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html c4e09b7 Dave Tang 2024-10-31 Build site.
Rmd 04948c5 Dave Tang 2024-10-31 Export edgeR results
html cfbb929 Dave Tang 2024-10-31 Build site.
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Rmd dda9d85 Dave Tang 2024-10-22 Visualise DEGs
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Rmd f7369ce Dave Tang 2024-10-22 Differential gene expression analysis using edgeR

edgeR carries out:

Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE.

edgeR Manual.

TL;DR

edgeR offers many variants on analyses. The glm approach is more popular than the classic approach as it offers great flexibilities. There are two testing methods under the glm framework: likelihood ratio tests and quasi-likelihood F-tests. The quasi-likelihood (QL) method is highly recommended for differential expression analyses of bulk RNA-seq data as it gives stricter error rate control by accounting for the uncertainty in dispersion estimation. The likelihood ratio test can be useful in some special cases such as single cell RNA-seq and datasets with no replicates.

  1. Create DGEList object using a count table and a grouping factor.
  2. Remove genes that are lowly expressed.
  3. Normalise data using normLibSizes().
  4. Create design matrix using model.matrix().
  5. Estimate dispersions using estimateDisp().
  6. Fit model using the read counts using glmQLFit().
  7. Conduct F-tests using glmQLFTest().

Installation

Install using BiocManager::install().

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("edgeR")

Data

Use data from An example RNA-seq count table using nf-core/rnaseq.

my_url <- 'https://zenodo.org/records/13970886/files/rsem.merged.gene_counts.tsv?download=1'
my_file <- 'rsem.merged.gene_counts.tsv'

if(file.exists(my_file) == FALSE){
  download.file(url = my_url, destfile = my_file)
}

gene_counts <- read_tsv("rsem.merged.gene_counts.tsv", show_col_types = FALSE)
head(gene_counts)
# A tibble: 6 × 10
  gene_id   `transcript_id(s)` ERR160122 ERR160123 ERR160124 ERR164473 ERR164550
  <chr>     <chr>                  <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
1 ENSG0000… ENST00000373020,E…        2         6         5      374       1637 
2 ENSG0000… ENST00000373031,E…       19        40        28        0          1 
3 ENSG0000… ENST00000371582,E…      268.      274.      429.     489        637 
4 ENSG0000… ENST00000367770,E…      360.      449.      566.     363.       606.
5 ENSG0000… ENST00000286031,E…      156.      185.      265.      85.4      312.
6 ENSG0000… ENST00000374003,E…       24        23        40     1181        423 
# ℹ 3 more variables: ERR164551 <dbl>, ERR164552 <dbl>, ERR164554 <dbl>

Metadata.

tibble::tribble(
  ~sample, ~run_id, ~group,
  "C2_norm", "ERR160122", "normal",
  "C3_norm", "ERR160123", "normal",
  "C5_norm", "ERR160124", "normal",
  "C1_norm", "ERR164473", "normal",
  "C1_cancer", "ERR164550", "cancer",
  "C2_cancer", "ERR164551", "cancer",
  "C3_cancer", "ERR164552", "cancer",
  "C5_cancer", "ERR164554", "cancer"
) -> my_metadata

my_metadata$group <- factor(my_metadata$group, levels = c('normal', 'cancer'))

DGEList

The input to edgeR is the DGEList object. The required inputs for creating a DGEList object is the count table and a grouping factor.

gene_counts |>
  dplyr::select(starts_with("ERR")) |>
  mutate(across(everything(), as.integer)) |>
  as.matrix() -> gene_counts_mat

row.names(gene_counts_mat) <- gene_counts$gene_id

idx <- match(colnames(gene_counts_mat), my_metadata$run_id)
colnames(gene_counts_mat) <- my_metadata$sample[idx]

y <- DGEList(
  counts = gene_counts_mat,
  group = my_metadata$group[idx]
)

y
An object of class "DGEList"
$counts
                C2_norm C3_norm C5_norm C1_norm C1_cancer C2_cancer C3_cancer
ENSG00000000003       2       6       5     374      1637       650      1015
ENSG00000000005      19      40      28       0         1         0         0
ENSG00000000419     268     273     428     489       637       879      1157
ENSG00000000457     360     449     566     362       605       708       632
ENSG00000000460     155     184     264      85       312       239       147
                C5_cancer
ENSG00000000003       562
ENSG00000000005         0
ENSG00000000419       729
ENSG00000000457       478
ENSG00000000460       156
63135 more rows ...

