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Introduction to Alignment Free Analysis | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Introduction to Alignment Free Analysis

Module 4 - Key concepts

  • pseudo-alignment, reference-free expression estimation,

Module 4 - Learning objectives

  • Alignment free estimation of transcript abundance
  • Introduction to k-mers
  • Alignment free tools
  • Sailfish, RNA-Skim, Kallisto, Salmon
  • Abundance estimation and differential expression analysis with Kallisto and Sleuth

Lecture

Ballgown DE Visualization | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Ballgown DE Visualization


RNA-seq_Flowchart4


Navigate to the correct directory and then launch R:

cd $RNA_HOME/de/ballgown/ref_only
R

A separate R tutorial file has been provided below. Run the R commands detailed in the R script. All results are directed to pdf file(s). The output pdf files can be viewed in your browser at the following urls. Note, you must replace YOUR_IP_ADDRESS with your own amazon instance IP (e.g., 101.0.1.101)).

First you’ll need to load the libraries needed for this analysis and define a path for the output PDF to be written.

###R code###
#load libraries
library(ballgown)
library(genefilter)
library(dplyr)
library(devtools)

#designate output file
outfile="~/workspace/rnaseq/de/ballgown/ref_only/Tutorial_Part2_ballgown_output.pdf"

Next we’ll load our data into R.

###R code###
# Load phenotype data
pheno_data = read.csv("UHR_vs_HBR.csv")

# Display the phenotype data
pheno_data

# Load the ballgown object from file
load('bg.rda')

# The load command, loads an R object from a file into memory in our R session. 
# You can use ls() to view the names of variables that have been loaded
ls()

# Print a summary of the ballgown object
bg

Now we’ll start to generate our figures with the following R code.

###R code###
# Open a PDF file where we will save some plots. We will save all figures and then view the PDF at the end
pdf(file=outfile)

# Extract FPKM values from the 'bg' object
fpkm = texpr(bg,meas="FPKM")

# View the last several rows of the FPKM table
tail(fpkm)

# Transform the FPKM values by adding 1 and convert to a log2 scale
fpkm = log2(fpkm+1)

# View the last several rows of the transformed FPKM table
tail(fpkm)

# Create boxplots to display summary statistics for the FPKM values for each sample
boxplot(fpkm,col=as.numeric(pheno_data$type)+1,las=2,ylab='log2(FPKM+1)')

# Display the transcript ID for a single row of data
ballgown::transcriptNames(bg)[2763]

# Display the gene name for a single row of data 
ballgown::geneNames(bg)[2763]

# Create a BoxPlot comparing the expression of a single gene for all replicates of both conditions
plot(fpkm[2763,] ~ pheno_data$type, border=c(2,3), main=paste(ballgown::geneNames(bg)[2763],' : ', ballgown::transcriptNames(bg)[2763]),pch=19, xlab="Type", ylab='log2(FPKM+1)')

# Add the FPKM values for each sample onto the plot 
points(fpkm[2763,] ~ jitter(as.numeric(pheno_data$type)), col=as.numeric(pheno_data$type)+1, pch=16)

# Create a plot of transcript structures observed in each replicate and color transcripts by expression level
plotTranscripts(ballgown::geneIDs(bg)[2763], bg, main=c('Gene in sample HBR_Rep1'), sample=c('HBR_Rep1'))
plotTranscripts(ballgown::geneIDs(bg)[2763], bg, main=c('Gene in sample HBR_Rep2'), sample=c('HBR_Rep2'))
plotTranscripts(ballgown::geneIDs(bg)[2763], bg, main=c('Gene in sample HBR_Rep3'), sample=c('HBR_Rep3'))
plotTranscripts(ballgown::geneIDs(bg)[2763], bg, main=c('Gene in sample UHR_Rep1'), sample=c('UHR_Rep1'))
plotTranscripts(ballgown::geneIDs(bg)[2763], bg, main=c('Gene in sample UHR_Rep2'), sample=c('UHR_Rep2'))
plotTranscripts(ballgown::geneIDs(bg)[2763], bg, main=c('Gene in sample UHR_Rep3'), sample=c('UHR_Rep3'))

#plotMeans('TST',bg,groupvar="type",legend=FALSE)

# Close the PDF device where we have been saving our plots
dev.off()

# Exit the R session
quit(save="no")

Remember that you can view the output graphs of this step on your instance by navigating to this location in a web browser window:

http://YOUR_IP_ADDRESS/rnaseq/de/ballgown/ref_only/Tutorial_Part2_ballgown_output.pdf

The above code can be found in a single R script located here.

