Last updated: 2025-02-24
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 bba6082. 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: .Rproj.user/
Ignored: data/1M_neurons_filtered_gene_bc_matrices_h5.h5
Ignored: data/293t/
Ignored: data/293t_3t3_filtered_gene_bc_matrices.tar.gz
Ignored: data/293t_filtered_gene_bc_matrices.tar.gz
Ignored: data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/5k_Human_Donor3_PBMC_3p_gem-x_5k_Human_Donor3_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/5k_Human_Donor4_PBMC_3p_gem-x_5k_Human_Donor4_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/Parent_SC3v3_Human_Glioblastoma_filtered_feature_bc_matrix.tar.gz
Ignored: data/brain_counts/
Ignored: data/cl.obo
Ignored: data/cl.owl
Ignored: data/jurkat/
Ignored: data/jurkat:293t_50:50_filtered_gene_bc_matrices.tar.gz
Ignored: data/jurkat_293t/
Ignored: data/jurkat_filtered_gene_bc_matrices.tar.gz
Ignored: data/pbmc20k/
Ignored: data/pbmc20k_seurat/
Ignored: data/pbmc3k/
Ignored: data/pbmc4k_filtered_gene_bc_matrices.tar.gz
Ignored: data/refdata-gex-GRCh38-2020-A.tar.gz
Ignored: data/seurat_1m_neuron.rds
Ignored: data/t_3k_filtered_gene_bc_matrices.tar.gz
Ignored: r_packages_4.4.1/
Untracked files:
Untracked: analysis/bioc_scrnaseq.Rmd
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/annotation_hub.Rmd) and
HTML (docs/annotation_hub.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 | bba6082 | Dave Tang | 2025-02-24 | Checking out AnnotationHub |
https://bioconductor.org/packages/release/bioc/vignettes/AnnotationHub/inst/doc/AnnotationHub.html
The AnnotationHub package provides a client interface to resources stored at the AnnotationHub web service.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("AnnotationHub")suppressPackageStartupMessages(library(AnnotationHub))
packageVersion("AnnotationHub")[1] '3.14.0'The AnnotationHub package is straightforward to use. Create an
AnnotationHub object.
ah <- AnnotationHub()Now at this point you have already done everything you need in order
to start retrieving annotations. For most operations, using the
AnnotationHub object should feel a lot like working with a
familiar list or data.frame.
ahAnnotationHub with 72098 records
# snapshotDate(): 2024-10-28
# $dataprovider: Ensembl, BroadInstitute, UCSC, ftp://ftp.ncbi.nlm.nih.gov/g...
# $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Rattus norv...
# $rdataclass: GRanges, TwoBitFile, BigWigFile, EnsDb, Rle, OrgDb, SQLiteFil...
# additional mcols(): taxonomyid, genome, description,
# coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
# rdatapath, sourceurl, sourcetype
# retrieve records with, e.g., 'object[["AH5012"]]'
title
AH5012 | Chromosome Band
AH5013 | STS Markers
AH5014 | FISH Clones
AH5015 | Recomb Rate
AH5016 | ENCODE Pilot
... ...
AH119504 | Ensembl 113 EnsDb for Xiphophorus maculatus
AH119505 | Ensembl 113 EnsDb for Xenopus tropicalis
AH119506 | Ensembl 113 EnsDb for Zonotrichia albicollis
AH119507 | Ensembl 113 EnsDb for Zalophus californianus
AH119508 | Ensembl 113 EnsDb for Zosterops lateralis melanopsYou can see that it gives you an idea about the different types of data that are present inside the hub. You can see where the data is coming from (dataprovider), as well as what species have samples present (species), what kinds of R data objects could be returned (rdataclass). We can take a closer look at all the kinds of data providers that are available by simply looking at the contents of dataprovider as if it were the column of a data.frame object like this:
unique(ah$dataprovider) [1] "UCSC"
[2] "Ensembl"
[3] "RefNet"
[4] "Inparanoid8"
[5] "NHLBI"
[6] "ChEA"
[7] "Pazar"
[8] "NIH Pathway Interaction Database"
[9] "Haemcode"
[10] "BroadInstitute"
[11] "PRIDE"
[12] "Gencode"
[13] "CRIBI"
[14] "Genoscope"
[15] "MISO, VAST-TOOLS, UCSC"
[16] "Stanford"
