Last updated: 2024-10-23
Checks: 7 0
Knit directory: muse/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200712)
was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version e471135. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish
or
wflow_git_commit
). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data/pbmc3k.csv
Ignored: data/pbmc3k.csv.gz
Ignored: data/pbmc3k/
Ignored: r_packages_4.4.0/
Untracked files:
Untracked: analysis/rsem.merged.gene_counts.tsv
Untracked: rsem.merged.gene_counts.tsv
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/edger_vs_deseq2.Rmd
) and
HTML (docs/edger_vs_deseq2.html
) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote
),
click on the hyperlinks in the table below to view the files as they
were in that past version.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | e471135 | Dave Tang | 2024-10-23 | edgeR versus DESeq2 |
Install packages using BiocManager::install()
.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("edgeR")
BiocManager::install("DESeq2")
https://zenodo.org/records/13970886
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'))
Matrix.
Remove genes that are lowly expressed.
keep <- rowSums(cpm(gene_counts_mat) > 0.5) >= 2
gene_counts_mat <- gene_counts_mat[keep, ]
tail(gene_counts_mat)
C2_norm C3_norm C5_norm C1_norm C1_cancer C2_cancer C3_cancer
ENSG00000293576 0 7 12 0 0 0 0
ENSG00000293586 157 157 193 21 40 15 0
ENSG00000293587 3 3 5 0 2 1 0
ENSG00000293588 4 5 6 1 2 5 2
ENSG00000293595 3 5 3 0 0 0 0
ENSG00000293600 45 59 85 561 789 1099 701
C5_cancer
ENSG00000293576 0
ENSG00000293586 10
ENSG00000293587 3
ENSG00000293588 3
ENSG00000293595 0
ENSG00000293600 845
lung_cancer <- DESeqDataSetFromMatrix(
countData = gene_counts_mat,
colData = my_metadata,
design = ~ group
)
lung_cancer <- DESeq(lung_cancer)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
lung_cancer_res <- results(lung_cancer, pAdjustMethod = "BH")
lung_cancer_res[order(lung_cancer_res$padj), ] |> head(10)
log2 fold change (MLE): group cancer vs normal
Wald test p-value: group cancer vs normal
DataFrame with 10 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue
<numeric> <numeric> <numeric> <numeric> <numeric>
ENSG00000211893 13264.627 6.54532 0.564034 11.60448 3.91049e-31
ENSG00000100985 627.926 5.35553 0.494870 10.82209 2.70522e-27
ENSG00000211892 4174.271 5.09062 0.482865 10.54252 5.50060e-26
ENSG00000169385 214.909 -4.60663 0.486305 -9.47271 2.72667e-21
ENSG00000172288 760.700 -27.98296 2.956493 -9.46492 2.93793e-21
ENSG00000236424 2182.510 -29.42478 3.099437 -9.49359 2.23219e-21
ENSG00000211897 6680.207 5.40162 0.574687 9.39924 5.49588e-21
ENSG00000211966 262.489 5.05816 0.539184 9.38115 6.52570e-21
ENSG00000290677 155.595 -4.71772 0.505092 -9.34033 9.60378e-21
ENSG00000182415 515.257 -27.44538 2.958942 -9.27541 1.76945e-20
padj
<numeric>
ENSG00000211893 1.43867e-26
ENSG00000100985 4.97625e-23
ENSG00000211892 6.74557e-22
ENSG00000169385 1.80144e-17
ENSG00000172288 1.80144e-17
ENSG00000236424 1.80144e-17
ENSG00000211897 2.88848e-17
ENSG00000211966 3.00101e-17
ENSG00000290677 3.92581e-17
ENSG00000182415 5.91801e-17
my_thres <- 0.01
topTags(res, n = Inf, adjust.method = "BH") |>
as.data.frame() |>
dplyr::filter(FDR < my_thres) |>
row.names() -> edger_degs
lung_cancer_res |>
as.data.frame() |>
dplyr::filter(padj < my_thres) |>
row.names() -> deseq2_degs
jaccard_index <- function(set1, set2) {
length(intersect(set1, set2)) / length(union(set1, set2))
}
jaccard_index(edger_degs, deseq2_degs)
[1] 0.2256522
DESeq2 returns a lot more differentially expressed genes (DEGs) than edgeR.
