Last updated: 2025-05-07
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 990a827. 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/97516b79-8d08-46a6-b329-5d0a25b0be98.h5ad
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/pbmc3k_bpcells_mat/
Ignored: data/pbmc3k_seurat.rds
Ignored: data/pbmc4k_filtered_gene_bc_matrices.tar.gz
Ignored: data/pbmc_1k_v3_filtered_feature_bc_matrix.h5
Ignored: data/pbmc_1k_v3_raw_feature_bc_matrix.h5
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: Nothobranchius_furzeri.Nfu_20140520.113.gtf.gz
Untracked: analysis/bioc_scrnaseq.Rmd
Untracked: bpcells_matrix/
Untracked: data/GCF_043380555.1-RS_2024_12_gene_ontology.gaf.gz
Untracked: m3/
Untracked: pbmc3k_before_filtering.rds
Untracked: pbmc3k_save_rds.rds
Untracked: rsem.merged.gene_counts.tsv
Unstaged changes:
Modified: data/nfurzeri_gene_id_to_go_id.csv.gz
Modified: data/nfurzeri_gene_info.csv.gz
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/turquoise_killifish.Rmd)
and HTML (docs/turquoise_killifish.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 | 990a827 | Dave Tang | 2025-05-07 | Lookup table |
| html | 074ec50 | Dave Tang | 2025-05-07 | Build site. |
| Rmd | 331a019 | Dave Tang | 2025-05-07 | GAF file |
| html | 9228995 | Dave Tang | 2025-03-04 | Build site. |
| Rmd | 225a1d1 | Dave Tang | 2025-03-04 | nothobranchius_furzeri |
The biomaRt package provides an interface to BioMart databases provided by Ensembl.
biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite. The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintain by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining.
For more information, check out the Accessing Ensembl annotation with biomaRt guide.
To begin, install the {biomaRt} package.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("biomaRt")Load package.
packageVersion("biomaRt")[1] '2.62.1'suppressPackageStartupMessages(library(biomaRt))Use mirro.
ensembl <- useEnsembl(biomart = "ensembl", mirror = "asia")
ensemblObject of class 'Mart':
Using the ENSEMBL_MART_ENSEMBL BioMart database
No dataset selected.Connect to the selected BioMart database by using
useMart().
avail_datasets <- listDatasets(ensembl)
head(avail_datasets) dataset description
1 abrachyrhynchus_gene_ensembl Pink-footed goose genes (ASM259213v1)
2 acalliptera_gene_ensembl Eastern happy genes (fAstCal1.3)
3 acarolinensis_gene_ensembl Green anole genes (AnoCar2.0v2)
4 acchrysaetos_gene_ensembl Golden eagle genes (bAquChr1.2)
5 acitrinellus_gene_ensembl Midas cichlid genes (Midas_v5)
6 amelanoleuca_gene_ensembl Giant panda genes (ASM200744v2)
version
1 ASM259213v1
2 fAstCal1.3
3 AnoCar2.0v2
4 bAquChr1.2
5 Midas_v5
6 ASM200744v2Look for human datasets by searching the description column.
idx <- grep(pattern = "furzeri", avail_datasets$dataset, ignore.case = TRUE)
avail_datasets[idx, ] dataset description version
117 nfurzeri_gene_ensembl Turquoise killifish genes (Nfu_20140520) Nfu_20140520Connect to the selected BioMart database and turquoise killifish dataset.
ensembl <- useEnsembl(biomart = "ensembl", mirror = "asia", dataset=avail_datasets[idx, 'dataset'])
ensemblObject of class 'Mart':
Using the ENSEMBL_MART_ENSEMBL BioMart database
Using the nfurzeri_gene_ensembl datasetBuilding a query, requires three things:
Use listFilters() to show available filters.
avail_filters <- listFilters(ensembl)
head(avail_filters) name description
1 chromosome_name Chromosome/scaffold name
2 start Start
3 end End
4 strand Strand
5 chromosomal_region e.g. 1:100:10000:-1, 1:100000:200000:1
6 with_entrezgene_trans_name With EntrezGene transcript name ID(s)Use listAttributes() to show available attributes.
