Differential Expression

Last updated: 2020-01-13

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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/investigatePantro5.Rmd
    Modified:   analysis/multiMap.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	1121102	brimittleman	2020-01-13	add dpau and de overlap with dom intronic
html	326f6e2	brimittleman	2020-01-10	Build site.
Rmd	61bcd28	brimittleman	2020-01-10	prepare DE volcano
html	e5dff29	brimittleman	2020-01-06	Build site.
Rmd	767ca26	brimittleman	2020-01-06	add eQTL enrichment for eQTLs
html	c453931	brimittleman	2020-01-05	Build site.
Rmd	894a755	brimittleman	2020-01-05	update DE new Orthoexon file
html	3ee8382	brimittleman	2019-12-08	Build site.
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 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
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✔ tidyr   0.8.3       ✔ stringr 1.3.1  
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── Conflicts ───────────────────────────────────────────────── tidyverse_conflicts() ──
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library("scales")


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library("gplots")


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library("edgeR")

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library("R.utils")

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

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library("RColorBrewer")
library(reshape2)


Attaching package: 'reshape2'

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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) %>% dplyr::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
c453931	brimittleman	2020-01-05
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) %>% dplyr::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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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) %>% 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") %>% column_to_rownames(var="Geneid")

head(counts_genes)

                NA18498 NA18504 NA18510 NA18523 NA18499 NA18502 NA4973
ENSG00000000003       0       3       4       1       2       2     13
ENSG00000000005       0       0       0       0       0       0      0
ENSG00000000419     911     742    1202     867     979    1023    580
ENSG00000000457     529     421     613     408     623     555    716
ENSG00000000460     448     287     545     159     560     481    546
ENSG00000000938    5186    3676    7197   12054    2916    3675   2865
                NAPT30 NAPT91 NA3622 NA3659 NA18358
ENSG00000000003      3      1     18     13       0
ENSG00000000005      0      0      0      0       0
ENSG00000000419    451    533    530    563     501
ENSG00000000457    656    905    712    727     531
ENSG00000000460    410    237    510    526     245
ENSG00000000938    230    369    427   2307    3742

# 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
c453931	brimittleman	2020-01-05
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   18209    1164

# 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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
ENSG00000000003      -Inf  1.584963  2.000000  0.000000  1.000000
ENSG00000000005      -Inf      -Inf      -Inf      -Inf      -Inf
ENSG00000000419  9.831307  9.535275 10.231221  9.759888  9.935165
ENSG00000000457  9.047124  8.717676  9.259743  8.672425  9.283088
ENSG00000000460  8.807355  8.164907  9.090112  7.312883  9.129283
ENSG00000000938 12.340406 11.843921 12.813180 13.557224 11.509775
                  NA18502    NA4973   NAPT30   NAPT91   NA3622    NA3659
ENSG00000000003  1.000000  3.700440 1.584963 0.000000 4.169925  3.700440
ENSG00000000005      -Inf      -Inf     -Inf     -Inf     -Inf      -Inf
ENSG00000000419  9.998590  9.179909 8.816984 9.057992 9.049849  9.136991
ENSG00000000457  9.116344  9.483816 9.357552 9.821774 9.475733  9.505812
ENSG00000000460  8.909893  9.092757 8.679480 7.888743 8.994353  9.038919
ENSG00000000938 11.843529 11.484319 7.845490 8.527477 8.738092 11.171802
                  NA18358
ENSG00000000003      -Inf
ENSG00000000005      -Inf
ENSG00000000419  8.968667
ENSG00000000457  9.052568
ENSG00000000460  7.936638
ENSG00000000938 11.869594

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
c453931	brimittleman	2020-01-05
32b435b	brimittleman	2019-11-12

