Last updated: 2022-10-07
Checks: 7 0
Knit directory: ChromatinSplicingQTLs/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). 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(20191126)
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 149e3ef. 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: analysis/.Rhistory
Ignored: code/.DS_Store
Ignored: code/.RData
Ignored: code/._.DS_Store
Ignored: code/._README.md
Ignored: code/._report.html
Ignored: code/.ipynb_checkpoints/
Ignored: code/.snakemake/
Ignored: code/APA_Processing/
Ignored: code/Alignments/
Ignored: code/ChromHMM/
Ignored: code/ENCODE/
Ignored: code/ExpressionAnalysis/
Ignored: code/FastqFastp/
Ignored: code/FastqFastpSE/
Ignored: code/Genotypes/
Ignored: code/IntronSlopes/
Ignored: code/Misc/
Ignored: code/MiscCountTables/
Ignored: code/Multiqc/
Ignored: code/Multiqc_chRNA/
Ignored: code/NonCodingRNA_annotation/
Ignored: code/PeakCalling/
Ignored: code/Phenotypes/
Ignored: code/PlotGruberQTLs/
Ignored: code/PlotQTLs/
Ignored: code/ProCapAnalysis/
Ignored: code/QC/
Ignored: code/QTL_SNP_Enrichment/
Ignored: code/QTLs/
Ignored: code/ReferenceGenome/
Ignored: code/Rplots.pdf
Ignored: code/Session.vim
Ignored: code/SplicingAnalysis/
Ignored: code/TODO
Ignored: code/Tehranchi/
Ignored: code/bigwigs/
Ignored: code/bigwigs_FromNonWASPFilteredReads/
Ignored: code/config/.DS_Store
Ignored: code/config/._.DS_Store
Ignored: code/config/.ipynb_checkpoints/
Ignored: code/debug.ipynb
Ignored: code/debug_python.ipynb
Ignored: code/deepTools/
Ignored: code/featureCounts/
Ignored: code/gwas_summary_stats/
Ignored: code/hyprcoloc/
Ignored: code/igv_session.xml
Ignored: code/log
Ignored: code/logs/
Ignored: code/notebooks/.ipynb_checkpoints/
Ignored: code/rules/.QTL_SNP_Enrichment.smk.swp
Ignored: code/rules/.ipynb_checkpoints/
Ignored: code/rules/OldRules/
Ignored: code/rules/notebooks/
Ignored: code/scratch/
Ignored: code/scripts/.ipynb_checkpoints/
Ignored: code/scripts/GTFtools_0.8.0/
Ignored: code/scripts/__pycache__/
Ignored: code/scripts/liftOverBedpe/liftOverBedpe.py
Ignored: code/snakemake.log
Ignored: code/snakemake.sbatch.log
Ignored: data/.DS_Store
Ignored: data/._.DS_Store
Ignored: data/._20220414203249_JASPAR2022_combined_matrices_25818_jaspar.txt
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-10.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-11.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-2.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-3.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-4.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-5.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-6.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-7.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-8.csv
Ignored: data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022.csv
Untracked files:
Untracked: analysis/20220713_PlotHeatmapManyWays_JustPi1.Rmd
Untracked: code/snakemake_profiles/slurm/__pycache__/
Unstaged changes:
Modified: analysis/20220713_PlotHeatmapManyWays.Rmd
Modified: code/scripts/GenometracksByGenotype
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/index.Rmd
) and HTML (docs/index.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 |
---|---|---|---|---|
html | 149e3ef | Benjmain Fair | 2022-10-07 | update enrichment nb |
html | 311db7d | Benjmain Fair | 2022-09-29 | explore intron sum nb |
html | f94d014 | Benjmain Fair | 2022-09-22 | edit index |
html | a80e66e | Benjmain Fair | 2022-09-22 | update site |
html | bfb625e | Benjmain Fair | 2022-08-09 | added finemapping snp enrichment rules |
html | 125a583 | Benjmain Fair | 2022-06-08 | update site |
html | fe6333b | Benjmain Fair | 2022-05-25 | update site and molQTL coloc |
html | 0c8ee9c | Benjmain Fair | 2022-01-18 | update site |
html | 237b549 | Benjmain Fair | 2021-12-28 | rebuilt some site analyses |
html | 2f0181f | Benjmain Fair | 2021-12-15 | Added genewise hyprcoloc rules |
html | 50ad818 | Benjmain Fair | 2021-12-07 | update site |
html | a166e86 | Benjmain Fair | 2021-10-06 | added test notebook |
Rmd | 24331d7 | cfbuenabadn | 2021-09-23 | adding chRNA-seq processing smk |
html | 24331d7 | cfbuenabadn | 2021-09-23 | adding chRNA-seq processing smk |
html | ee655cc | Benjmain Fair | 2021-09-23 | fixed site links |
html | afc10fb | Benjmain Fair | 2021-09-23 | fixed ipynb links on site homepage |
html | 91f067a | Benjmain Fair | 2021-09-23 | added example ipynb to site |
Rmd | 53ba92f | Benjmain Fair | 2021-06-07 | update side and check sample swapping |
html | 53ba92f | Benjmain Fair | 2021-06-07 | update side and check sample swapping |
Rmd | dd0eadb | Benjmain Fair | 2020-08-21 | initial commit |
html | dd0eadb | Benjmain Fair | 2020-08-21 | initial commit |
Welcome to my research website.