avail_attributes <- listAttributes(ensembl)
avail_attributes |>
head() name description page
1 ensembl_gene_id Gene stable ID feature_page
2 ensembl_gene_id_version Gene stable ID version feature_page
3 ensembl_transcript_id Transcript stable ID feature_page
4 ensembl_transcript_id_version Transcript stable ID version feature_page
5 ensembl_peptide_id Protein stable ID feature_page
6 ensembl_peptide_id_version Protein stable ID version feature_pageThe getBM() function is the main query function in
{biomaRt}; use it once you have identified your attributes of interest
and filters to use.
gene_ids <- c("ENSNFUG00015000040", "ENSNFUG00015000042", "ENSNFUG00015000043", "ENSNFUG00015000127")
getBM(
attributes=c('ensembl_gene_id', 'external_gene_name', 'description'),
filters = 'ensembl_gene_id',
values = gene_ids,
mart = ensembl
) -> res
res ensembl_gene_id external_gene_name
1 ENSNFUG00015000040
2 ENSNFUG00015000042 irf2b
3 ENSNFUG00015000043 CASP3
4 ENSNFUG00015000127 SLC38A2
description
1
2 interferon regulatory factor 2b [Source:ZFIN;Acc:ZDB-GENE-041212-38]
3 caspase 3 [Source:UniProtKB Gene Name;Acc:A0A8C6K862]
4 solute carrier family 38 member 2 [Source:ZFIN;Acc:ZDB-GENE-030131-9659]All gene IDs.
gtf_file <- "https://ftp.ensembl.org/pub/release-113/gtf/nothobranchius_furzeri/Nothobranchius_furzeri.Nfu_20140520.113.gtf.gz"
if(file.exists(basename(gtf_file)) == FALSE){
download.file(url = gtf_file, destfile = basename(gtf_file))
}
gtf_cols <- c(
"seqname",
"source",
"feature",
"start",
"end",
"score",
"strand",
"frame",
"attribute"
)
gtf <- readr::read_tsv(file = gtf_file, comment = "#", col_names = gtf_cols, show_col_types = FALSE)
gtf |>
dplyr::filter(feature == "gene") |>
dplyr::select(attribute) |>
tidyr::separate_rows(attribute, sep = ";\\s*") |>
dplyr::filter(grepl("gene_id", attribute)) |>
tidyr::separate(attribute, c('key', 'value'), sep = "\\s") |>
dplyr::pull(value) |>
gsub(pattern ='"', replacement = "") -> all_gene_ids
length(all_gene_ids) == length(unique(all_gene_ids))[1] TRUEGet gene names and descriptions.
getBM(
attributes=c('ensembl_gene_id', 'external_gene_name', 'description'),
filters = 'ensembl_gene_id',
values = all_gene_ids,
mart = ensembl
) -> all_gene_info
head(all_gene_info) ensembl_gene_id external_gene_name
1 ENSNFUG00015000040
2 ENSNFUG00015000041
3 ENSNFUG00015000042 irf2b
4 ENSNFUG00015000043 CASP3
5 ENSNFUG00015000044 cenpu
6 ENSNFUG00015000045 ST3GAL5
description
1
2
3 interferon regulatory factor 2b [Source:ZFIN;Acc:ZDB-GENE-041212-38]
4 caspase 3 [Source:UniProtKB Gene Name;Acc:A0A8C6K862]
5
6 ST3 beta-galactoside alpha-2,3-sialyltransferase 5 [Source:ZFIN;Acc:ZDB-GENE-060322-1]Save lookup table.
readr::write_csv(x = all_gene_info, file = "data/nfurzeri_gene_info.csv.gz")Find GO attribute names.
grep("^go", avail_attributes$name, ignore.case=TRUE, value=TRUE)[1] "go_id" "go_linkage_type" "goslim_goa_accession"
[4] "goslim_goa_description"Query.
getBM(
attributes=c("ensembl_gene_id", "go_id"),
filters="ensembl_gene_id",
values = all_gene_ids,
mart = ensembl
) -> all_gene_go_ids
head(all_gene_go_ids) ensembl_gene_id go_id
1 ENSNFUG00015000040
2 ENSNFUG00015000041 GO:0007156
3 ENSNFUG00015000041 GO:0005886
4 ENSNFUG00015000041 GO:0007399
5 ENSNFUG00015000041 GO:0098609
6 ENSNFUG00015000041 GO:0050808Save GO table.
readr::write_csv(x = all_gene_go_ids, file = "data/nfurzeri_gene_id_to_go_id.csv.gz")NCBI Genome assembly NfurGRZ-RIMD1 reference.