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

Version	Author	Date
c453931	brimittleman	2020-01-05
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
ENSG00000000003 -3.214119 -1.687803 -1.678332 -2.557786 -2.259918
ENSG00000000005 -3.214119 -3.214119 -3.214119 -3.214119 -3.214119
ENSG00000000419  5.579570  5.650403  5.945220  5.752976  5.631088
ENSG00000000457  4.797732  4.835162  4.976165  4.668752  4.980802
ENSG00000000460  4.558974  4.284933  4.807150  3.318750  4.827551
ENSG00000000938  8.085987  7.956576  8.525076  9.547634  7.203612
                  NA18502    NA4973    NAPT30    NAPT91      NA3622
ENSG00000000003 -2.244077 -0.508794 -1.907969 -2.610028  0.08159357
ENSG00000000005 -3.214119 -3.214119 -3.214119 -3.214119 -3.21411905
ENSG00000000419  5.726859  4.736398  4.582972  4.905709  4.81211618
ENSG00000000457  4.847086  5.039196  5.121512  5.667358  5.23658578
ENSG00000000460  4.641466  4.649609  4.446117  3.742924  4.75683807
ENSG00000000938  7.569677  7.036149  3.617698  4.377498  4.50169416
                    NA3659   NA18358
ENSG00000000003 -0.4625636 -3.214119
ENSG00000000005 -3.2141190 -3.214119
ENSG00000000419  4.7478795  5.024297
ENSG00000000457  5.1153942  5.107928
ENSG00000000460  4.6502143  3.997251
ENSG00000000938  6.7783101  7.921081

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

Version	Author	Date
c453931	brimittleman	2020-01-05
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
ENSG00000000003 -3.214119 -1.687803 -1.678332 -2.557786 -2.259918
ENSG00000000005 -3.214119 -3.214119 -3.214119 -3.214119 -3.214119
ENSG00000000419  5.579570  5.650403  5.945220  5.752976  5.631088
ENSG00000000457  4.797732  4.835162  4.976165  4.668752  4.980802
ENSG00000000460  4.558974  4.284933  4.807150  3.318750  4.827551
ENSG00000000938  8.085987  7.956576  8.525076  9.547634  7.203612
                  NA18502    NA4973    NAPT30    NAPT91      NA3622
ENSG00000000003 -2.244077 -0.508794 -1.907969 -2.610028  0.08159357
ENSG00000000005 -3.214119 -3.214119 -3.214119 -3.214119 -3.21411905
ENSG00000000419  5.726859  4.736398  4.582972  4.905709  4.81211618
ENSG00000000457  4.847086  5.039196  5.121512  5.667358  5.23658578
ENSG00000000460  4.641466  4.649609  4.446117  3.742924  4.75683807
ENSG00000000938  7.569677  7.036149  3.617698  4.377498  4.50169416
                    NA3659   NA18358
ENSG00000000003 -0.4625636 -3.214119
ENSG00000000005 -3.2141190 -3.214119
ENSG00000000419  4.7478795  5.024297
ENSG00000000457  5.1153942  5.107928
ENSG00000000460  4.6502143  3.997251
ENSG00000000938  6.7783101  7.921081

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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
ENSG00000000419 5.605914 5.653296 5.916152 5.845268 5.644714 5.728449
ENSG00000000457 4.822027 4.836001 4.944992 4.758197 4.992866 4.846521
ENSG00000000460 4.582386 4.283559 4.775443 3.399813 4.839132 4.640177
ENSG00000000938 8.114674 7.961624 8.497834 9.642281 7.219058 7.573114
ENSG00000001036 5.888276 5.743706 5.726636 5.927711 5.758006 5.063235
ENSG00000001084 4.763617 4.061338 4.530956 5.004519 3.991180 4.092598
                  NA4973   NAPT30   NAPT91   NA3622   NA3659  NA18358
ENSG00000000419 4.776243 4.670620 5.016289 4.838002 4.798818 5.096826
ENSG00000000457 5.080011 5.210943 5.779817 5.263708 5.167477 5.180694
ENSG00000000460 4.689137 4.533195 3.847813 4.782534 4.700797 4.065408
ENSG00000000938 7.080071 3.699882 4.486049 4.526414 6.833086 7.997246
ENSG00000001036 4.068473 3.761218 4.405707 3.489676 4.878534 5.234014
ENSG00000001084 5.065843 4.396833 4.623827 4.751093 4.730640 4.640130

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

Version	Author	Date
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
c453931	brimittleman	2020-01-05
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)] %>% dplyr::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
c453931	brimittleman	2020-01-05
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
c453931	brimittleman	2020-01-05
2c9b5b4	brimittleman	2019-12-06
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21

Plot PVE by each PC:

eigs <- pca_genes$sdev^2
proportion = eigs/sum(eigs)

plot(proportion, main="PVE by each PC", xlab="PC")

