Differential Expression Remove Bad Individuals

Last updated: 2019-11-19

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Knit directory: Comparative_APA/analysis/

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

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File	Version	Author	Date	Message
Rmd	db85b26	brimittleman	2019-11-20	add remove 2 analysis

This is exactly the diffExpression analysis but I will remove 2 individuals that may be hurting the analysis.

These are:

Chimp 4973
Human 18498

library(workflowr)

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library(tidyverse)

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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")
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library("RColorBrewer")
library(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_2Removed.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")

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")

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="")

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"))

Diffferential Expression

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

Raw Counts

HumanCounts=read.table("../Human/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr,-Start,-End,-Strand, -Length, -NA18498)

ChimpCounts=read.table("../Chimp/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr,-Start,-End,-Strand, -Length, -NA4973)


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

head(counts_genes)

                NA18504 NA18510 NA18523 NA18499 NA18502 NAPT30 NAPT91
ENSG00000188976      24      50      31      34      58      2      1
ENSG00000188157     106      65     106     128      39      5      7
ENSG00000273443      40      43      54      48      11     21      2
ENSG00000217801      60      36     164      61      19     34      8
ENSG00000237330       0       1       0       1       1      0      0
ENSG00000223823       0       0       0       0       0      0      0
                NA3622 NA3659 NA18358
ENSG00000188976      1      1       0
ENSG00000188157      7      8       6
ENSG00000273443      3     78      16
ENSG00000217801     19    139      31
ENSG00000237330      0      2       0
ENSG00000223823      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))

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

   Mode   FALSE    TRUE 
logical   31883   12242

# 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)
}

### 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)
}

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

Convert to log2

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

                 NA18504  NA18510  NA18523  NA18499  NA18502   NAPT30
ENSG00000188976 4.584963 5.643856 4.954196 5.087463 5.857981 1.000000
ENSG00000188157 6.727920 6.022368 6.727920 7.000000 5.285402 2.321928
ENSG00000273443 5.321928 5.426265 5.754888 5.584963 3.459432 4.392317
ENSG00000217801 5.906891 5.169925 7.357552 5.930737 4.247928 5.087463
ENSG00000237330     -Inf 0.000000     -Inf 0.000000 0.000000     -Inf
ENSG00000223823     -Inf     -Inf     -Inf     -Inf     -Inf     -Inf
                  NAPT91   NA3622   NA3659  NA18358
ENSG00000188976 0.000000 0.000000 0.000000     -Inf
ENSG00000188157 2.807355 2.807355 3.000000 2.584963
ENSG00000273443 1.000000 1.584963 6.285402 4.000000
ENSG00000217801 3.000000 4.247928 7.118941 4.954196
ENSG00000237330     -Inf     -Inf 1.000000     -Inf
ENSG00000223823     -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)

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

Convert to CPM

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

                   NA18504   NA18510   NA18523   NA18499    NA18502
ENSG00000188976  0.9894703  1.620342  1.209959  1.068977  1.8425919
ENSG00000188157  3.0594442  1.985078  2.926705  2.916115  1.2946137
ENSG00000273443  1.6891590  1.412390  1.976621  1.540857 -0.3532791
ENSG00000217801  2.2551156  1.169332  3.547816  1.873190  0.3334631
ENSG00000237330 -2.9815417 -2.429988 -2.981542 -2.437603 -2.4245831
ENSG00000223823 -2.9815417 -2.981542 -2.981542 -2.981542 -2.9815417
                   NAPT30     NAPT91     NA3622     NA3659    NA18358
ENSG00000188976 -2.008556 -2.3855698 -2.4153553 -2.4615104 -2.9815417
ENSG00000188157 -1.212936 -0.7860576 -0.8558140 -0.8206567 -0.8020982
ENSG00000273443  0.492301 -1.9650589 -1.6935886  2.1415838  0.3987400
ENSG00000217801  1.136923 -0.6333308  0.3592322  2.9568407  1.2842881
ENSG00000237330 -2.981542 -2.9815417 -2.9815417 -2.0800683 -2.9815417
ENSG00000223823 -2.981542 -2.9815417 -2.9815417 -2.9815417 -2.9815417