SUPPLEMENTARY R ANALYSIS

Occasionally you may wish to reformat and work with stringtie output in R manually. Therefore we provide an optional/advanced tutorial on how to format your results for R and perform “old school” (non-ballgown analysis) on your data.

In this tutorial you will:

Expression and differential expression files will be read into R. The R analysis will make use of the transcript-level expression and differential expression files from stringtie/ballgown. Navigate to the correct directory and then launch R:

cd $RNA_HOME/de/ballgown/ref_only/
R

A separate R script has been provided below.

First you’ll load your libraries and your data.

***R script***
#Tutorial_Part3_Supplementary_R.R

#Starting from the output of the RNA-seq Tutorial Part 1.

#Load libraries
library(ggplot2)
library(gplots)
library(GenomicRanges)
library(ballgown)

#If X11 not available, open a pdf device for output of all plots
pdf(file="Tutorial_Part3_Supplementary_R_output.pdf")

#### Import the gene expression data from the HISAT2/StringTie/Ballgown tutorial

#Set working directory where results files exist
working_dir = "~/workspace/rnaseq/de/ballgown/ref_only" 
setwd(working_dir)

# List the current contents of this directory
dir()

#Import expression and differential expression results from the HISAT2/StringTie/Ballgown pipeline
load('bg.rda')

# View a summary of the ballgown object
bg

# Load gene names for lookup later in the tutorial
bg_table = texpr(bg, 'all')
bg_gene_names = unique(bg_table[, 9:10])

# Pull the gene_expression data frame from the ballgown object
gene_expression = as.data.frame(gexpr(bg))

Next, you’ll load the data into a R dataframe.

***R code***
#### Working with 'dataframes'
#View the first five rows of data (all columns) in one of the dataframes created
head(gene_expression)

#View the column names
colnames(gene_expression)

#View the row names
row.names(gene_expression)

#Determine the dimensions of the dataframe.  'dim()' will return the number of rows and columns
dim(gene_expression)

You’ll then get the first 3 rows of data and view expression and transcript information.

***R code***
#Get the first 3 rows of data and a selection of columns
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("FPKM.UHR_Rep1","FPKM.UHR_Rep2","FPKM.UHR_Rep3","FPKM.HBR_Rep3")]

#Assign colors to each.  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")

#View expression values for the transcripts of a particular gene symbol of chromosome 22.  e.g. 'TST'
#First determine the rows in the data.frame that match 'TST', aka. ENSG00000128311, then display only those rows of the data.frame
i = row.names(gene_expression) == "ENSG00000128311"
gene_expression[i,]

#What if we want to view values for a list of genes of interest all at once? 
#genes_of_interest = c("TST", "MMP11", "LGALS2", "ISX")
genes_of_interest = c("ENSG00000128311","ENSG00000099953","ENSG00000100079","ENSG00000175329")
i = which(row.names(gene_expression) %in% genes_of_interest)
gene_expression[i,]

# Load the transcript to gene index from the ballgown object
transcript_gene_table = indexes(bg)$t2g
head(transcript_gene_table)

#Each row of data represents a transcript. Many of these transcripts represent the same gene. Determine the numbers of transcripts and unique genes  
length(row.names(transcript_gene_table)) #Transcript count
length(unique(transcript_gene_table[,"g_id"])) #Unique Gene count

The following code blocks are to generate various plots using the above data set.

###R code###
#### Plot #1 - the number of transcripts per gene.  
#Many genes will have only 1 transcript, some genes will have several transcripts
#Use the 'table()' command to count the number of times each gene symbol occurs (i.e. the # of transcripts that have each gene symbol)
#Then use the 'hist' command to create a histogram of these counts
#How many genes have 1 transcript?  More than one transcript?  What is the maximum number of transcripts for a single gene?
counts=table(transcript_gene_table[,"g_id"])
c_one = length(which(counts == 1))
c_more_than_one = length(which(counts > 1))
c_max = max(counts)
hist(counts, breaks=50, col="bisque4", xlab="Transcripts per gene", main="Distribution of transcript count per gene")
legend_text = c(paste("Genes with one transcript =", c_one), paste("Genes with more than one transcript =", c_more_than_one), paste("Max transcripts for single gene = ", c_max))
legend("topright", legend_text, lty=NULL)

