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make the spacing follow a more consistent style
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@malachig
malachig committed Jun 19, 2024
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Showing 1 changed file with 37 additions and 36 deletions.
73 changes: 37 additions & 36 deletions _posts/0003-04-03-DE_Visualization_AdvancedR.md
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,7 @@ gene_expression = read.table("~/workspace/rnaseq/expression/stringtie/ref_only/g

#Import gene name mapping file (http://genomedata.org/rnaseq-tutorial/results/cshl2022/rnaseq/ENSG_ID2Name.txt)
gene_names=read.table("~/workspace/rnaseq/de/htseq_counts/ENSG_ID2Name.txt", header = TRUE, stringsAsFactors = FALSE)
colnames(gene_names) = c("gene_id","gene_name")
colnames(gene_names) = c("gene_id", "gene_name")

#Import DE results from the HISAT2/htseq-count/DESeq2 pipeline (http://genomedata.org/cri-workshop/deseq2/DE_all_genes_DESeq2.tsv)
setwd(outdir)
Expand All @@ -88,10 +88,10 @@ row.names(gene_expression)
dim(gene_expression)

#Get the first 3 rows of data and a selection of columns
gene_expression[1:3,c(1:3,6)]
gene_expression[1:3, c(1:3, 6)]

#Do the same thing, but using the column names instead of numbers
gene_expression[1:3, c("HBR_Rep1","HBR_Rep2","HBR_Rep3","UHR_Rep3")]
gene_expression[1:3, c("HBR_Rep1", "HBR_Rep2", "HBR_Rep3", "UHR_Rep3")]

