Differential Expression

Last updated: 2019-12-04

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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    Untracked:  projectNotes.Rmd

Unstaged changes:
    Modified:   analysis/CorrbetweenInd.Rmd
    Modified:   analysis/PASnumperSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/mapStats.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.Rmd

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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	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  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ 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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    discard

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    col_factor

library("gplots")


Attaching package: 'gplots'

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library("edgeR")

Loading required package: limma

library("R.utils")

Loading required package: R.oo

Loading required package: R.methodsS3

R.methodsS3 v1.7.1 (2016-02-15) successfully loaded. See ?R.methodsS3 for help.

R.oo v1.22.0 (2018-04-21) successfully loaded. See ?R.oo for help.


Attaching package: 'R.oo'

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    getClasses, getMethods

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    attach, detach, gc, load, save

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    extract

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    cat, commandArgs, getOption, inherits, isOpen, parse, warnings

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.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
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
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
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
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
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   32602   11523

# 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
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
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
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
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  1.588860  0.9875607  1.619314  1.208048  1.067299
ENSG00000188157  3.172608  3.0589988  1.849193  2.925931  2.915757
ENSG00000273443  2.094212  1.6879881  1.411168  1.975384  1.539689
ENSG00000217801  2.248116  2.2543286  1.167845  3.547215  1.872294
ENSG00000237330 -2.441797 -3.0119624 -2.450603 -3.011962 -2.458352
ENSG00000223823 -3.011962 -3.0119624 -3.011962 -3.011962 -3.011962
                   NA18502     NA4973     NAPT30     NAPT91     NA3622
ENSG00000188976  1.8417566 -2.1228041 -2.0245721 -2.4061474 -2.4360717
ENSG00000188157  1.2932833 -1.1815912 -1.2226553 -0.7938422 -0.8632943
ENSG00000273443 -0.3579576  0.4766994  0.4884893 -1.9807956 -1.7064048
ENSG00000217801  0.3306479  1.7649286  1.1340509 -0.6404855  0.3555977
ENSG00000237330 -2.4451144 -3.0119624 -3.0119624 -3.0119624 -3.0119624
ENSG00000223823 -3.0119624 -3.0119624 -3.0119624 -3.0119624 -3.0119624
                    NA3659    NA18358
ENSG00000188976 -2.4830271 -3.0119624
ENSG00000188157 -0.8283452 -0.8093695
ENSG00000273443  2.1395468  0.3951397
ENSG00000217801  2.9551621  1.2819822
ENSG00000237330 -2.0968409 -3.0119624
ENSG00000223823 -3.0119624 -3.0119624

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

Version	Author	Date
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  1.588860  0.9875607  1.619314  1.208048  1.067299
ENSG00000188157  3.172608  3.0589988  1.849193  2.925931  2.915757
ENSG00000273443  2.094212  1.6879881  1.411168  1.975384  1.539689
ENSG00000217801  2.248116  2.2543286  1.167845  3.547215  1.872294
ENSG00000237330 -2.441797 -3.0119624 -2.450603 -3.011962 -2.458352
ENSG00000223823 -3.011962 -3.0119624 -3.011962 -3.011962 -3.011962
                   NA18502     NA4973     NAPT30     NAPT91     NA3622
ENSG00000188976  1.8417566 -2.1228041 -2.0245721 -2.4061474 -2.4360717
ENSG00000188157  1.2932833 -1.1815912 -1.2226553 -0.7938422 -0.8632943
ENSG00000273443 -0.3579576  0.4766994  0.4884893 -1.9807956 -1.7064048
ENSG00000217801  0.3306479  1.7649286  1.1340509 -0.6404855  0.3555977
ENSG00000237330 -2.4451144 -3.0119624 -3.0119624 -3.0119624 -3.0119624
ENSG00000223823 -3.0119624 -3.0119624 -3.0119624 -3.0119624 -3.0119624
                    NA3659    NA18358
ENSG00000188976 -2.4830271 -3.0119624
ENSG00000188157 -0.8283452 -0.8093695
ENSG00000273443  2.1395468  0.3951397
ENSG00000217801  2.9551621  1.2819822
ENSG00000237330 -2.0968409 -3.0119624
ENSG00000223823 -3.0119624 -3.0119624