$samples
           group lib.size norm.factors
C2_norm   normal  4441588            1
C3_norm   normal  5349291            1
C5_norm   normal  7613422            1
C1_norm   normal 15972904            1
C1_cancer cancer 22329493            1
C2_cancer cancer 29928921            1
C3_cancer cancer 24891206            1
C5_cancer cancer 23703606            1

Filtering to remove low counts

Remove genes that are lowly expressed.

keep <- rowSums(cpm(y) > 0.5) >= 2
y <- y[keep, , keep.lib.sizes=FALSE]
y
An object of class "DGEList"
$counts
                C2_norm C3_norm C5_norm C1_norm C1_cancer C2_cancer C3_cancer
ENSG00000000003       2       6       5     374      1637       650      1015
ENSG00000000005      19      40      28       0         1         0         0
ENSG00000000419     268     273     428     489       637       879      1157
ENSG00000000457     360     449     566     362       605       708       632
ENSG00000000460     155     184     264      85       312       239       147
                C5_cancer
ENSG00000000003       562
ENSG00000000005         0
ENSG00000000419       729
ENSG00000000457       478
ENSG00000000460       156
37419 more rows ...

$samples
           group lib.size norm.factors
C2_norm   normal  4431282            1
C3_norm   normal  5337400            1
C5_norm   normal  7594512            1
C1_norm   normal 15964680            1
C1_cancer cancer 22317658            1
C2_cancer cancer 29912740            1
C3_cancer cancer 24876336            1
C5_cancer cancer 23693355            1

Normalisation for composition bias

The normLibSizes() function normalizes the library sizes in such a way to minimize the log-fold changes between the samples for most genes. The default method for computing these scale factors uses a trimmed mean of M-values (TMM) between each pair of samples. We call the product of the original library size and the scaling factor the effective library size, i.e., the normalized library size. The effective library size replaces the original library size in all downstream analyses

y <- normLibSizes(y)
y
An object of class "DGEList"
$counts
                C2_norm C3_norm C5_norm C1_norm C1_cancer C2_cancer C3_cancer
ENSG00000000003       2       6       5     374      1637       650      1015
ENSG00000000005      19      40      28       0         1         0         0
ENSG00000000419     268     273     428     489       637       879      1157
ENSG00000000457     360     449     566     362       605       708       632
ENSG00000000460     155     184     264      85       312       239       147
                C5_cancer
ENSG00000000003       562
ENSG00000000005         0
ENSG00000000419       729
ENSG00000000457       478
ENSG00000000460       156
37419 more rows ...

$samples
           group lib.size norm.factors
C2_norm   normal  4431282    1.4420477
C3_norm   normal  5337400    1.4377942
C5_norm   normal  7594512    1.3836402
C1_norm   normal 15964680    0.8389637
C1_cancer cancer 22317658    0.8199077
C2_cancer cancer 29912740    0.7972139
C3_cancer cancer 24876336    0.8151023
C5_cancer cancer 23693355    0.7798397

MDS

plotMDS(y, plot = FALSE)$eigen.vectors[, 1:2] |>
  as.data.frame() |>
  cbind(my_metadata) |>
  dplyr::rename(`Eigenvector 1` = V1, `Eigenvector 2` = V2) |>
  ggplot(aes(`Eigenvector 1`, `Eigenvector 2`, colour = group, label = sample)) +
  geom_point(size = 2) +
  geom_text_repel(show.legend = FALSE) +
  theme_minimal() +
  ggtitle("MDS plot")

Version Author Date
6625ffc Dave Tang 2024-10-22
c9037d1 Dave Tang 2024-10-22

Differential expression

Follow {edgeR} Quick start guide, which recommends using the quasi-likelihood (QL) method for differential expression analyses of bulk RNA-seq data.

design <- model.matrix(~y$samples$group)
y <- estimateDisp(y, design, robust=TRUE)
fit <- glmQLFit(y, design, robust=TRUE)
res <- glmQLFTest(fit)
topTags(res)
Coefficient:  y$samples$groupcancer 
                     logFC   logCPM         F       PValue          FDR
ENSG00000289381  -7.412407 2.341373 103.05773 1.227198e-08 0.0004592667
ENSG00000151834  -8.032643 3.644102  61.61545 9.204327e-07 0.0026899249
ENSG00000220695  -9.133225 4.037457  57.74371 1.112720e-06 0.0026899249
ENSG00000250696  -8.589695 3.809297  57.58050 1.144570e-06 0.0026899249
ENSG00000100985   5.741912 5.769059  80.27623 1.152096e-06 0.0026899249
ENSG00000275332  -7.647863 2.572124  53.65708 1.231408e-06 0.0026899249
ENSG00000196778  -8.811912 4.237774  56.53459 1.463195e-06 0.0026899249
ENSG00000167910  -8.020138 3.453041  55.90088 1.468063e-06 0.0026899249
ENSG00000229894  -9.124263 4.910552  58.92704 1.618181e-06 0.0026899249
ENSG00000243978 -11.487799 6.384022  53.46913 1.802654e-06 0.0026899249

CPM.

