Last updated: 2025-01-24
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 954f4bf. 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/
Ignored: r_packages_4.4.1/
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/gsea.Rmd
) and HTML
(docs/gsea.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 | 954f4bf | Dave Tang | 2025-01-24 | Using the GSEA GUI tool |
GSEA:
Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).
Download page that requires your email address. For this post, I will download GSEA v4.3.3 for Windows.
Text file format for expression dataset.
The TXT format is a tab delimited file format that describes an expression dataset.
The first line contains the labels Name and Description followed by the identifiers for each sample in the dataset.
The Description column is intended to be optional, but there is currently a bug such that it is treated as required. We hope to fix this in a future release. If you have no descriptions available, a value of NA will suffice.
Name(tab)Description(tab)(sample 1 name)(tab)(sample 2 name) (tab) … (sample N name) Name Description DLBC1_1 DLBC2_1 … DLBC58_0
Download some example data Lung_Michigan_collapsed.gct.
Collapsed
refers to datasets whose identifiers (i.e Affymetrix probe set ids) have been replaced with symbols. In this process, all probe sets that map to a particular gene are summarized into a single expression vector by picking the maximum expression value in each sample. A utility to do this is included in the GSEA java software.
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] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 httr_1.4.7 cli_3.6.3 knitr_1.48
[5] rlang_1.1.4 xfun_0.48 stringi_1.8.4 processx_3.8.4
[9] promises_1.3.0 jsonlite_1.8.9 glue_1.8.0 rprojroot_2.0.4
[13] git2r_0.35.0 htmltools_0.5.8.1 httpuv_1.6.15 ps_1.8.1
[17] sass_0.4.9 fansi_1.0.6 rmarkdown_2.28 jquerylib_0.1.4
[21] tibble_3.2.1 evaluate_1.0.1 fastmap_1.2.0 yaml_2.3.10
[25] lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.1 compiler_4.4.1
[29] fs_1.6.4 pkgconfig_2.0.3 Rcpp_1.0.13 rstudioapi_0.17.1
[33] later_1.3.2 digest_0.6.37 R6_2.5.1 utf8_1.2.4
[37] pillar_1.9.0 callr_3.7.6 magrittr_2.0.3 bslib_0.8.0
[41] tools_4.4.1 cachem_1.1.0 getPass_0.2-4