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

###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)

                   NA18504   NA18510   NA18523   NA18499    NA18502
ENSG00000188976  0.9894703  1.620342  1.209959  1.068977  1.8425919
ENSG00000188157  3.0594442  1.985078  2.926705  2.916115  1.2946137
ENSG00000273443  1.6891590  1.412390  1.976621  1.540857 -0.3532791
ENSG00000217801  2.2551156  1.169332  3.547816  1.873190  0.3334631
ENSG00000237330 -2.9815417 -2.429988 -2.981542 -2.437603 -2.4245831
ENSG00000223823 -2.9815417 -2.981542 -2.981542 -2.981542 -2.9815417
                   NAPT30     NAPT91     NA3622     NA3659    NA18358
ENSG00000188976 -2.008556 -2.3855698 -2.4153553 -2.4615104 -2.9815417
ENSG00000188157 -1.212936 -0.7860576 -0.8558140 -0.8206567 -0.8020982
ENSG00000273443  0.492301 -1.9650589 -1.6935886  2.1415838  0.3987400
ENSG00000217801  1.136923 -0.6333308  0.3592322  2.9568407  1.2842881
ENSG00000237330 -2.981542 -2.9815417 -2.9815417 -2.0800683 -2.9815417
ENSG00000223823 -2.981542 -2.9815417 -2.9815417 -2.9815417 -2.9815417

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

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

# Plot 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"))

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

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")

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)

                 NA18504  NA18510  NA18523  NA18499  NA18502   NAPT30
ENSG00000186891 5.019760 5.041382 4.900641 5.711124 3.366537 4.651440
ENSG00000078808 6.869945 7.037620 7.477403 6.629313 6.741815 6.705657
ENSG00000176022 4.763855 4.687804 4.803574 4.617006 4.705504 4.849646
ENSG00000160087 4.849052 5.381048 5.119697 4.965717 5.086619 5.207984
ENSG00000131584 6.069280 4.920816 4.539908 4.737501 5.356339 5.133882
ENSG00000169972 3.357225 3.394890 3.555318 3.773273 3.315091 3.621735
                  NAPT91   NA3622   NA3659  NA18358
ENSG00000186891 3.175319 4.739154 6.460823 6.498061
ENSG00000078808 6.781323 6.788155 6.796852 7.076976
ENSG00000176022 5.566032 4.857458 5.289102 5.577640
ENSG00000160087 5.451318 5.542696 5.337421 5.414395
ENSG00000131584 5.709962 5.380906 5.258878 5.565531
ENSG00000169972 3.552346 4.077282 3.072501 3.321316

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

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            0            1

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)

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

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

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)

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

#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))

This is wierd. Normalization moves 2 samples to opposite species clusters but the samples that separate in the correlation are not those samples. 4973 and 18498 are the samples that looked funny on the original 3’ data. This may be a sample swap at the RNA stage. These samples were in the same extraction batch. It could have happened then. I will look into this more.

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)
}

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)
}

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)
}

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)
}

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)
}

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)
}

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

I should look at the correlation between PCs and these factors.

Try to run the pca the opposite way:

pca_line=prcomp(cpm.voom$E, center=T,scale=T)

pca_line_df=as.data.frame(pca_line$rotation) %>% rownames_to_column(var="Line") %>% select(1:11)


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

plot(proportion)

metaData_order=metaData %>% arrange(Line)
PCA_order=pca_line_df %>% arrange(Line)
Pc1Spec <- summary(lm(PCA_order$PC1 ~ metaData_order$Species))$adj.r.squared
Pc2Spec <- summary(lm(PCA_order$PC2 ~ metaData_order$Species))$adj.r.squared
Pc3Spec <- summary(lm(PCA_order$PC3 ~ metaData_order$Species))$adj.r.squared
Pc1Spec

[1] -0.1137969

Pc2Spec

[1] 0.9783785

Pc3Spec

[1] -0.1240532

Expand this to full heatmap.

plotpca

col.v <- pal[as.integer(metaData$Species)]
plot_scores(pca_line, scores, n, n+1,cols = col.v )

Question: what is the difference between these PCAs???

