308328  43.27471 8.118680e-15 1.387077e-11
ENSG00000142541  6.136435 6.806316  40.37221 1.896383e-14 2.777117e-11
ENSG00000100316  5.939356 8.286237  39.35544 2.589428e-14 3.318029e-11
ENSG00000072864 -6.888911 4.645283 -37.85395 4.162991e-14 4.741646e-11
ENSG00000183020 -4.839255 4.196150 -31.80094 3.478039e-13 3.565338e-10
ENSG00000071082  5.701659 6.557932  29.78081 7.722054e-13 7.196252e-10
ENSG00000147604 -6.185695 5.466412 -27.74841 1.820354e-12 1.555037e-09
ENSG00000116478  3.371308 5.843304  27.55764 1.979085e-12 1.560584e-09
ENSG00000186298  3.080858 6.879040  27.10999 2.413402e-12 1.767127e-09
ENSG00000198242  4.180714 4.991847  25.31780 5.520552e-12 3.772745e-09
ENSG00000105640 -2.840101 8.171072 -24.34587 8.856795e-12 5.619929e-09
ENSG00000197958  4.359628 7.193296  24.24324 9.319949e-12 5.619929e-09
ENSG00000198918 -5.732348 5.424144 -23.80278 1.162663e-11 6.621363e-09
ENSG00000198034  6.255072 7.138676  23.21900 1.568224e-11 8.460982e-09
ENSG00000167193 -4.243003 4.135098 -22.95159 1.802961e-11 9.241077e-09
                       B
ENSG00000133112 26.01466
ENSG00000204463 23.65724
ENSG00000205531 23.45459
ENSG00000105372 25.30811
ENSG00000142937 24.71006
ENSG00000145741 23.49375
ENSG00000142541 22.77665
ENSG00000100316 22.98772
ENSG00000072864 20.84202
ENSG00000183020 19.73654
ENSG00000071082 19.71403
ENSG00000147604 18.63627
ENSG00000116478 18.96720
ENSG00000186298 18.86337
ENSG00000198242 17.82095
ENSG00000105640 17.59772
ENSG00000197958 17.53671
ENSG00000198918 17.07923
ENSG00000198034 16.98147
ENSG00000167193 16.58857

length(which(top.table$adj.P.Val < 0.05))

[1] 3764

Make a table to plot:

-log10(bh adjusted pval) vs logFC (log3 fold change)

top.table2=top.table %>% mutate(Species=ifelse(logFC > 1 & adj.P.Val<.05, "Chimp", ifelse(logFC < -1 & adj.P.Val< .05, "Human", "Neither")))
  
  

ggplot(top.table2, aes(x=logFC, y= -log10(adj.P.Val))) + geom_point(aes(col=Species), alpha=.3)

Version	Author	Date
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21

summary(decideTests(tmp))

       Chimp - Human
Down            1883
NotSig          6487
Up              1881

deGenes=as.data.frame(row.names(top.table[top.table$adj.P.Val < 0.05,]))
#mkdir ../data/DiffExpression

write.table(deGenes,"../data/DiffExpression/DE_genes.txt", col.names = F, 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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] reshape2_1.4.3      RColorBrewer_1.1-2  VennDiagram_1.6.20 
 [4] futile.logger_1.4.3 R.utils_2.7.0       R.oo_1.22.0        
 [7] R.methodsS3_1.7.1   edgeR_3.24.0        limma_3.38.2       
[10] gplots_3.0.1        scales_1.0.0        forcats_0.3.0      
[13] stringr_1.3.1       dplyr_0.8.0.1       purrr_0.3.2        
[16] readr_1.3.1         tidyr_0.8.3         tibble_2.1.1       
[19] ggplot2_3.1.1       tidyverse_1.2.1     workflowr_1.5.0    

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           futile.options_1.0.1
[13] backports_1.1.2      evaluate_0.12        httr_1.3.1          
[16] pillar_1.3.1         rlang_0.4.0          lazyeval_0.2.1      
[19] readxl_1.1.0         rstudioapi_0.10      gdata_2.18.0        
[22] whisker_0.3-2        rmarkdown_1.10       labeling_0.3        
[25] munsell_0.5.0        broom_0.5.1          compiler_3.5.1      
[28] httpuv_1.4.5         modelr_0.1.2         pkgconfig_2.0.2     
[31] htmltools_0.3.6      tidyselect_0.2.5     crayon_1.3.4        
[34] withr_2.1.2          later_0.7.5          bitops_1.0-6        
[37] nlme_3.1-137         jsonlite_1.6         gtable_0.2.0        
[40] formatR_1.5          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       lambda.r_1.2.3       tools_3.5.1         
[52] glue_1.3.0           hms_0.4.2            yaml_2.2.0          
[55] colorspace_1.3-2     caTools_1.17.1.1     rvest_0.3.2         
[58] knitr_1.20           haven_1.1.2