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DE Visualization with Ballgown | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

DE Visualization with Ballgown

Course

RNA-seq_Flowchart4

Ballgown DE Visualization

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_PUBLIC_IPv4_ADDRESS with your own amazon instance IP (e.g., 101.0.1.101)).

  • http://YOUR_PUBLIC_IPv4_ADDRESS/rnaseq/de/ballgown/ref_only/Tutorial_Part2_ballgown_output.pdf

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

#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.


# Generate phenotype data
ids = c("UHR_Rep1","UHR_Rep2","UHR_Rep3","HBR_Rep1","HBR_Rep2","HBR_Rep3")
type = c("UHR","UHR","UHR","HBR","HBR","HBR")
results = "/home/ubuntu/workspace/rnaseq/expression/stringtie/ref_only/"
path = paste(results,ids,sep="")
pheno_data = data.frame(ids,type,path)

# 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

# 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 and transcript expression data frame from the ballgown object
gene_expression = as.data.frame(gexpr(bg))
transcript_expression = as.data.frame(texpr(bg))

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

# 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]

#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 = bg_table[,"gene_id"] %in% genes_of_interest
bg_table[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

# 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)

Now we’ll start to generate figures with the following 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)

#### 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

hist(bg_table$length, breaks=50, xlab="Transcript length (bp)", main="Distribution of transcript lengths", col="steelblue")

#### Plot #3 - distribution of gene expression levels for each sample
# Create boxplots to display summary statistics for the FPKM values for each sample
# set color based on pheno_data$type which is UHR vs. HBR
# set labels perpendicular to axis (las=2)
# set ylab to indicate that values are log2 transformed
boxplot(fpkm,col=as.numeric(as.factor(pheno_data$type))+1,las=2,ylab='log2(FPKM+1)')

#### Plot 4 - BoxPlot comparing the expression of a single gene for all replicates of both conditions
# set border color for each of the boxplots
# set title (main) to gene : transcript
# set x label to Type
# set ylab to indicate that values are log2 transformed
boxplot(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
# set plot symbol to solid circle, default is empty circle
points(fpkm[2763,] ~ jitter(c(2,2,2,1,1,1)), col=c(2,2,2,1,1,1)+1, pch=16)


#### Plot 5 - Plot of transcript structures observed in each replicate and color transcripts by expression level
plotTranscripts(ballgown::geneIDs(bg)[2763], bg, main=c('TST in all HBR samples'), sample=c('HBR_Rep1', 'HBR_Rep2', 'HBR_Rep3'), labelTranscripts=TRUE)
plotTranscripts(ballgown::geneIDs(bg)[2763], bg, main=c('TST in all UHR samples'), sample=c('UHR_Rep1', 'UHR_Rep2', 'UHR_Rep3'), labelTranscripts=TRUE)

# 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_PUBLIC_IPv4_ADDRESS/rnaseq/de/ballgown/ref_only/Tutorial_Part2_ballgown_output.pdf

Course
Differential Expression with DESeq2 | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Differential Expression with DESeq2

Course

RNA-seq_Flowchart4

Differential Expression mini lecture

If you would like a brief refresher on differential expression analysis, please refer to the mini lecture.

DESeq2 DE Analysis

In this tutorial you will:

Setup

Here we launch R, install relevant packages (if needed), set working directories and read in the raw read counts data. Two pieces of information are required to perform analysis with DESeq2. A matrix of raw counts, such as was generated previously while running HTseq previously in this course. This is important as DESeq2 normalizes the data, correcting for differences in library size using using this type of data. DESeq2 also requires the experimental design which can be supplied as a data.frame, detailing the samples and conditions.

Launch R:

R

# Install the latest version of DEseq2
# if (!requireNamespace("BiocManager", quietly = TRUE))
#   install.packages("BiocManager")
# BiocManager::install("DESeq2")

# define working dir paths
datadir = "/cloud/project/data/bulk_rna"
outdir = "/cloud/project/outdir"

# load R libraries we will use in this section
library(DESeq2)
library(data.table)

# set working directory to data dir
setwd(datadir)

# read in the RNAseq read counts for each gene (produced by htseq-count)
htseqCounts <- fread("gene_read_counts_table_all_final.tsv")

Format htseq counts data to work with DESeq2

DESeq2 has a number of options for data import and actually has a function to read HTseq output files directly. Here the most universal option is used, reading in raw counts from a matrix in simple TSV format (one row per gene, one column per sample). The HTseq count data that was read in above is stored as an object of class data.table, this can be verified with the class() function. To use in this exercise it is required to convert this object to an appropriate matrix format with gene names as rows and samples as columns.