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

Version	Author	Date
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
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
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
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 2.256718 2.234174  1.0782676 3.660677 1.851409 0.2045780
ENSG00000186891 6.449880 5.009564  5.0298795 4.887036 5.699309 3.3564937
ENSG00000186827 5.245424 3.093249  4.6497808 1.700761 3.965562 1.8968145
ENSG00000078808 7.201366 6.859761  7.0261290 7.463811 6.617502 6.7318204
ENSG00000176022 4.796475 4.753656  4.6762970 4.789968 4.605179 4.6954931
ENSG00000184163 1.692704 1.418512 -0.1694645 1.630729 1.054723 0.3470491
                  NA4973    NAPT30     NAPT91   NA3622    NA3659  NA18358
ENSG00000217801 1.778595 1.1604206 -0.7907848 0.275830 2.9986515 1.300800
ENSG00000186891 6.668817 4.6333309  3.1597896 4.727162 6.4478748 6.482073
ENSG00000186827 1.778595 0.2520802  2.0071797 3.150776 0.6465898 2.175191
ENSG00000078808 7.024203 6.6875607  6.7658411 6.776178 6.7839057 7.060989
ENSG00000176022 5.263240 4.8315395  5.5505445 4.845467 5.2761471 5.561648
ENSG00000184163 1.381145 2.8985450  2.1959470 1.409687 1.7662843 3.222286

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

Version	Author	Date
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
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
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
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
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
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13
da4bab0	brimittleman	2019-11-12

Version	Author	Date
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
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
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
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
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
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
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
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
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
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
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
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
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21
a22bae9	brimittleman	2019-11-13

Version	Author	Date
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
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
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 1.8239316 1.096239
ENSG00000186891 5.0834054 5.358053
ENSG00000186827 3.4807377 1.529885
ENSG00000078808 6.9813425 6.845201
ENSG00000176022 4.7020373 5.221061
ENSG00000184163 0.8723989 2.120493

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  6.030511 8.579411  53.42599 6.164514e-16 3.580859e-12
ENSG00000204463 -6.206595 5.290833 -51.12264 1.057583e-15 3.580859e-12
ENSG00000205531 -5.979561 5.093742 -50.01988 1.381262e-15 3.580859e-12
ENSG00000105372  4.911162 7.958402  49.97123 1.397818e-15 3.580859e-12
ENSG00000142937  5.675106 8.225847  46.54479 3.334322e-15 6.833359e-12
ENSG00000145741  4.945035 6.308921  43.30669 8.053676e-15 1.375434e-11
ENSG00000142541  6.131464 6.808892  40.44806 1.855178e-14 2.715716e-11
ENSG00000100316  5.939257 8.286283  39.35669 2.590875e-14 3.318587e-11
ENSG00000072864 -6.885850 4.647008 -37.94264 4.049551e-14 4.610639e-11
ENSG00000183020 -4.840796 4.195607 -31.83651 3.433979e-13 3.518798e-10
ENSG00000071082  5.702735 6.557358  29.78317 7.720885e-13 7.192356e-10
ENSG00000147604 -6.188588 5.465040 -27.93667 1.678431e-12 1.433240e-09
ENSG00000116478  3.371012 5.843517  27.59385 1.949359e-12 1.536545e-09
ENSG00000186298  3.080919 6.879067  27.07869 2.449289e-12 1.792705e-09
ENSG00000198242  4.185597 4.987658  25.28558 5.610300e-12 3.832583e-09
ENSG00000105640 -2.839830 8.171195 -24.34695 8.858449e-12 5.573342e-09
ENSG00000197958  4.358474 7.193875  24.26064 9.246297e-12 5.573342e-09
ENSG00000198918 -5.743655 5.418473 -23.86031 1.130114e-11 6.433487e-09
ENSG00000198034  6.255369 7.138553  23.22182 1.567032e-11 8.451252e-09
ENSG00000167193 -4.241868 4.135580 -22.96405 1.792463e-11 9.183685e-09
                       B
ENSG00000133112 26.01294
ENSG00000204463 23.68152
ENSG00000205531 23.48417
ENSG00000105372 25.41614
ENSG00000142937 24.69753
ENSG00000145741 23.50158
ENSG00000142541 22.79732
ENSG00000100316 22.98735
ENSG00000072864 20.88166
ENSG00000183020 19.75642
ENSG00000071082 19.71471
ENSG00000147604 18.70807
ENSG00000116478 18.98168
ENSG00000186298 18.84917
ENSG00000198242 17.80741
ENSG00000105640 17.59757
ENSG00000197958 17.54457
ENSG00000198918 17.10523
ENSG00000198034 16.98239
ENSG00000167193 16.59832

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

[1] 3761

Make a table to plot:

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

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

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

Version	Author	Date
8fca47f	brimittleman	2019-11-22
25971ed	brimittleman	2019-11-21

summary(decideTests(tmp))

       Chimp - Human
Down            1881
NotSig          6486
Up              1880

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