This project will investigate the correlation of genetic effects on chromatin, splicing, transcription, and complex phenotypes, using naturally occuring human genetic variation.
We are still collecting data, but I have also been perusing around published data a bit.
Here are links to all of my rendered analysis Rmarkdowns (code here):
library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.7 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 1.3.1 ✔ forcats 0.4.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
rmds <- list.files(path = "../analysis/", pattern="^\\d+.+")
rmd_htmls <- str_replace(rmds, "Rmd$", "html")
for(i in rmd_htmls){
cat("[", i, "](", i,")\n\n", sep="")
}
20200110_PickSamplesForGrowth.html
20200210_PickSamplesToOrder.html
20210604_CheckSampleGenotypes.html
20211207_ExploreColocalizations.html
20211217_GenewiseColocFirstLook.html
20220114_ColocalizationEffectSizeCorrelations.html
20220228_PickCutAndTagSamples.html
202204012_Cluster_TestGwasHarmonisation.html
20220401_Cluster_CheckOutNA18855chRNA.html
20220415_Cluster_CheckTehranchiConcordance.html
20220424_CheckH3K36me3SampleGenotypes.html
20220511_Cluster_CheckH3K36me3QC.html
20220511_ExploreColocsAtDifferentThresholds.html
20220518_Explore_eQTLColocsAtDifferentThresholds.html
20220524_CheckClosestPeakToTSS.html
20220527_ExploreCarlosChromatinSplicingData.html
20220606_PlotColocsForIntuitions.html
20220622_QuantifyColocRateMoreInterpretableWay.html
20220623_CalculatePi1_AllTraits.html
20220627_CalculatePi1_AllTraits_AndCompareToColoc.html
20220628_exploretiertwomethods.html
20220713_PlotHeatmapManyWays.html
20220713_PlotHeatmapManyWays_JustPi1.html
20220728_CheckFinemapSNPEnrichments.html
20220920_Explore_chRNA_sQTLs.html
20220928_ExploreIntronSum.html
20220930_CheckSNP_QTL_Positions.html
Also, a list of links to ipynb files that were incorporated into the snakemake. Since github renders ipynbs directly, this is not a relative link from the site folder, but rather links directly to the file to view on github (code here):
ipynbs <- list.files(path = "../docs/", pattern="^\\d+.+ipynb$")
for(i in ipynbs){
cat("[", i, "](","https://github.com/bfairkun/ChromatinSplicingQTLs/blob/master/docs/", i,")\n\n", sep="")
}
20210921_CountSpliceSiteSNPsInSQTLs.py.ipynb
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.4.0 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
[5] readr_1.3.1 tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
[9] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] tidyselect_1.1.2 xfun_0.31 haven_2.3.1 colorspace_1.4-1
[5] vctrs_0.4.1 generics_0.1.3 htmltools_0.5.3 yaml_2.2.0
[9] utf8_1.1.4 rlang_1.0.5 later_0.8.0 pillar_1.7.0
[13] withr_2.5.0 glue_1.6.2 DBI_1.1.0 dbplyr_1.4.2
[17] readxl_1.3.1 modelr_0.1.8 lifecycle_1.0.1 cellranger_1.1.0
[21] munsell_0.5.0 gtable_0.3.0 workflowr_1.6.2 rvest_0.3.5
[25] evaluate_0.15 knitr_1.39 fastmap_1.1.0 httpuv_1.5.1
[29] fansi_0.4.0 broom_1.0.0 Rcpp_1.0.5 promises_1.0.1
[33] backports_1.4.1 scales_1.1.0 jsonlite_1.6 fs_1.5.2
[37] hms_0.5.3 digest_0.6.20 stringi_1.4.3 rprojroot_2.0.2
[41] grid_3.6.1 cli_3.3.0 tools_3.6.1 magrittr_1.5
[45] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2 ellipsis_0.3.2
[49] xml2_1.3.2 reprex_0.3.0 lubridate_1.7.4 assertthat_0.2.1
[53] rmarkdown_1.13 httr_1.4.4 rstudioapi_0.14 R6_2.4.0
[57] git2r_0.26.1 compiler_3.6.1