It should be noted that while the replicate samples are technical replicates (i.e. the same library was sequenced), herein they are treated as biological replicates for illustrative purposes. DESeq2 does have a function to collapse technical replicates though.


# view class of the data
class(htseqCounts)

# convert the data.table to matrix format
htseqCounts <- as.matrix(htseqCounts)
class(htseqCounts)

# set the gene ID values to be the row names for the matrix
rownames(htseqCounts) <- htseqCounts[,"GeneID"]

# now that the gene IDs are the row names, remove the redundant column that contains them
htseqCounts <- htseqCounts[, colnames(htseqCounts) != "GeneID"]

# convert the actual count values from strings (with spaces) to integers, because originally the gene column contained characters, the entire matrix was set to character
class(htseqCounts) <- "integer"

# view the first few lines of the gene count matrix
head(htseqCounts)

# it can also be useful to view interactively (if in Rstudio)
View(htseqCounts)

Filter raw counts

Before running DESeq2 or any differential expression analysis it is useful to pre-filter data. There are computational benefits to doing this as the memory size of the objects within R will decrease and DESeq2 will have less data to work through and will be faster. By removing “low quality” data, it is also reduces the number of statistical tests performed, which is turn reduces the impact of multiple test correction and can lead to more significant genes.

The amount of pre-filtering is up to the analyst however, it should be done in an unbiased way. DESeq2 recommends removing any gene with less than 10 reads across all samples. Below, we filter a gene if at least 1 sample does not have at least 10 reads. Either way, mostly what is being removed here are genes with very little evidence for expression in any sample (in may cases 0 counts in all samples).

# run a filtering step
# i.e. require that for every gene: at least 1 of 6 samples must have counts greater than 10
# get index of rows that meet this criterion and use that to subset the matrix
# note the dimensions of the matrix before and after filtering with dim

dim(htseqCounts)
htseqCounts <- htseqCounts[which(rowSums(htseqCounts >= 10) >=1),]
dim(htseqCounts)

# Hint! if you find the above command confusing, break it into pieces and observe the result
# 
# what does "rowSums(htseqCounts >= 10)" do?
#
# what does "rowSums(htseqCounts >= 10) >=1" do?

Specifying the experimental design

As mentioned above DESeq2 also needs to know the experimental design, that is which samples belong to which condition to test. The experimental design for the example dataset herein is quite simple as there are 6 samples with one condition to test, as such we can just create the experimental design right within R. There is one important thing to note, DESeq2 does not check sample names, it expects that the column names in the counts matrix we created correspond exactly to the row names we specifiy in the experimental design.

# construct a mapping of the meta data for our experiment (comparing UHR cell lines to HBR brain tissues)
# in simple terms this is defining the biological condition/label for each experimental replicate
# create a simple one column dataframe to start
metaData <- data.frame('Condition'=c('UHR', 'UHR', 'UHR', 'HBR', 'HBR', 'HBR'))

# convert the "Condition" column to a factor data type, this will determine the direction of log2 fold-changes for the genes (i.e. up or down regulated)
metaData$Condition <- factor(metaData$Condition, levels=c('HBR', 'UHR'))

# set the row names of the metaData dataframe to be the names of our sample replicates from the read counts matrix
rownames(metaData) <- colnames(htseqCounts)

# view the metadata dataframe
head(metaData)

# check that names of htseq count columns match the names of the meta data rows
# use the "all" function which tests whether an entire logical vector is TRUE
all(rownames(metaData) == colnames(htseqCounts))

Construct the DESeq2 object piecing all the data together

With all the data properly formatted it is now possible to combine all the information required to run differental expression in one object. This object will hold the input data, and intermediary calculations. It is also where the condition to test is specified.

