Last updated: 2024-07-25
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 39a6fe8. 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: r_packages_4.3.3/
Ignored: r_packages_4.4.0/
Unstaged changes:
Modified: script/run_rstudio.sh
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/sradb.Rmd) and HTML
(docs/sradb.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 | 39a6fe8 | Dave Tang | 2024-07-25 | Querying the SRA |
First download SRAmetadb.sqlite.gz and gunzip it; the
function getSRAdbFile() can do this but I recommend
downloading this file externally. The gzipped file is around 6.8G in
size (2024-07-26) and when uncompressed is 138G in size.
sqlfile <- getSRAdbFile(method = "wget")Download database file externally using wget.
wget -c https://gbnci.cancer.gov/backup/SRAmetadb.sqlite.gzPoint to the location of downloaded and gunzipped file.
sqlfile <- '/data2/sradb/SRAmetadb.sqlite'Create a connection for queries. The standard DBI functionality as implemented in RSQLite function dbConnect makes the connection to the database. The dbDisconnect function disconnects the connection.
sra_con <- dbConnect(SQLite(), sqlfile)How do we get the SRR accession for SRX510365? (Should be SRR1216135)
my_id <- 'SRX510365'
conversion <- sraConvert(my_id, sra_con = sra_con)
conversion experiment submission study sample run
1 SRX510365 SRA090948 SRP025982 SRS588883 SRR1216135Disconnect.
dbDisconnect(conn = sra_con)
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] SRAdb_1.66.0 RCurl_1.98-1.14 graph_1.82.0
[4] BiocGenerics_0.50.0 RSQLite_2.3.7 lubridate_1.9.3
[7] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[10] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[13] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[16] 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] Biobase_2.64.0 callr_3.7.6 tzdb_0.4.0 bitops_1.0-7
[9] vctrs_0.6.5 tools_4.4.0 ps_1.7.6 generics_0.1.3
[13] stats4_4.4.0 fansi_1.0.6 blob_1.2.4 pkgconfig_2.0.3
[17] data.table_1.15.4 lifecycle_1.0.4 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 later_1.3.2
[29] pillar_1.9.0 jquerylib_0.1.4 whisker_0.4.1 limma_3.60.4
[33] cachem_1.1.0 tidyselect_1.2.1 digest_0.6.35 stringi_1.8.4
[37] rprojroot_2.0.4 fastmap_1.2.0 grid_4.4.0 GEOquery_2.72.0
[41] colorspace_2.1-0 cli_3.6.2 magrittr_2.0.3 utf8_1.2.4
[45] withr_3.0.0 scales_1.3.0 promises_1.3.0 bit64_4.0.5
[49] timechange_0.3.0 rmarkdown_2.27 httr_1.4.7 bit_4.0.5
[53] hms_1.1.3 memoise_2.0.1 evaluate_0.24.0 knitr_1.47
[57] rlang_1.1.4 Rcpp_1.0.12 glue_1.7.0 DBI_1.2.3
[61] xml2_1.3.6 rstudioapi_0.16.0 jsonlite_1.8.8 R6_2.5.1
[65] fs_1.6.4