length(edger_degs)
[1] 3918
length(deseq2_degs)
[1] 17363
Compare top subset.
compare_degs <- function(my_topn){
topTags(res, n = Inf, adjust.method = "BH") |>
as.data.frame() |>
dplyr::filter(FDR < my_thres) |>
dplyr::slice_min(order_by = FDR, n = my_topn) |>
row.names() -> edger_degs_topn
lung_cancer_res |>
as.data.frame() |>
dplyr::filter(padj < my_thres) |>
dplyr::slice_min(order_by = padj, n = my_topn) |>
row.names() -> deseq2_degs_topn
jaccard_index(edger_degs_topn, deseq2_degs_topn)
}
compare_degs(500)
[1] 0.245122
Jaccard indexes.
ns <- seq(100, 3500, 100)
jis <- sapply(ns, compare_degs)
Plot.
data.frame(n = ns, index = jis) |>
ggplot(aes(n, index)) +
geom_point() +
theme_minimal() +
labs(x = 'Subset size', y = 'Jaccard Index')
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] pheatmap_1.0.12 ggrepel_0.9.5
[3] DESeq2_1.44.0 SummarizedExperiment_1.34.0
[5] Biobase_2.64.0 MatrixGenerics_1.16.0
[7] matrixStats_1.3.0 GenomicRanges_1.56.1
[9] GenomeInfoDb_1.40.1 IRanges_2.38.1
[11] S4Vectors_0.42.1 BiocGenerics_0.50.0
[13] edgeR_4.2.1 limma_3.60.4
[15] lubridate_1.9.3 forcats_1.0.0
[17] stringr_1.5.1 dplyr_1.1.4
[19] purrr_1.0.2 readr_2.1.5
[21] tidyr_1.3.1 tibble_3.2.1
[23] ggplot2_3.5.1 tidyverse_2.0.0
[25] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 farver_2.1.2 fastmap_1.2.0
[4] promises_1.3.0 digest_0.6.37 timechange_0.3.0
[7] lifecycle_1.0.4 statmod_1.5.0 processx_3.8.4
[10] magrittr_2.0.3 compiler_4.4.0 rlang_1.1.4
[13] sass_0.4.9 tools_4.4.0 utf8_1.2.4
[16] yaml_2.3.8 knitr_1.47 labeling_0.4.3
[19] S4Arrays_1.4.1 bit_4.0.5 DelayedArray_0.30.1
[22] RColorBrewer_1.1-3 abind_1.4-5 BiocParallel_1.38.0
[25] withr_3.0.1 grid_4.4.0 fansi_1.0.6
[28] git2r_0.33.0 colorspace_2.1-0 scales_1.3.0
[31] cli_3.6.3 rmarkdown_2.27 crayon_1.5.2
[34] generics_0.1.3 rstudioapi_0.16.0 httr_1.4.7
[37] tzdb_0.4.0 cachem_1.1.0 splines_4.4.0
[40] zlibbioc_1.50.0 parallel_4.4.0 XVector_0.44.0
[43] vctrs_0.6.5 Matrix_1.7-0 jsonlite_1.8.8
[46] callr_3.7.6 hms_1.1.3 bit64_4.0.5
[49] locfit_1.5-9.9 jquerylib_0.1.4 glue_1.7.0
[52] codetools_0.2-20 ps_1.7.6 stringi_1.8.4
[55] gtable_0.3.5 later_1.3.2 UCSC.utils_1.0.0
[58] munsell_0.5.1 pillar_1.9.0 htmltools_0.5.8.1
[61] GenomeInfoDbData_1.2.12 R6_2.5.1 rprojroot_2.0.4
[64] vroom_1.6.5 evaluate_0.24.0 lattice_0.22-6
[67] highr_0.11 httpuv_1.6.15 bslib_0.7.0
[70] Rcpp_1.0.12 SparseArray_1.4.8 whisker_0.4.1
[73] xfun_0.44 fs_1.6.4 getPass_0.2-4
[76] pkgconfig_2.0.3