# make deseq2 data sets
# here we are setting up our experiment by supplying: (1) the gene counts matrix, (2) the sample/replicate for each column, and (3) the biological conditions we wish to compare.
# this is a simple example that works for many experiments but these can also get more complex
# for example, including designs with multiple variables such as "~ group + condition",
# and designs with interactions such as "~ genotype + treatment + genotype:treatment".

dds <- DESeqDataSetFromMatrix(countData = htseqCounts, colData = metaData, design = ~Condition)

Running DESeq2

With all the data now in place DESeq2 can be run. Calling DESeq2 will perform the following actions:

# run the DESeq2 analysis on the "dds" object
dds <- DESeq(dds)

# view the DE results
res <- results(dds)
View(res)

Log-fold change shrinkage

It is good practice to shrink the log-fold change values, this does exactly what it sounds like, reducing the amount of log-fold change for genes where there are few counts which create huge variability that is not truly biological signal. Consider for example a gene for two samples, one sample has 1 read, and and one sample has 6 reads, that is a 6 fold change, that is likely not accurate. There are a number of algorithms that can be used to shrink log2 fold change, here we will use the apeglm algorithm, which does require the apeglm package to be installed.

# shrink the log2 fold change estimates
#   shrinkage of effect size (log fold change estimates) is useful for visualization and ranking of genes

#   In simplistic terms, the goal of calculating "dispersion estimates" and "shrinkage" is also to account for the problem that
#   genes with low mean counts across replicates tend of have higher variability than those with higher mean counts.
#   Shrinkage attempts to correct for this. For a more detailed discussion of shrinkage refer to the DESeq2 vignette

# first get the name of the coefficient (log fold change) to shrink
resultsNames(dds)

# now apply the shrinkage approach
resLFC <- lfcShrink(dds, coef="Condition_UHR_vs_HBR", type="apeglm")

# make a copy of the shrinkage results to manipulate
deGeneResult <- resLFC

#contrast the values for a few genes before and after shrinkage
head(res)
head(deGeneResult)

How did the results change before and after shinkage? What direction is each log2 fold change value moving?

Annotate gene symbols onto the DE results

DESeq2 was run with ensembl gene IDs as identifiers, this is not the most human friendly way to interpret results. Here gene symbols are merged onto the differential expressed gene list to make the results a bit more interpretable.

# read in gene ID to name mappings (using "fread" an alternative to "read.table")
mapping <- fread("ENSG_ID2Name.txt", header=F)

# add names to the columns in the "mapping" dataframe
setnames(mapping, c('ensemblID', 'Symbol'))

# view the first few lines of the gene ID to name mappings
head(mapping)

# merge on gene names
deGeneResult$ensemblID <- rownames(deGeneResult)
deGeneResult <- as.data.table(deGeneResult)
deGeneResult <- merge(deGeneResult, mapping, by='ensemblID', all.x=T)

# merge the original raw count values onto this final dataframe to aid interpretation
original_counts = as.data.frame(htseqCounts)
original_counts[,"ensemblID"] = rownames(htseqCounts)
deGeneResult = merge(deGeneResult, original_counts, by='ensemblID', all.x=T)

# view the final merged dataframe
# based on the raw counts and fold change values what does a negative fold change mean with respect to our two conditions HBR and UHR?
head(deGeneResult)

Data manipulation

With the DE analysis complete it is useful to view and filter the data frames to only the relevant genes, here some basic data manipulation is performed filtering to significant genes at specific thresholds.

# view the top genes according to adjusted p-value
deGeneResult[order(deGeneResult$padj),]

# view the top genes according to fold change
deGeneResult[order(deGeneResult$log2FoldChange),]

# determine the number of up/down significant genes at FDR < 0.05 significance level
dim(deGeneResult)[1] # number of genes tested
dim(deGeneResult[deGeneResult$padj < 0.05])[1] #number of significant genes

# order the DE results by adjusted p-value
deGeneResultSorted = deGeneResult[order(deGeneResult$padj),]

# create a filtered data frame that limits to only the significant DE genes (adjusted p.value < 0.05)
deGeneResultSignificant = deGeneResultSorted[deGeneResultSorted$padj < 0.05]

Save results to files

The data generated is now written out as tab separated files. Some of the DESeq2 objects are also saved as serialized R (RDS) objects which can be read back into R later for visualization.

# set the working directory to the output dir where we will store any results files
setwd(outdir)

# save the final DE result (all genes)  to an output file
fwrite(deGeneResultSorted, file='DE_all_genes_DESeq2.tsv', sep="\t")

# save the final DE result (significant genes only)  to an output file
fwrite(deGeneResultSignificant, file='DE_sig_genes_DESeq2.tsv', sep="\t")

# save the DESeq2 objects for the data visualization section
saveRDS(dds, 'dds.rds')
saveRDS(res, 'res.rds')
saveRDS(resLFC, 'resLFC.rds')

# to exit R type the following
#quit(save="no")
Course