Introduction to Bulk RNAseq data analysis
Initial exploration of RNA-seq data
Last modified: 20 Jul 2021
Introduction
In this section we will begin the process of analyzing the RNAseq data in R. In the next section we will use DESeq2 for differential analysis. A detailed analysis workflow, recommended by the authors of DESeq2 can be found on the Bionconductor website.
Before embarking on the main analysis of the data, it is essential to do some exploration of the raw data. We want to assess the patterns and characteristics of the data and compare these to what we expect from mRNAseq data and assess the data based on our knowledge of the experimental design. The primary means of data explorations are summary statistics and visualisations. In this session we will primarily concentrate on assessing if the patterns in the raw data conform to what we know about the experimental design. This is essential to identify problems such as batch effects, outlier samples and sample swaps.
Due to time constraints we are not able to cover all the ways we might do this, so additional information on initial data exploration are available in the supplementary materials.
In this session we will:
- import our counts into R
- filter out unwanted genes
- look at the effects of variance and how to mitigate this with data transformation
- do some initial exploration of the raw count data using principle component analysis
Set working directory
Before starting we need to make sure that the working directory for R is set to the RNAseq directory, which contains all the necessary file for these practicals. You can do this using the menu bar:
Session >> Set Working Directory >> Choose Directory ...
or you can use the following code in the R console:
setwd("~/Course_Materials/RNAseq")Data import
First, let’s load all the packages we will need to analyse the data.
library(tximport)
library(DESeq2)
library(tidyverse)A brief description of the data set
The data for this tutorial comes from the paper Transcriptomic Profiling of Mouse Brain During Acute and Chronic Infections by Toxoplasma gondii Oocysts (Hu et al. 2020). The raw data (sequence reads) can be downloaded from the NCBI Short Read Archive under project number PRJNA483261.
Please see extended material for instructions on downloading raw files from SRA.
This study examines changes in the gene expression profile in mouse brain in response to infection with the protozoan Toxoplasma gondii. The authors performed transcriptome analysis on samples from infected and uninfected mice at two time points, 11 days post infection and 33 days post infection. For each sample group there are 3 biological replicates. This effectively makes this a two factor study with two groups in each factor:
- Status: Infected/Uninfected
- Time Point: 11 dpi/33 dpi
Reading in the sample metadata
The SampleInfo.txt file contains basic information about the samples that we will need for the analysis today: name, cell type, status.
# Read the sample information into a data frame
sampleinfo <- read_tsv("data/samplesheet.tsv", col_types = c("cccc"))
sampleinfo %>%
arrange(Status, TimePoint, Replicate)## # A tibble: 12 x 4
## SampleName Replicate Status TimePoint
## <chr> <chr> <chr> <chr>
## 1 SRR7657878 1 Infected d11
## 2 SRR7657881 2 Infected d11
## 3 SRR7657880 3 Infected d11
## 4 SRR7657874 1 Infected d33
## 5 SRR7657882 2 Infected d33
## 6 SRR7657872 3 Infected d33
## 7 SRR7657877 1 Uninfected d11
## 8 SRR7657876 2 Uninfected d11
## 9 SRR7657879 3 Uninfected d11
## 10 SRR7657883 1 Uninfected d33
## 11 SRR7657873 2 Uninfected d33
## 12 SRR7657875 3 Uninfected d33Reading in the count data
Salmon (Patro 2017) was used to quantify gene expression from raw reads against the Ensembl transcriptome GRCm38 version 102 (as described in the previous session).
First we need to read the data into R from the quant.sf files under the salmon directory. To do this we use the tximport function. We need to create a named vector in which the values are the paths to the quant.sf files and the names are sample names that we want in the column headers - these should match the sample names in our sampleinfo table.
The Salmon quantification results are per transcript, we’ll want to summarise to gene level. To this we need a table that relates transcript IDs to gene IDs.
files <- str_c("salmon/", sampleinfo$SampleName, "/quant.sf")
files <- set_names(files, sampleinfo$SampleName)
tx2gene <- read_tsv("references/tx2gene.tsv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## TxID = col_character(),
## GeneID = col_character()
## )txi <- tximport(files, type = "salmon", tx2gene = tx2gene)## reading in files with read_tsv## 1 2 3 4 5 6 7 8 9 10 11 12
## summarizing abundance
## summarizing counts
## summarizing lengthstr(txi)## List of 4
## $ abundance : num [1:35896, 1:12] 20.381 0 1.966 1.059 0.949 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:35896] "ENSMUSG00000000001" "ENSMUSG00000000003" "ENSMUSG00000000028" "ENSMUSG00000000037" ...