my_cpms <- cpm(y, prior.count=2, log=TRUE)
head(my_cpms)
                  C2_norm    C3_norm    C5_norm   C1_norm C1_cancer C2_cancer
ENSG00000000003 -1.159752 -0.1258585 -0.7121002  4.810340  6.485360  4.775676
ENSG00000000005  1.635961  2.4187173  1.4850268 -2.893182 -2.401590 -2.893182
ENSG00000000419  5.394869  5.1582110  5.3528004  5.195507  5.127070  5.209251
ENSG00000000457  5.819453  5.8738922  5.7548326  4.763521  5.053005  4.898404
ENSG00000000460  4.608265  4.5916439  4.6586766  2.696179  4.103099  3.344387
ENSG00000000938  1.959920  1.6469490  1.9786359  6.464501  4.539243  7.133878
                C3_cancer C5_cancer
ENSG00000000003  5.649383  4.933136
ENSG00000000005 -2.893182 -2.893182
ENSG00000000419  5.837817  5.307028
ENSG00000000457  4.968242  4.700693
ENSG00000000460  2.884458  3.100561
ENSG00000000938  5.947951  5.961622

Down-regulated genes with respect to the cancer group.

topTags(res, n = Inf) |>
  as.data.frame() |>
  dplyr::filter(FDR < 0.01) |>
  dplyr::filter(logFC < 0) |>
  dplyr::slice_min(order_by = FDR, n = 20, with_ties = FALSE) |>
  row.names() -> down_genes

my_cpms[down_genes, ] |>
  pheatmap()

Version Author Date
cfbb929 Dave Tang 2024-10-31
6aa4e2d Dave Tang 2024-10-22

Up-regulated genes with respect to the cancer group.

topTags(res, n = Inf) |>
  as.data.frame() |>
  dplyr::filter(FDR < 0.01) |>
  dplyr::filter(logFC > 0) |>
  dplyr::slice_min(order_by = FDR, n = 20, with_ties = FALSE) |>
  row.names() -> up_genes

my_cpms[up_genes, ] |>
  pheatmap()

Version Author Date
cfbb929 Dave Tang 2024-10-31
6aa4e2d Dave Tang 2024-10-22

Export results

Results are stored in res.

class(res)
[1] "DGELRT"
attr(,"package")
[1] "edgeR"

Specifically, in res$table.

head(res$table)
                     logFC   logCPM           F      PValue
ENSG00000000003  2.7217550 4.832838  4.83066709 0.051010174
ENSG00000000005 -7.0014768 0.540980 18.88444244 0.001429757
ENSG00000000419  0.1201527 5.339964  0.06819814 0.798505091
ENSG00000000457 -0.7077471 5.306090  2.17156913 0.166893688
ENSG00000000460 -0.8962715 3.947381  2.20362195 0.164041956
ENSG00000000938  1.5352102 5.597107  2.02000277 0.183879584

Add adjusted p-values and export.

res$table |>
  dplyr::mutate(adjusted_pvalue = p.adjust(PValue, method = "BH")) |>
  tibble::rownames_to_column("ensembl_gene_id") |>
  readr::write_csv(file = "data/13970886_edger_res.csv")

sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] pheatmap_1.0.12 ggrepel_0.9.6   edgeR_4.4.1     limma_3.62.1   
 [5] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
 [9] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[13] ggplot2_3.5.1   tidyverse_2.0.0 workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.48          bslib_0.8.0        processx_3.8.4    
 [5] lattice_0.22-6     callr_3.7.6        tzdb_0.4.0         vctrs_0.6.5       
 [9] tools_4.4.1        ps_1.8.1           generics_0.1.3     parallel_4.4.1    
[13] fansi_1.0.6        highr_0.11         pkgconfig_2.0.3    RColorBrewer_1.1-3
[17] lifecycle_1.0.4    farver_2.1.2       compiler_4.4.1     git2r_0.35.0      
[21] statmod_1.5.0      munsell_0.5.1      getPass_0.2-4      httpuv_1.6.15     
[25] htmltools_0.5.8.1  sass_0.4.9         yaml_2.3.10        crayon_1.5.3      
[29] later_1.3.2        pillar_1.9.0       jquerylib_0.1.4    whisker_0.4.1     
[33] cachem_1.1.0       tidyselect_1.2.1   locfit_1.5-9.10    digest_0.6.37     
[37] stringi_1.8.4      labeling_0.4.3     rprojroot_2.0.4    fastmap_1.2.0     
[41] grid_4.4.1         colorspace_2.1-1   cli_3.6.3          magrittr_2.0.3    
[45] utf8_1.2.4         withr_3.0.2        scales_1.3.0       promises_1.3.0    
[49] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.28     httr_1.4.7        
[53] bit_4.5.0          hms_1.1.3          evaluate_1.0.1     knitr_1.48        
[57] rlang_1.1.4        Rcpp_1.0.13        glue_1.8.0         rstudioapi_0.17.1 
[61] vroom_1.6.5        jsonlite_1.8.9     R6_2.5.1           fs_1.6.4