#### Plot #2 - the distribution of transcript sizes as a histogram
#In this analysis we supplied StringTie with transcript models so the lengths will be those of known transcripts
#However, if we had used a de novo transcript discovery mode, this step would give us some idea of how well transcripts were being assembled
#If we had a low coverage library, or other problems, we might get short 'transcripts' that are actually only pieces of real transcripts

full_table <- texpr(bg , 'all')
hist(full_table$length, breaks=50, xlab="Transcript length (bp)", main="Distribution of transcript lengths", col="steelblue")

#### Summarize FPKM values for all 6 replicates
#What are the minimum and maximum FPKM values for a particular library?
min(gene_expression[,"FPKM.UHR_Rep1"])
max(gene_expression[,"FPKM.UHR_Rep1"])

#Set the minimum non-zero FPKM values for use later.
#Do this by grabbing a copy of all data values, coverting 0's to NA, and calculating the minimum or all non NA values
#zz = fpkm_matrix[,data_columns]
#zz[zz==0] = NA
#min_nonzero = min(zz, na.rm=TRUE)
#min_nonzero

#Alternatively just set min value to 1
min_nonzero=1

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

#### Plot #3 - View the range of values and general distribution of FPKM values for all 4 libraries
#Create boxplots for this purpose
#Display on a log2 scale and add the minimum non-zero value to avoid log2(0)
boxplot(log2(gene_expression[,data_columns]+min_nonzero), col=data_colors, names=short_names, las=2, ylab="log2(FPKM)", main="Distribution of FPKMs for all 6 libraries")
#Note that the bold horizontal line on each boxplot is the median

#### Plot #4 - 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[,"FPKM.UHR_Rep1"]
y = gene_expression[,"FPKM.UHR_Rep2"]
plot(x=log2(x+min_nonzero), y=log2(y+min_nonzero), pch=16, col="blue", cex=0.25, xlab="FPKM (UHR, Replicate 1)", ylab="FPKM (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
legend("topleft", paste("R squared = ", round(rs, digits=3), sep=""), lwd=1, col="black")

#### Plot #5 - Scatter plots with a large number of data points can be misleading ... regenerate this figure as a density scatter plot
colors = colorRampPalette(c("white", "blue", "#007FFF", "cyan","#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
smoothScatter(x=log2(x+min_nonzero), y=log2(y+min_nonzero), xlab="FPKM (UHR, Replicate 1)", ylab="FPKM (UHR, Replicate 2)", main="Comparison of expression values for a pair of replicates", colramp=colors, nbin=200)

#### Plot all sets of replicates on a single plot
#Create an function that generates an R plot.  This function will take as input the two libraries to be compared and a plot name and color
plotCor = function(lib1, lib2, name, color){
	x=gene_expression[,lib1]
	y=gene_expression[,lib2]
	zero_count = length(which(x==0)) + length(which(y==0))
	plot(x=log2(x+min_nonzero), y=log2(y+min_nonzero), pch=16, col=color, cex=0.25, xlab=lib1, ylab=lib2, main=name)
	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)
}
#Open a plotting page with room for two plots on one page
par(mfrow=c(1,2))

#Plot #6 - Now make a call to our custom function created above, once for each library comparison
plotCor("FPKM.UHR_Rep1", "FPKM.HBR_Rep1", "UHR_1 vs HBR_1", "tomato2")
plotCor("FPKM.UHR_Rep2", "FPKM.HBR_Rep2", "UHR_2 vs HBR_2", "royalblue2")


##### One problem with these plots is that there are so many data points on top of each other, that information is being lost
#Regenerate these plots using a density scatter plot
plotCor2 = function(lib1, lib2, name, color){
	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)
	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)
}

#### Plot #7 - Now make a call to our custom function created above, once for each library comparison
par(mfrow=c(1,2))
plotCor2("FPKM.UHR_Rep1", "FPKM.HBR_Rep1", "UHR_1 vs HBR_1", "tomato2")
plotCor2("FPKM.UHR_Rep2", "FPKM.HBR_Rep2", "UHR_2 vs HBR_2", "royalblue2")


#### Compare the correlation 'distance' between all replicates
#Do we see the expected pattern for all eight libraries (i.e. replicates most similar, then tumor vs. normal)?