## .. ..$ : chr [1:12] "SRR7657878" "SRR7657881" "SRR7657880" "SRR7657874" ...
## $ counts : num [1:35896, 1:12] 1039 0 65 39 8 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:35896] "ENSMUSG00000000001" "ENSMUSG00000000003" "ENSMUSG00000000028" "ENSMUSG00000000037" ...
## .. ..$ : chr [1:12] "SRR7657878" "SRR7657881" "SRR7657880" "SRR7657874" ...
## $ length : num [1:35896, 1:12] 2905 541 1884 2100 480 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:35896] "ENSMUSG00000000001" "ENSMUSG00000000003" "ENSMUSG00000000028" "ENSMUSG00000000037" ...
## .. ..$ : chr [1:12] "SRR7657878" "SRR7657881" "SRR7657880" "SRR7657874" ...
## $ countsFromAbundance: chr "no"head(txi$counts)## SRR7657878 SRR7657881 SRR7657880 SRR7657874 SRR7657882
## ENSMUSG00000000001 1039.000 1005.888 892.000 917.360 1136.691
## ENSMUSG00000000003 0.000 0.000 0.000 0.000 0.000
## ENSMUSG00000000028 65.000 74.000 72.000 44.000 45.999
## ENSMUSG00000000037 39.000 47.000 29.001 54.001 67.000
## ENSMUSG00000000049 8.000 9.000 4.000 4.000 4.000
## ENSMUSG00000000056 2163.468 2067.819 2006.924 1351.675 2367.801
## SRR7657872 SRR7657877 SRR7657876 SRR7657879 SRR7657883
## ENSMUSG00000000001 1259.000 1351.221 1110.999 1067.634 1134.522
## ENSMUSG00000000003 0.000 0.000 0.000 0.000 0.000
## ENSMUSG00000000028 60.000 35.000 52.000 55.999 58.000
## ENSMUSG00000000037 62.000 69.000 35.000 60.000 21.001
## ENSMUSG00000000049 9.001 6.000 10.000 4.000 8.000
## ENSMUSG00000000056 1412.733 2154.230 2121.740 1962.000 2274.701
## SRR7657873 SRR7657875
## ENSMUSG00000000001 1272.003 1065.000
## ENSMUSG00000000003 0.000 0.000
## ENSMUSG00000000028 75.000 54.000
## ENSMUSG00000000037 50.000 28.000
## ENSMUSG00000000049 6.000 9.000
## ENSMUSG00000000056 1693.000 2260.046Save the txi object for use in later sessions.
saveRDS(txi, file = "salmon_outputs/txi.rds")Exercise 1
We have loaded in the raw counts here. These are what we need for the differential expression analysis. For other investigations we might want counts normalised to library size.
tximportallows us to import “transcript per million” (TPM) scaled counts instead.
- Create a new object called
tpmthat contains length scaled TPM counts. You will need to add an extra argument to the command. Use the help page to determine how you need to change the code:?tximport.
A quick intro to dplyr
One of the most complex aspects of learning to work with data in R is getting to grips with subsetting and manipulating data tables. The package dplyr (Wickham et al. 2018) was developed to make this process more intuitive than it is using standard base R processes. It also makes use of a new symbol %>%, called the “pipe,” which makes the code a bit tidier.
In particular we will use the commands:
selectto select columns from a tablefilterto filter rows based on the contents of a column in the tablerenameto rename columns
We will encounter a few more dplyr commands during the course, we will explain their use as we come to them.
If you are familiar with R but not dplyr or tidyverse then we have a very brief introduction here. A more detailed introduction can be found in our online R course
Prepare count matrix
Create a raw counts matrix for data exploration
DESeq2 will use the txi object directly but we will need a counts matrix to do the data exploration.
rawCounts <- round(txi$counts, 0) Filtering the genes
For many analysis methods it is advisable to filter out as many genes as possible before the analysis to decrease the impact of multiple testing correction on false discovery rates. This is normally done by filtering out genes with low numbers of reads and thus likely to be uninformative.
With DESeq2 this is however not necessary as it applies independent filtering during the analysis. On the other hand, some filtering for genes that are very lowly expressed does reduce the size of the data matrix, meaning that less memory is required and processing steps are carried out faster. Furthermore, for the purposes of visualization it is important to remove the genes that are not expressed in order to avoid them dominating the patterns that we observe.
We will keep all genes where the total number of reads across all samples is greater than 5.