#Now, exlore the differential expression (DESeq2 results)
head(results_genes)
Expand All @@ -100,7 +100,7 @@ dim(results_genes)
#Assign some colors for use later. You can specify color by RGB, Hex code, or name
#To get a list of color names:
colours()
data_colors = c("tomato1","tomato2","tomato3","royalblue1","royalblue2","royalblue3")
data_colors = c("tomato1", "tomato2", "tomato3", "royalblue1", "royalblue2", "royalblue3")

```

Expand All @@ -115,51 +115,51 @@ min_nonzero = 1

# Set the columns for finding TPM and create shorter names for figures
data_columns = c(1:6)
short_names = c("HBR_1","HBR_2","HBR_3","UHR_1","UHR_2","UHR_3")
short_names = c("HBR_1", "HBR_2", "HBR_3", "UHR_1", "UHR_2", "UHR_3")

pdf(file = "All_samples_TPM_boxplots.pdf")
boxplot(log2(gene_expression[,data_columns]+min_nonzero), col = data_colors, names = short_names, las = 2, ylab = "log2(TPM)", main = "Distribution of TPMs for all 6 libraries")
boxplot(log2(gene_expression[, data_columns] + min_nonzero), col = data_colors, names = short_names, las = 2, ylab = "log2(TPM)", main = "Distribution of TPMs for all 6 libraries")
#Note that the bold horizontal line on each boxplot is the median
dev.off()

#### Plot #2 - plot a pair of replicates to assess reproducibility of technical replicates
#Tranform the data by converting to log2 scale after adding an arbitrary small value to avoid log2(0)
x = gene_expression[,"UHR_Rep1"]
y = gene_expression[,"UHR_Rep2"]
x = gene_expression[, "UHR_Rep1"]
y = gene_expression[, "UHR_Rep2"]
pdf(file = "UHR_Rep1_vs_Rep2_scatter.pdf")
plot(x = log2(x+min_nonzero), y = log2(y+min_nonzero), pch = 16, col = "blue", cex = 0.25, xlab = "TPM (UHR, Replicate 1)", ylab = "TPM (UHR, Replicate 2)", main = "Comparison of expression values for a pair of replicates")
plot(x = log2(x + min_nonzero), y = log2(y + min_nonzero), pch = 16, col = "blue", cex = 0.25, xlab = "TPM (UHR, Replicate 1)", ylab = "TPM (UHR, Replicate 2)", main = "Comparison of expression values for a pair of replicates")

#Add a straight line of slope 1, and intercept 0
abline(a = 0, b = 1)

#Calculate the correlation coefficient and display in a legend
rs = cor(x,y)^2
rs = cor(x, y)^2
legend("topleft", paste("R squared = ", round(rs, digits = 3), sep = ""), lwd = 1, col = "black")
dev.off()

#### Plot #3 - Scatter plots with a large number of data points can be misleading ... regenerate this figure as a density scatter plot
pdf(file = "UHR_Rep1_vs_Rep2_SmoothScatter.pdf")
colors = colorRampPalette(c("white", "blue", "#007FFF", "cyan","#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
smoothScatter(x = log2(x+min_nonzero), y = log2(y+min_nonzero), xlab = "TPM (UHR, Replicate 1)", ylab = "TPM (UHR, Replicate 2)", main = "Comparison of expression values for a pair of replicates", colramp = colors, nbin = 200)
smoothScatter(x = log2(x + min_nonzero), y = log2(y + min_nonzero), xlab = "TPM (UHR, Replicate 1)", ylab = "TPM (UHR, Replicate 2)", main = "Comparison of expression values for a pair of replicates", colramp = colors, nbin = 200)
dev.off()

#### Plot #4 - Scatter plots of all sets of replicates on a single plot
#Create a function that generates an R plot. This function will take as input the two libraries to be compared and a plot name
plotCor = function(lib1, lib2, name){
x = gene_expression[,lib1]
y = gene_expression[,lib2]
zero_count = length(which(x==0)) + length(which(y==0))
x = gene_expression[, lib1]
y = gene_expression[, lib2]
zero_count = length(which(x == 0)) + length(which(y == 0))
colors = colorRampPalette(c("white", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
smoothScatter(x = log2(x+min_nonzero), y = log2(y+min_nonzero), xlab = lib1, ylab = lib2, main = name, colramp = colors, nbin = 275)
abline(a = 0,b = 1)
smoothScatter(x = log2(x + min_nonzero), y = log2(y + min_nonzero), xlab = lib1, ylab = lib2, main = name, colramp = colors, nbin = 275)
abline(a = 0, b = 1)
rs = cor(x, y, method = "pearson")^2
legend_text = c(paste("R squared = ", round(rs, digits = 3), sep=""), paste("Zero count = ", zero_count, sep = ""))
legend("topleft", legend_text, lwd = c(1,NA), col = "black", bg = "white", cex = 0.8)
legend("topleft", legend_text, lwd = c(1, NA), col = "black", bg = "white", cex = 0.8)
}

#Now make a call to our custom function created above, once for each library comparison
pdf(file = "UHR_All_Reps_SmoothScatter.pdf")
par(mfrow = c(1,3))
par(mfrow = c(1, 3))
plotCor("UHR_Rep1", "UHR_Rep2", "UHR_1 vs UHR_2")
plotCor("UHR_Rep2", "UHR_Rep3", "UHR_2 vs UHR_3")
plotCor("UHR_Rep1", "UHR_Rep3", "UHR_1 vs UHR_3")
Expand Down Expand Up @@ -187,20 +187,20 @@ dev.off()