#Calculate the FPKM sum for all 6 libraries
gene_expression[,"sum"]=apply(gene_expression[,data_columns], 1, sum)

#Identify the genes with a grand sum FPKM of at least 5 - we will filter out the genes with very low expression across the board
i = which(gene_expression[,"sum"] > 5)

#Calculate the correlation between all pairs of data
r=cor(gene_expression[i,data_columns], use="pairwise.complete.obs", method="pearson")

#Print out these correlation values
r

#### Plot #8 - Convert correlation to 'distance', and use 'multi-dimensional scaling' to display the relative differences between libraries
#This step calculates 2-dimensional coordinates to plot points for each library
#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)?
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)

# Calculate the differential expression results including significance
results_genes = stattest(bg, feature="gene", covariate="type", getFC=TRUE, meas="FPKM")
results_genes = merge(results_genes,bg_gene_names,by.x=c("id"),by.y=c("gene_id"))

#### Plot #9 - View the distribution of differential expression values as a histogram
#Display only those that are significant according to Ballgown

sig=which(results_genes$pval<0.05)
results_genes[,"de"] = log2(results_genes[,"fc"])
hist(results_genes[sig,"de"], 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)

#### Plot #10 - Display the grand expression values from UHR and HBR and mark those that are significantly differentially expressed
gene_expression[,"UHR"]=apply(gene_expression[,c(1:3)], 1, mean)
gene_expression[,"HBR"]=apply(gene_expression[,c(4:6)], 1, mean)

x=log2(gene_expression[,"UHR"]+min_nonzero)
y=log2(gene_expression[,"HBR"]+min_nonzero)
plot(x=x, y=y, pch=16, cex=0.25, xlab="UHR FPKM (log2)", ylab="HBR FPKM (log2)", main="UHR vs HBR FPKMs")
abline(a=0, b=1)
xsig=x[sig]
ysig=y[sig]
points(x=xsig, y=ysig, col="magenta", pch=16, cex=0.5)
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(abs(results_genes[sig,"fc"]), decreasing=TRUE)[1:25]
topn = order(results_genes[sig,"qval"])[1:25]
text(x[topn], y[topn], results_genes[topn,"gene_name"], col="black", cex=0.75, srt=45)


#### Write a simple table of differentially expressed transcripts to an output file
#Each should be significant with a log2 fold-change >= 2
sigpi = which(results_genes[,"pval"]<0.05)
sigp = results_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]

#Order the output by or p-value and then break ties using fold-change
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)

output = sig_tn_de[o,c("gene_name","id","fc","pval","qval","de")]
write.table(output, file="SigDE_supplementary_R.txt", sep="\t", row.names=FALSE, quote=FALSE)

#View selected columns of the first 25 lines of output
output[1:25,c(1,4,5)]

#You can open the file "SigDE.txt" in Excel, Calc, etc.
#It should have been written to the current working directory that you set at the beginning of the R tutorial
dir()


#### Plot #11 - Create a heatmap to vizualize expression differences between the eight samples
#Define custom dist and hclust functions for use with heatmaps
mydist=function(c) {dist(c,method="euclidian")}
myclust=function(c) {hclust(c,method="average")}

main_title="sig DE Transcripts"
par(cex.main=0.8)
sig_genes=results_genes[sig,"id"]
sig_gene_names=results_genes[sig,"gene_name"]
data=log2(as.matrix(gene_expression[sig_genes,data_columns])+1)
heatmap.2(data, hclustfun=myclust, distfun=mydist, na.rm = TRUE, scale="none", dendrogram="both", margins=c(6,7), Rowv=TRUE, Colv=TRUE, symbreaks=FALSE, key=TRUE, symkey=FALSE, density.info="none", trace="none", main=main_title, cexRow=0.3, cexCol=1, labRow=sig_gene_names,col=rev(heat.colors(75)))

dev.off()

#The output file can be viewed in your browser at the following url:
#Note, you must replace __YOUR_IP_ADDRESS__ with your own amazon instance IP
#http://__YOUR_IP_ADDRESS__/workspace/rnaseq/de/ballgown/ref_only/Tutorial_Part3_Supplementary_R_output.pdf
#To exit R type:
quit(save="no")

Run the R commands detailed in the R script above. A R script containing all of the above code can be found here.

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