Version	Author	Date
c453931	brimittleman	2020-01-05

proportion[4]

[1] 0.09109573

Test for DE

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

                   Chimp    Human
ENSG00000000419 5.729563 4.863479
ENSG00000000457 4.862504 5.280516
ENSG00000000460 4.452581 4.461012
ENSG00000000938 8.146155 5.749239
ENSG00000001036 5.682363 4.278239
ENSG00000001084 4.398539 4.711799

testgenes= rownames(coef(fit.cpm.voom))

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
ENSG00000105372  5.630358 8.110130  59.52351 1.485694e-16 1.508722e-12
ENSG00000204463 -6.197558 5.041060 -45.74372 3.777266e-15 1.142820e-11
ENSG00000205531 -5.940439 5.011790 -45.62134 3.903519e-15 1.142820e-11
ENSG00000142937  5.589075 8.074735  45.09454 4.501505e-15 1.142820e-11
ENSG00000137818  7.114468 6.794576  43.68229 6.651200e-15 1.350859e-11
ENSG00000071082  5.413729 7.296312  35.45376 8.574484e-14 1.451231e-10
ENSG00000186298  3.352417 6.680918  32.22943 2.748792e-13 3.974110e-10
ENSG00000148303  4.640610 7.595442  31.88765 3.130762e-13 3.974110e-10
ENSG00000116478  3.694113 6.385183  30.76256 4.852201e-13 5.474900e-10
ENSG00000147604 -7.064441 5.783547 -30.38644 5.637299e-13 5.724678e-10
ENSG00000088038 -4.999454 3.948451 -29.72131 7.382438e-13 6.815333e-10
ENSG00000072864 -6.857923 4.399064 -29.44551 8.270260e-13 6.998707e-10
ENSG00000183020 -5.130305 4.151035 -29.13161 9.423275e-13 7.361028e-10
ENSG00000128731 -3.587995 5.679996 -28.54326 1.207989e-12 8.762231e-10
ENSG00000161654 -3.666330 4.603805 -27.92183 1.578929e-12 1.040331e-09
ENSG00000179950 -5.983934 4.402584 -27.78236 1.678068e-12 1.040331e-09
ENSG00000167615 -6.313264 4.881549 -27.69762 1.741569e-12 1.040331e-09
ENSG00000145741  2.897300 7.633998  26.95215 2.426347e-12 1.368864e-09
ENSG00000105640 -2.953215 8.019379 -25.12854 5.677714e-12 2.925081e-09
ENSG00000049656 -2.675713 5.440187 -25.09842 5.760868e-12 2.925081e-09
                       B
ENSG00000105372 27.35947
ENSG00000204463 22.47175
ENSG00000205531 22.58641
ENSG00000142937 24.66798
ENSG00000137818 23.42239
ENSG00000071082 21.98589
ENSG00000186298 20.97083
ENSG00000148303 20.85556
ENSG00000116478 20.39436
ENSG00000147604 19.44754
ENSG00000088038 18.72823
ENSG00000072864 18.27882
ENSG00000183020 18.65049
ENSG00000128731 19.44355
ENSG00000161654 18.88385
ENSG00000179950 18.10333
ENSG00000167615 18.27649
ENSG00000145741 18.88448
ENSG00000105640 18.03812
ENSG00000049656 17.99535

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

[1] 3796

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) + scale_color_brewer(palette = "Dark2") + labs(title="LCL differential Expression")

Version	Author	Date
326f6e2	brimittleman	2020-01-10

summary(decideTests(tmp))

       Chimp - Human
Down            1895
NotSig          6359
Up              1901

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

geneNames=as.data.frame(row.names(top.table))
colnames(geneNames)="genes"
deGenes_witheffect= cbind(geneNames,top.table) %>% filter(adj.P.Val < 0.05)
tested_witheffect=cbind(geneNames,top.table)

write.table(deGenes,"../data/DiffExpression/DE_genes.txt", col.names = F, row.names = F, quote = F)
write.table(tested_witheffect,"../data/DiffExpression/DEtested_allres.txt", col.names = F, row.names = F, quote = F)
write.table(deGenes_witheffect,"../data/DiffExpression/DE_results5fdr.txt", col.names = T, row.names = F, quote = F)
write.table(testgenes,"../data/DiffExpression/DE_Testedgenes.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