Last updated: 2023-02-24
Checks: 5 2
Knit directory: TranscriptDE-wf/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown is untracked by Git. To know which version of the R
Markdown file created these results, you’ll want to first commit it to
the Git repo. If you’re still working on the analysis, you can ignore
this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the
HTML.
The global environment had objects present when the code in the R
Markdown file was run. These objects 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. Use wflow_publish or
wflow_build to ensure that the code is always run in an
empty environment.
The following objects were defined in the global environment when these results were created:
| Name | Class | Size |
|---|---|---|
| cmdpipe | pipe;connection | 584 bytes |
| ml | function | 784 bytes |
| mlre | character | 112 bytes |
| mlstatus | logical | 56 bytes |
| module | function | 6.1 Kb |
The command set.seed(20221115) 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 24d66f4. 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: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: ._.DS_Store
Ignored: .gitignore
Ignored: analysis/simulation-complete_cache/
Ignored: analysis/simulation-paper_cache/
Ignored: code/.DS_Store
Ignored: code/._.DS_Store
Ignored: code/mouse/.DS_Store
Ignored: code/mouse/._.DS_Store
Ignored: code/mouse/single-end/.DS_Store
Ignored: code/mouse/single-end/._.DS_Store
Ignored: code/mouse/single-end/salmon/slurm-9574761.out
Ignored: code/pkg/.Rhistory
Ignored: code/pkg/.Rproj.user/
Ignored: code/pkg/pkg.Rproj
Ignored: code/pkg/src/RcppExports.o
Ignored: code/pkg/src/pkg.so
Ignored: code/pkg/src/rcpparma_hello_world.o
Ignored: data/.DS_Store
Ignored: data/._.DS_Store
Ignored: data/annotation/.DS_Store
Ignored: data/annotation/._.DS_Store
Ignored: data/annotation/hg38/
Ignored: data/annotation/mm39/
Ignored: data/mouse/.DS_Store
Ignored: data/mouse/._.DS_Store
Ignored: data/mouse/._readme.txt
Ignored: data/mouse/paired-end/fastq/
Ignored: data/mouse/single-end/fastq/
Ignored: misc/.DS_Store
Ignored: misc/._.DS_Store
Ignored: misc/mouse.Rmd/._figure6.png
Ignored: misc/simulation-paper.Rmd/._figure2.png
Ignored: misc/simulation-paper.Rmd/._figure5.png
Ignored: output/mouse/paired-end/
Ignored: output/mouse/single-end/
Ignored: output/quasi_poisson/
Ignored: output/simulation/
Untracked files:
Untracked: analysis/casestudy-howto.Rmd
Untracked: analysis/casestudy.Rmd
Untracked: analysis/mouse-devel.Rmd
Untracked: analysis/mouse-suz12.Rmd
Untracked: code/lung-se/
Untracked: code/lung/
Untracked: code/mouse-devel/
Untracked: code/mouse-suz12/
Untracked: data/annotation/sequins/
Untracked: data/lung-ont/
Untracked: data/lung-se/
Untracked: data/lung/
Untracked: data/mouse-devel/
Untracked: data/mouse-suz12/
Untracked: misc/casestudy.Rmd/
Untracked: output/lung-se/
Untracked: output/lung/
Untracked: output/mouse-devel/
Untracked: output/mouse-suz12/
Unstaged changes:
Modified: analysis/simulation-howto.Rmd
Modified: code/mouse/single-end/data/runAspera.sh
Modified: data/annotation/readme.txt
Modified: data/mouse/readme.txt
Modified: misc/bibliography/bibliography.bib
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.
There are no past versions. Publish this analysis with
wflow_publish() to start tracking its development.
Our case study analysis is based on three RNA-seq experiments from the human lung cancer cell lines H1975 and HCC827: the paired-end Illumina short read RNA-seq data (GSE172421), the single-end Illumina short read RNA-seq data (GSE86337) and the long read Oxford Nanopore Technologies (ONT) data (GSE172421).
All the RNA-seq experiments are quantified with Salmon
Patro et al. (2017) using an decoy-aware
indexed transcriptome based on the Ensembl-oriented Gencode annotation
version 33 (Ensembl annotation version 99) with added transcripts from
spiked-in sequins (see Dong et al.
(2022)). All necessary annotation files used to build the
transcriptome index are publicly available and can also be downloaded
from the GitHub repository associated to this page.
Run the script code/lung/index/combineAnnotation.sh
to combine the Ensembl-oriented Gencode annotation with the sequins
annotation. This script uses the picard-tools
module.
Run the script code/lung/index/buildIndex.sh to
build Salmon’s transcriptome index. This script uses the
following modules: Salmon, MashMap,
bedtools and the generateDecoyTranscriptome.sh
script.
Run code/lung/data/runAspera.sh to download all
necessary FASTQ files. This script uses the module
aspera.
Run code/lung/salmon/runSalmon.sh to quantify the
RNA-seq reads. This scripts uses the Salmon
module.
Run code/lung/salmon/runWasabi.R to generate
.h5 files from Salmon’s output that can be
read by the DTE method sleuth.
The targets file for this experiment is provided in our GitHub
repository for convenience. See file
data/lung/misc/targets.txt.
Run code/lung-se/data/runAspera.sh to download all
necessary FASTQ files. This script uses the module
aspera.
Run code/lung-se/salmon/runSalmon.sh to quantify the
RNA-seq reads. This scripts uses the Salmon
module.
Run code/lung-se/salmon/runWasabi.R to generate
.h5 files from Salmon’s output that can be
read by the DTE method sleuth.
The targets file for this experiment is provided in our GitHub
repository for convenience. See file
data/lung-se/misc/targets.txt.
For convenience, we provide the ONT long read data in an already
processed DGEList object that can be used in the analysis1. The R
.rds file containing such object is
data/lung-ont/220928_dge.rds. The targets file for the ONT
experiment is can be found at
data/lung-ont/misc/targets.txt.
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRblas.so
LAPACK: /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRlapack.so
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
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 rstudioapi_0.14 knitr_1.42 magrittr_2.0.3
[5] workflowr_1.7.0 R6_2.5.1 rlang_1.0.6 fastmap_1.1.0
[9] fansi_1.0.4 highr_0.10 stringr_1.5.0 tools_4.2.1
[13] xfun_0.37 utf8_1.2.3 cli_3.6.0 git2r_0.31.0
[17] jquerylib_0.1.4 htmltools_0.5.4 rprojroot_2.0.3 yaml_2.3.7
[21] digest_0.6.31 tibble_3.1.8 lifecycle_1.0.3 later_1.3.0
[25] sass_0.4.1 vctrs_0.5.2 promises_1.2.0.1 fs_1.6.1
[29] cachem_1.0.6 glue_1.6.2 evaluate_0.20 rmarkdown_2.20
[33] stringi_1.7.12 bslib_0.4.2 compiler_4.2.1 pillar_1.8.1
[37] jsonlite_1.8.4 httpuv_1.6.5 pkgconfig_2.0.3 Thanks to Dr. Xueyi Dong for processing and generating this file.↩︎