Differential gene expression analysis using edgeR
2024-10-31
Last updated: 2024-10-31
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 92b951c | Dave Tang | 2024-10-31 | Export edgeR results |
| html | 6aa4e2d | Dave Tang | 2024-10-22 | Build site. |
| Rmd | dda9d85 | Dave Tang | 2024-10-22 | Visualise DEGs |
| html | 6625ffc | Dave Tang | 2024-10-22 | Build site. |
| Rmd | 3661a2e | Dave Tang | 2024-10-22 | Additional sample |
| html | c9037d1 | Dave Tang | 2024-10-22 | Build site. |
| 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.
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.
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]
yAn 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 ...
$samples
group lib.size norm.factors
C2_norm normal 4431282 1
C3_norm normal 5337400 1
C5_norm normal 7594512 1
C1_norm normal 15964680 1
C1_cancer cancer 22317658 1
C2_cancer cancer 29912740 1
C3_cancer cancer 24876336 1
C5_cancer cancer 23693355 1Normalisation for composition bias
The
normLibSizes()function normalizes the library sizes in such a way to minimize the log-fold changes between the samples for most genes. The default method for computing these scale factors uses a trimmed mean of M-values (TMM) between each pair of samples. We call the product of the original library size and the scaling factor the effective library size, i.e., the normalized library size. The effective library size replaces the original library size in all downstream analyses
MDS
plotMDS(y, plot = FALSE)$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")
Differential expression
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 -7.411609 2.3413727 156.14405 3.485881e-09 0.0001304556
ENSG00000100985 5.744629 5.7690594 110.89086 1.418804e-06 0.0058527061
ENSG00000275332 -7.648126 2.5721238 59.27705 1.721074e-06 0.0058527061
ENSG00000220695 -9.132437 4.0374574 58.73122 1.982088e-06 0.0058527061
ENSG00000256422 -5.529837 0.5541825 54.99786 2.395813e-06 0.0058527061
ENSG00000151834 -8.035376 3.6441016 61.94292 2.733010e-06 0.0058527061
ENSG00000167910 -8.018047 3.4530411 55.86149 3.928929e-06 0.0058527061
ENSG00000226443 -5.742734 0.9948963 51.37638 4.209874e-06 0.0058527061
ENSG00000250696 -8.580216 3.8092969 52.88815 4.376155e-06 0.0058527061
ENSG00000196778 -8.809149 4.2377739 54.66541 4.439027e-06 0.0058527061CPM.
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 5.961622Down-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 |
|---|---|---|
| 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 |
|---|---|---|
| 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 2.7275199 4.832838 4.2763383 0.068387013
ENSG00000000005 -7.0023947 0.540980 17.6315738 0.002159975
ENSG00000000419 0.1201226 5.339964 0.1140066 0.743147316
ENSG00000000457 -0.7079087 5.306090 3.3541835 0.099292833
ENSG00000000460 -0.8967285 3.947381 2.6625206 0.136187708
ENSG00000000938 1.5368400 5.597107 1.8649388 0.205160993Add 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 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 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_0.9.5 edgeR_4.2.2 limma_3.60.6
[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_0.3.5 xfun_0.44 bslib_0.7.0 processx_3.8.4
[5] lattice_0.22-6 callr_3.7.6 tzdb_0.4.0 vctrs_0.6.5
[9] tools_4.4.0 ps_1.7.6 generics_0.1.3 parallel_4.4.0
[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_4.4.0 git2r_0.33.0
[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_2.3.8 crayon_1.5.2
[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_1.5-9.9 digest_0.6.37
[37] stringi_1.8.4 splines_4.4.0 labeling_0.4.3 rprojroot_2.0.4
[41] fastmap_1.2.0 grid_4.4.0 colorspace_2.1-0 cli_3.6.3
[45] magrittr_2.0.3 utf8_1.2.4 withr_3.0.1 scales_1.3.0
[49] promises_1.3.0 bit64_4.0.5 timechange_0.3.0 rmarkdown_2.27
[53] httr_1.4.7 bit_4.0.5 hms_1.1.3 evaluate_0.24.0
[57] knitr_1.47 rlang_1.1.4 Rcpp_1.0.12 glue_1.7.0
[61] rstudioapi_0.16.0 vroom_1.6.5 jsonlite_1.8.8 R6_2.5.1
[65] fs_1.6.4