#Libraries with similar expression patterns (highly correlated to each other) should group together
#What pattern do we expect to see, given the types of libraries we have (technical replicates, biologal replicates, tumor/normal)?
pdf(file = "UHR_vs_HBR_MDS.pdf")
d = 1-r
d = 1 - r
mds = cmdscale(d, k = 2, eig = TRUE)
par(mfrow = c(1,1))
plot(mds$points, type = "n", xlab = "", ylab = "", main = "MDS distance plot (all non-zero genes)", xlim = c(-0.12,0.12), ylim = c(-0.12,0.12))
points(mds$points[,1], mds$points[,2], col = "grey", cex = 2, pch = 16)
text(mds$points[,1], mds$points[,2], short_names, col = data_colors)
plot(mds$points, type = "n", xlab = "", ylab = "", main = "MDS distance plot (all non-zero genes)", xlim = c(-0.12, 0.12), ylim = c(-0.12, 0.12))
points(mds$points[, 1], mds$points[, 2], col = "grey", cex = 2, pch = 16)
text(mds$points[, 1], mds$points[, 2], short_names, col = data_colors)
dev.off()


#### Plot #6 - View the distribution of differential expression values as a histogram
#Display only those results that are significant according to DESeq2 (loaded above)
pdf(file = "UHR_vs_HBR_DE_FC_distribution.pdf")
sig = which(results_genes$pvalue<0.05)
hist(results_genes[sig,"log2FoldChange"], breaks=50, col="seagreen", xlab="log2(Fold change) UHR vs HBR", main="Distribution of differential expression values")
sig = which(results_genes$pvalue < 0.05)
hist(results_genes[sig, "log2FoldChange"], breaks = 50, col = "seagreen", xlab = "log2(Fold change) UHR vs HBR", main = "Distribution of differential expression values")
abline(v = -2, col = "black", lwd = 2, lty = 2)
abline(v = 2, col = "black", lwd = 2, lty = 2)
legend("topleft", "Fold-change > 4", lwd = 2, lty = 2)
Expand All @@ -209,11 +209,11 @@ dev.off()
#### Plot #7 - Display the mean expression values from UHR and HBR and mark those that are significantly differentially expressed
pdf(file="UHR_vs_HBR_mean_TPM_scatter.pdf")

gene_expression[,"HBR_mean"] = apply(gene_expression[,c(1:3)], 1, mean)
gene_expression[,"UHR_mean"] = apply(gene_expression[,c(4:6)], 1, mean)
gene_expression[, "HBR_mean"] = apply(gene_expression[,c(1:3)], 1, mean)
gene_expression[, "UHR_mean"] = apply(gene_expression[,c(4:6)], 1, mean)

x = log2(gene_expression[,"UHR_mean"] + min_nonzero)
y = log2(gene_expression[,"HBR_mean"] + min_nonzero)
x = log2(gene_expression[, "UHR_mean"] + min_nonzero)
y = log2(gene_expression[, "HBR_mean"] + min_nonzero)
plot(x = x, y = y, pch = 16, cex = 0.25, xlab = "UHR TPM (log2)", ylab = "HBR TPM (log2)", main = "UHR vs HBR TPMs")
abline(a = 0, b = 1)
xsig = x[sig]
Expand All @@ -223,7 +223,7 @@ legend("topleft", "Significant", col = "magenta", pch = 16)

#Get the gene symbols for the top N (according to corrected p-value) and display them on the plot
topn = order(results_genes[sig,"padj"])[1:25]
text(x[topn], y[topn], results_genes[topn,"Symbol"], col = "black", cex = 0.75, srt = 45)
text(x[topn], y[topn], results_genes[topn, "Symbol"], col = "black", cex = 0.75, srt = 45)

dev.off()

Expand All @@ -233,16 +233,16 @@ mydist = function(c) {dist(c, method = "euclidian")}
myclust = function(c) {hclust(c, method = "average")}

#Create a subset of significant genes with p-value<0.05 and log2 fold-change >= 2
sigpi = which(results_genes[,"pvalue"] < 0.05)
sigp = results_genes[sigpi,]
sigfc = which(abs(sigp[,"log2FoldChange"]) >= 2)
sigDE = sigp[sigfc,]
sigpi = which(results_genes[, "pvalue"] < 0.05)
sigp = results_genes[sigpi, ]
sigfc = which(abs(sigp[, "log2FoldChange"]) >= 2)
sigDE = sigp[sigfc, ]

pdf(file = "EHR_vs_HBR_heatmap.pdf")
main_title = "sig DE Genes"
par(cex.main = 0.8)
sigDE_genes = sigDE[,"ensemblID"]
sigDE_genenames = sigDE[,"Symbol"]
sigDE_genes = sigDE[, "ensemblID"]
sigDE_genenames = sigDE[, "Symbol"]

data = log2(as.matrix(gene_expression[as.vector(sigDE_genes), data_columns]) + 1)
heatmap.2(data, hclustfun = myclust, distfun = mydist, na.rm = TRUE, scale = "none", dendrogram = "both", margins = c(10,4), Rowv = TRUE, Colv = TRUE, symbreaks = FALSE, key = TRUE, symkey = FALSE, density.info = "none", trace = "none", main = main_title, cexRow = 0.3, cexCol = 1, labRow = sigDE_genenames, col = rev(heat.colors(75)))
Expand Down Expand Up @@ -278,7 +278,8 @@ ggplot(data = results_genes[results_genes$diffexpressed != "No",], aes(x = log2F
dev.off()

#To exit R type:
#quit(save = "no")
quit(save = "no")

```

The output file can be viewed in your browser at the following url. Note, you must replace **YOUR_PUBLIC_IPv4_ADDRESS** with your own amazon instance IP (e.g., 101.0.1.101)).
Expand Down

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