# check dimension of count matrix
dim(rawCounts)## [1] 35896 12# for each gene, compute total count and compare to threshold
# keeping outcome in vector of 'logicals' (ie TRUE or FALSE, or NA)
keep <- rowSums(rawCounts) > 5
# summary of test outcome: number of genes in each class:
table(keep, useNA="always") ## keep
## FALSE TRUE <NA>
## 15805 20091 0# subset genes where test was TRUE
filtCounts <- rawCounts[keep,]
# check dimension of new count matrix
dim(filtCounts)## [1] 20091 12Count distribution and Data transformations
Differential expression calculations with DESeq2 uses raw read counts as input, but for visualization purposes we use transformed counts.
Raw counts
Why not raw counts? Two issues:
- Raw counts range is very large
- Variance increases with mean gene expression, this has impact on assessing the relationships.
summary(filtCounts)## SRR7657878 SRR7657881 SRR7657880 SRR7657874
## Min. : 0 Min. : 0 Min. : 0 Min. : 0
## 1st Qu.: 14 1st Qu.: 17 1st Qu.: 15 1st Qu.: 22
## Median : 327 Median : 351 Median : 333 Median : 346
## Mean : 1387 Mean : 1346 Mean : 1330 Mean : 1200
## 3rd Qu.: 1305 3rd Qu.: 1297 3rd Qu.: 1268 3rd Qu.: 1193
## Max. :652317 Max. :590722 Max. :435515 Max. :444447
## SRR7657882 SRR7657872 SRR7657877 SRR7657876
## Min. : 0 Min. : 0 Min. : 0 Min. : 0
## 1st Qu.: 17 1st Qu.: 25 1st Qu.: 15 1st Qu.: 14
## Median : 407 Median : 380 Median : 365 Median : 346
## Mean : 1696 Mean : 1286 Mean : 1536 Mean : 1441
## 3rd Qu.: 1628 3rd Qu.: 1304 3rd Qu.: 1473 3rd Qu.: 1376
## Max. :699342 Max. :418059 Max. :613857 Max. :757857
## SRR7657879 SRR7657883 SRR7657873 SRR7657875
## Min. : 0 Min. : 0 Min. : 0 Min. : 0
## 1st Qu.: 13 1st Qu.: 12 1st Qu.: 24 1st Qu.: 13
## Median : 329 Median : 316 Median : 396 Median : 348
## Mean : 1363 Mean : 1279 Mean : 1430 Mean : 1505
## 3rd Qu.: 1296 3rd Qu.: 1215 3rd Qu.: 1392 3rd Qu.: 1424
## Max. :722647 Max. :652247 Max. :616070 Max. :625798# few outliers affect distribution visualization
boxplot(filtCounts, main='Raw counts', las=2)
# Raw counts mean expression Vs standard Deviation (SD)
plot(rowMeans(filtCounts), rowSds(filtCounts),
main='Raw counts: sd vs mean',
xlim=c(0,10000),
ylim=c(0,5000))
Data transformation
To avoid problems posed by raw counts, they can be transformed. Several transformation methods exist to limit the dependence of variance on mean gene expression:
- Simple log2 transformation
- VST : variance stabilizing transformation
- rlog : regularized log transformation
log2 transformation
Because some genes are not expressed (detected) in some samples, their count are 0. As log2(0) returns -Inf in R which triggers errors by some functions, we add 1 to every count value to create ‘pseudocounts.’ The lowest value then is 1, or 0 on the log2 scale (log2(1) = 0).
# Get log2 counts
logcounts <- log2(filtCounts + 1)
# summary(logcounts[,1]) # summary for first column
# summary(logcounts) # summary for each columnWe will check the distribution of read counts using a boxplot and add some colour to see if there is any difference between sample groups.
# make a colour vector
statusCols <- str_replace_all(sampleinfo$Status, c(Infected="red", Uninfected="orange"))
# Check distributions of samples using boxplots
boxplot(logcounts,
xlab="",
ylab="Log2(Counts)",
las=2,
col=statusCols,
main="Log2(Counts)")
# Let's add a blue horizontal line that corresponds to the median
abline(h=median(logcounts), col="blue")
From the boxplots we see that overall the density distributions of raw log-counts are not identical but still not very different. If a sample is really far above or below the blue horizontal line (overall median) we may need to investigate that sample further.
# Log2 counts standard deviation (sd) vs mean expression
plot(rowMeans(logcounts), rowSds(logcounts),
main='Log2 Counts: sd vs mean')
In contrast to raw counts, with log2 transformed counts lowly expressed genes show higher variation.