[17] "dbSNP"
[18] "BioMart"
[19] "GeneOntology"
[20] "KEGG"
[21] "URGI"
[22] "EMBL-EBI"
[23] "MicrosporidiaDB"
[24] "FungiDB"
[25] "TriTrypDB"
[26] "ToxoDB"
[27] "AmoebaDB"
[28] "PlasmoDB"
[29] "PiroplasmaDB"
[30] "CryptoDB"
[31] "TrichDB"
[32] "GiardiaDB"
[33] "The Gene Ontology Consortium"
[34] "ENCODE Project"
[35] "SchistoDB"
[36] "NCBI/UniProt"
[37] "GENCODE"
[38] "http://www.pantherdb.org"
[39] "RMBase v2.0"
[40] "snoRNAdb"
[41] "tRNAdb"
[42] "NCBI"
[43] "DrugAge, DrugBank, Broad Institute"
[44] "DrugAge"
[45] "DrugBank"
[46] "Broad Institute"
[47] "HMDB, EMBL-EBI, EPA"
[48] "STRING"
[49] "OMA"
[50] "OrthoDB"
[51] "PathBank"
[52] "EBI/EMBL"
[53] "NCBI,DBCLS"
[54] "FANTOM5,DLRP,IUPHAR,HPRD,STRING,SWISSPROT,TREMBL,ENSEMBL,CELLPHONEDB,BADERLAB,SINGLECELLSIGNALR,HOMOLOGENE"
[55] "WikiPathways"
[56] "VAST-TOOLS"
[57] "pyGenomeTracks "
[58] "NA"
[59] "UoE"
[60] "TargetScan,miRTarBase,USCS,ENSEMBL"
[61] "TargetScan"
[62] "QuickGO"
[63] "CIS-BP"
[64] "CTCFBSDB 2.0"
[65] "HOCOMOCO v11"
[66] "JASPAR 2022"
[67] "Jolma 2013"
[68] "SwissRegulon"
[69] "ENCODE SCREEN v3"
[70] "MassBank"
[71] "excluderanges"
[72] "ENCODE"
[73] "GitHub"
[74] "Stanford.edu"
[75] "Publication"
[76] "CHM13"
[77] "UCSChub"
[78] "Google DeepMind"
[79] "UWashington"
[80] "Bioconductor"
[81] "ENCODE cCREs"
[82] "The Human Phenotype Ontology"
[83] "MGI"
[84] "ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/" In the same way, you can also see data from different species inside the hub by looking at the contents of species.
head(unique(ah$species))[1] "Homo sapiens" "Vicugna pacos" "Dasypus novemcinctus"
[4] "Otolemur garnettii" "Papio hamadryas" "Papio anubis" One can get chain files for Drosophila melanogaster from UCSC with:
dm <- query(ah, c("ChainFile", "UCSC", "Drosophila melanogaster"))
dmAnnotationHub with 45 records
# snapshotDate(): 2024-10-28
# $dataprovider: UCSC
# $species: Drosophila melanogaster
# $rdataclass: ChainFile
# additional mcols(): taxonomyid, genome, description,
# coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
# rdatapath, sourceurl, sourcetype
# retrieve records with, e.g., 'object[["AH15102"]]'
title
AH15102 | dm3ToAnoGam1.over.chain.gz
AH15103 | dm3ToApiMel3.over.chain.gz
AH15104 | dm3ToDm2.over.chain.gz
AH15105 | dm3ToDm6.over.chain.gz
AH15106 | dm3ToDp3.over.chain.gz
... ...
AH15142 | dm2ToDroVir3.over.chain.gz
AH15143 | dm2ToDroWil1.over.chain.gz
AH15144 | dm2ToDroYak1.over.chain.gz
AH15145 | dm2ToDroYak2.over.chain.gz
AH15146 | dm1ToDm2.over.chain.gz
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] AnnotationHub_3.14.0 BiocFileCache_2.14.0 dbplyr_2.5.0
[4] BiocGenerics_0.52.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] KEGGREST_1.46.0 xfun_0.48 bslib_0.8.0
[4] processx_3.8.4 Biobase_2.66.0 callr_3.7.6
[7] vctrs_0.6.5 tools_4.4.1 ps_1.8.1
[10] generics_0.1.3 stats4_4.4.1 curl_5.2.3
[13] tibble_3.2.1 fansi_1.0.6 AnnotationDbi_1.68.0
[16] RSQLite_2.3.7 blob_1.2.4 pkgconfig_2.0.3
[19] S4Vectors_0.44.0 GenomeInfoDbData_1.2.13 lifecycle_1.0.4
[22] compiler_4.4.1 stringr_1.5.1 git2r_0.35.0
[25] Biostrings_2.74.1 getPass_0.2-4 GenomeInfoDb_1.42.3
[28] httpuv_1.6.15 htmltools_0.5.8.1 sass_0.4.9
[31] yaml_2.3.10 later_1.3.2 pillar_1.9.0
[34] crayon_1.5.3 jquerylib_0.1.4 whisker_0.4.1
[37] cachem_1.1.0 mime_0.12 tidyselect_1.2.1
[40] digest_0.6.37 stringi_1.8.4 purrr_1.0.2
[43] dplyr_1.1.4 BiocVersion_3.20.0 rprojroot_2.0.4
[46] fastmap_1.2.0 cli_3.6.3 magrittr_2.0.3
[49] utf8_1.2.4 withr_3.0.2 UCSC.utils_1.2.0
[52] filelock_1.0.3 promises_1.3.0 rappdirs_0.3.3
[55] bit64_4.5.2 XVector_0.46.0 rmarkdown_2.28
[58] httr_1.4.7 bit_4.5.0 png_0.1-8
[61] memoise_2.0.1 evaluate_1.0.1 knitr_1.48
[64] IRanges_2.40.1 rlang_1.1.4 Rcpp_1.0.13
[67] glue_1.8.0 DBI_1.2.3 BiocManager_1.30.25
[70] rstudioapi_0.17.1 jsonlite_1.8.9 R6_2.5.1
[73] zlibbioc_1.52.0 fs_1.6.4