Differential Gene Expression Analysis

Last updated: 2021-07-02

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Knit directory: Bulk_RNAseq/

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Summary

On this page, we will perform differential gene expression analysis to identify genes that show significant differential expression between Covid19 and Healthy samples. The steps involved are:

Importing in the DGEList object prepared in the Setting Up page.
Filtering of low expressed genes to increase our power to detect differentially expressed genes.
Identifying differentially expressed genes using the limma package.
Visualisation of differentially expressed genes using volcano plots and boxplots.
GO enrichment analysis of differentially expressed genes to explore their biological significance, including network plot visualisation.

Load R Packages

The following code chunk contains the R packages that are required to run through the analysis on this page:

# Working with data:
library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(magrittr)
library(readr)
library(tibble)
library(reshape2)

# Visualisation:
library(kableExtra)


Attaching package: 'kableExtra'

The following object is masked from 'package:dplyr':

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library(ggplot2)
library(ggbiplot)

Loading required package: plyr

------------------------------------------------------------------------------

You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)

------------------------------------------------------------------------------


Attaching package: 'plyr'

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    summarize

Loading required package: scales


Attaching package: 'scales'

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Loading required package: grid

library(ggrepel)
library(grid)
library(cowplot)
# Set ggplot2 theme
theme_set(theme_bw())

# Other packages:
library(here)

here() starts at /Users/nhi.hin/Projects/Bulk_RNAseq


Attaching package: 'here'

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library(export)

# Bioconductor packages:
library(AnnotationHub)

Loading required package: BiocGenerics

Loading required package: parallel


Attaching package: 'BiocGenerics'

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Loading required package: BiocFileCache

Loading required package: dbplyr


Attaching package: 'dbplyr'

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library(edgeR)

Loading required package: limma


Attaching package: 'limma'

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library(limma)
library(Glimma)
library(clusterProfiler)

clusterProfiler v3.18.1  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.


Attaching package: 'clusterProfiler'

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library(org.Hs.eg.db)

Loading required package: AnnotationDbi

Loading required package: stats4

Loading required package: Biobase

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


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Attaching package: 'AnnotationDbi'

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library(enrichplot)

Import Data

Below, we are importing in an object called dge, which was prepared in the Setting Up page. This object contains: gene counts, sample metadata, and gene annotations. Unfortunately we don’t have time to cover this in detail, but please at least skim through the Setting Up page to understand how we have imported the data in and gotten it into this format.

dge <- readRDS(here("data", "R", "dge.rds"))

To quickly summarise, the gene counts in the dge object can be accessed using dge$counts, and we can preview the first 5 rows and columns as follows. Each row represents one gene and the columns represent different samples.

dge$counts[1:5,1:5]

      Healthy_1 Healthy_2 Healthy_3 Healthy_4 Healthy_5
A1BG  133.14360 110.14223  94.68670  89.60004  85.81497
A1CF    9.00000   8.00000   1.00000  10.00020   3.00000
A2M    65.00001  31.72058  36.63567  19.22508  27.99998
A2ML1  29.28124  26.30470  20.57935  13.00000   5.00000
A2MP1 262.00034  83.00004  98.99997  55.99998  41.00000

The sample metadata in the dge object can be accessed using dge$samples, and we can preview the first 5 rows and columns as follows. Each row represents one sample and the columns represent information/characteristics about the samples.

dge$samples[1:5,1:5]

            group lib.size norm.factors patient_code age
Healthy_1 Healthy 40594184     1.139493        507-V  53
Healthy_2 Healthy 36139025     1.127571       1189-V  57
Healthy_3 Healthy 29466261     1.180684       1406-V  61
Healthy_4 Healthy 33691447     1.080104       1918-V  56
Healthy_5 Healthy 33097717     1.012251       1951-V  57

Lastly, the gene metadata in the dge object can be accessed using dge$genes, and we can preview the first 5 rows and columns as follows. Each row represents one gene and the columns represent information about that gene.

dge$genes[1:5,1:5]

       gene seqnames    start      end width
A1BG   A1BG       19 58345178 58353492  8315
A1CF   A1CF       10 50799409 50885675 86267
A2M     A2M       12  9067664  9116229 48566
A2ML1 A2ML1       12  8822621  8887001 64381
A2MP1 A2MP1       12  9228533  9275817 47285

To understand the contents of these objects better, try viewing them using the View() function, i.e. View(dge$genes) or View(dge$samples).
The nrow() and ncol() functions can be used to know the number of rows and columns in these objects, e.g.

nrow(dge$samples) # Number of samples

[1] 54

Questions

How many samples in total are in this dataset? How many Healthy/Covid19 samples are there?
How many genes are in this dataset?

Filtering of low-expressed genes

Filtering of low-expressed genes is a standard step in differential gene expression analysis as it helps to increase the power we have to detect differentially expressed genes, by not having to consider those which are expressed at levels too low to be reliable. Please refer to this paper for more background reading.
The cutoff for filtering out low-expressed genes is somewhat arbitrary, and we can decide through plotting density plots like the ones below. Ideally, the filtering should remove the large peak of genes with low expression towards the left of the density plot.
A common guideline is to filter so that we retain genes expressed at least 1 cpm in the smallest group of samples. Here, the smallest group of samples is 10 (we have 10 Healthy samples and 44 Covid-19 samples).
By setting the expression cutoff to 1 cpm in at least 10 samples below, we can see that the peak corresponding to low-expressed genes is successfully largely reduced in the data after filtering.

keepTheseGenes <- (rowSums(cpm(dge) > 1) >= 10) 

beforeFiltering_plot <- dge %>% 
  cpm(log = TRUE) %>% 
  melt %>% 
  dplyr::filter(is.finite(value)) %>% 
  ggplot(aes(x = value, colour = Var2)) +
  geom_density() + 
  guides(colour = FALSE) +
  ggtitle("A. Before filtering") +
  labs(x = "logCPM", y = "Density")

afterFiltering_plot <- dge %>% 
  cpm(log = TRUE) %>% 
  magrittr::extract(keepTheseGenes,) %>%
  melt %>% 
  dplyr::filter(is.finite(value)) %>% 
  ggplot(aes(x = value, colour = Var2)) +
  geom_density() + 
  guides(colour = FALSE) +
  ggtitle("B. After filtering")+
  labs(x = "logCPM", y = "Density")

cowplot::plot_grid(beforeFiltering_plot, afterFiltering_plot)

Version	Author	Date
f62ae4f	Nhi Hin	2021-06-30

Using this threshold, the step below filters out 15097 genes from the original 29302 genes, giving the remaining 14205 to be used in the analysis.

dge <- dge[keepTheseGenes,,keep.lib.sizes = FALSE]