Test for DE

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

                   Chimp    Human
ENSG00000186891 4.795816 5.095375
ENSG00000078808 6.947449 6.828201
ENSG00000176022 4.693342 5.230150
ENSG00000160087 5.073666 5.402331
ENSG00000131584 5.103075 5.410320
ENSG00000169972 3.468275 3.544200

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  4.885120 8.015919  49.50888 1.051297e-13 4.138512e-10
ENSG00000204463 -6.291373 5.272106 -49.50052 1.053142e-13 4.138512e-10
ENSG00000205531 -5.980720 5.127592 -47.39996 1.651394e-13 4.138512e-10
ENSG00000145741  6.272278 5.704197  47.26164 1.702216e-13 4.138512e-10
ENSG00000133112  6.037437 8.611334  45.96615 2.270894e-13 4.416889e-10
ENSG00000142937  5.708662 8.279388  44.22833 3.386317e-13 5.488655e-10
ENSG00000142541  6.366791 6.767260  39.58302 1.069006e-12 1.485155e-09
ENSG00000100316  6.010986 8.347096  37.36455 1.941895e-12 2.360616e-09
ENSG00000147604 -6.194041 5.570670 -34.97357 3.847768e-12 4.157728e-09
ENSG00000071082  5.845714 6.590811  31.97632 9.707041e-12 9.440097e-09
ENSG00000183020 -4.817991 4.189720 -30.95079 1.358744e-11 1.201253e-08
ENSG00000186298  3.182017 6.870342  30.14030 1.786324e-11 1.447667e-08
ENSG00000198242  4.240871 5.024421  25.20195 1.125488e-10 8.042832e-08
ENSG00000165672  2.443572 5.535737  25.13245 1.157837e-10 8.042832e-08
ENSG00000153395 -4.940243 5.261343 -24.69814 1.384552e-10 8.829558e-08
ENSG00000116478  3.404755 5.871950  24.58273 1.452678e-10 8.829558e-08
ENSG00000089009  2.275400 7.530109  24.11044 1.772287e-10 1.013852e-07
ENSG00000171863  3.179473 6.726062  22.92492 2.969928e-10 1.554700e-07
ENSG00000105640 -2.873676 8.228281 -22.87459 3.037460e-10 1.554700e-07
ENSG00000198034  6.351635 7.259074  22.55804 3.502709e-10 1.689663e-07
                       B
ENSG00000105372 21.10198
ENSG00000204463 19.68397
ENSG00000205531 19.45798
ENSG00000145741 19.60711
ENSG00000133112 20.53890
ENSG00000142937 20.21086
ENSG00000142541 18.80122
ENSG00000100316 18.80876
ENSG00000147604 17.57624
ENSG00000071082 17.14047
ENSG00000183020 16.48622
ENSG00000186298 16.88711
ENSG00000198242 14.96025
ENSG00000165672 15.09170
ENSG00000153395 14.76334
ENSG00000116478 14.86424
ENSG00000089009 14.74940
ENSG00000171863 14.23110
ENSG00000105640 14.21959
ENSG00000198034 14.02234

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

[1] 3200

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)

summary(decideTests(tmp))

       Chimp - Human
Down            1584
NotSig          6525
Up              1616

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

Differential Expression Remove Bad Individuals

Briana Mittleman

11/19/2019

Diffferential Expression

Raw Counts

Filter low expressed gene

Test for DE