VST : variance stabilizing transformation
Variance stabilizing transformation (VST) aims at generating a matrix of values for which variance is constant across the range of mean values, especially for low mean.
The vst function computes the fitted dispersion-mean relation, derives the transformation to apply and accounts for library size.
vst_counts <- vst(filtCounts)
# Check distributions of samples using boxplots
boxplot(vst_counts,
xlab="",
ylab="VST counts",
las=2,
col=statusCols)
# Let's add a blue horizontal line that corresponds to the median
abline(h=median(vst_counts), col="blue")
# VST counts standard deviation (sd) vs mean expression
plot(rowMeans(vst_counts), rowSds(vst_counts),
main='VST counts: sd vs mean')
Exercise 2
- Use the
DESeq2functionrlogto transform the count data. This function also normalises for library size.- Plot the count distribution boxplots with this data
How has this affected the count distributions?
Principal Component Analysis
A principal component analysis (PCA) is an example of an unsupervised analysis, where we don’t specify the grouping of the samples. If the experiment is well controlled and has worked well, we should find that replicate samples cluster closely, whilst the greatest sources of variation in the data should be between treatments/sample groups. It is also an incredibly useful tool for checking for outliers and batch effects.
To run the PCA we should first normalise our data for library size and transform to a log scale. DESeq2 provides two separate commands to do this (vst and rlog). Here we will use the command rlog. rlog performs a log2 scale transformation in a way that compensates for differences between samples for genes with low read count and also normalizes between samples for library size.
You can read more about rlog, its alternative vst and the comparison between the two here.
To plot the PCA results we will use the autoplot function from the ggfortify package (Tang, Horikoshi, and Li 2016). ggfortify is built on top of ggplot2 and is able to recognise common statistical objects such as PCA results or linear model results and automatically generate summary plot of the results in an appropriate manner.
library(ggfortify)
rlogcounts <- rlog(filtCounts)
# run PCA
pcDat <- prcomp(t(rlogcounts))
# plot PCA
autoplot(pcDat)
We can use colour and shape to identify the Cell Type and the Status of each sample.
autoplot(pcDat,
data = sampleinfo,
colour="Status",
shape="TimePoint",
size=5)
Exercise 3
The plot we have generated shows us the first two principle components. This shows us the relationship between the samples according to the two greatest sources of variation. Sometime, particularly with more complex experiments with more than two experimental factors, or where there might be confounding factors, it is helpful to look at more principle components.
- Redraw the plot, but this time plot the 2nd principle component on the x-axis and the 3rd prinicple component on the y axis. To find out how to do the consult the help page for the
prcompdata method for theautoplotfunction:?autoplot.prcomp.
Discussion: What do the PCA plots tell us about our samples?
Let’s identify these samples. The package ggrepel allows us to add text to the plot, but ensures that points that are close together don’t have their labels overlapping (they repel each other).
library(ggrepel)
# setting shape to FALSE causes the plot to default to using the labels instead of points
autoplot(pcDat,
data = sampleinfo,
colour="Status",
shape="TimePoint",
size=5) +
geom_text_repel(aes(x=PC1, y=PC2, label=SampleName), box.padding = 0.8)
The mislabelled samples are SRR7657882, which is labelled as Test but should be Control, and SRR7657873, which is labelled as Control but should be Test. Let’s fix the sample sheet.
We’re going to use another dplyr command mutate.
sampleinfo <- mutate(sampleinfo, Status=case_when(
SampleName=="SRR7657882" ~ "Uninfected",
SampleName=="SRR7657873" ~ "Infected",
TRUE ~ Status))…and export it so that we have the correct version for later use.
write_tsv(sampleinfo, "results/SampleInfo_Corrected.txt")Let’s look at the PCA now.
autoplot(pcDat,
data = sampleinfo,
colour="Status",
shape="TimePoint",
size=5)
Replicate samples from the same group cluster together in the plot, while samples from different groups form separate clusters. This indicates that the differences between groups are larger than those within groups. The biological signal of interest is stronger than the noise (biological and technical) and can be detected.
Also, there appears to be a strong difference between days 11 and 33 post infection for the test group, but the day 11 and day 33 samples for the controls are mixed together.
Clustering in the PCA plot can be used to motivate changes to the design matrix in light of potential batch effects. For example, imagine that the first replicate of each group was prepared at a separate time from the second replicate. If the PCA plot showed separation of samples by time, it might be worthwhile including time in the downstream analysis to account for the time-based effect.