Download GAF file *_gene_ontology.gaf.gz, which contains
Gene Ontology (GO) annotation of the annotated genes in GO Annotation
File GAF
format.
gaf_url <- 'https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/043/380/555/GCF_043380555.1_NfurGRZ-RIMD1/GCF_043380555.1-RS_2024_12_gene_ontology.gaf.gz'
gaf_file <- paste0("data/", basename(gaf_url))
if(file.exists(gaf_file) == FALSE){
download.file(url = gaf_url, destfile = gaf_file)
}The annotation flat file format is comprised of 17 tab-delimited fields.
gaf_cols <- c("DB","GeneID","Symbol","Qualifier","GO_ID","Reference","Evidence_Code","With_From","Aspect","Gene_Name","Gene_Synonym","Type","Taxon","Date","Assigned_By","Annot_Ext","Gene_Product_Form_ID")
gaf <- readr::read_tsv(file = gaf_file, comment = '!', show_col_types = FALSE, col_names = gaf_cols)
head(gaf)# A tibble: 6 × 17
DB GeneID Symbol Qualifier GO_ID Reference Evidence_Code With_From Aspect
<chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 NCBIGe… 1.07e8 psd2 enables GO:0… PMID:223… IEA InterPro… F
2 NCBIGe… 1.07e8 psd2 enables GO:0… PMID:223… IEA InterPro… F
3 NCBIGe… 1.07e8 psd2 enables GO:0… PMID:223… IEA InterPro… F
4 NCBIGe… 1.07e8 psd2 involved… GO:0… PMID:223… IEA InterPro… P
5 NCBIGe… 1.07e8 cnn2 involved… GO:0… PMID:300… IEA PANTHER:… P
6 NCBIGe… 1.07e8 cnn2 located_… GO:0… PMID:300… IEA PANTHER:… C
# ℹ 8 more variables: Gene_Name <chr>, Gene_Synonym <lgl>, Type <chr>,
# Taxon <chr>, Date <dbl>, Assigned_By <chr>, Annot_Ext <lgl>,
# Gene_Product_Form_ID <lgl>Qualifiers.
table(gaf$Qualifier)
enables involved_in located_in part_of
36393 36175 23210 5183 Evidence code.
table(gaf$Evidence_Code)
IEA
100961 Lookup table; check whether lookup is unique.
library(dplyr)
Attaching package: 'dplyr'The following object is masked from 'package:biomaRt':
selectThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionselect(gaf, GeneID, GO_ID) |>
group_by(GeneID, GO_ID) |>
summarise(n = n(), .groups = "keep") |>
arrange(-n) -> lookup_table
head(lookup_table)# A tibble: 6 × 3
# Groups: GeneID, GO_ID [6]
GeneID GO_ID n
<dbl> <chr> <int>
1 107372345 GO:0005975 2
2 107372345 GO:0016020 2
3 107372345 GO:0016758 2
4 107372351 GO:0004674 2
5 107372352 GO:0017025 2
6 107372360 GO:0004930 2Look at an example of a duplicated entry.
eg <- head(lookup_table, n = 1)
dplyr::filter(gaf, GeneID == eg$GeneID, GO_ID == eg$GO_ID)# A tibble: 2 × 17
DB GeneID Symbol Qualifier GO_ID Reference Evidence_Code With_From Aspect
<chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 NCBIGe… 1.07e8 LOC10… involved… GO:0… PMID:223… IEA InterPro… P
2 NCBIGe… 1.07e8 LOC10… involved… GO:0… PMID:300… IEA UniProtK… P
# ℹ 8 more variables: Gene_Name <chr>, Gene_Synonym <lgl>, Type <chr>,
# Taxon <chr>, Date <dbl>, Assigned_By <chr>, Annot_Ext <lgl>,
# Gene_Product_Form_ID <lgl>
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] dplyr_1.1.4 biomaRt_2.62.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] KEGGREST_1.46.0 xfun_0.48 bslib_0.8.0
[4] httr2_1.1.0 processx_3.8.4 Biobase_2.66.0
[7] tzdb_0.4.0 callr_3.7.6 generics_0.1.3
[10] vctrs_0.6.5 tools_4.4.1 ps_1.8.1
[13] parallel_4.4.1 curl_6.2.1 stats4_4.4.1
[16] tibble_3.2.1 AnnotationDbi_1.68.0 RSQLite_2.3.9
[19] blob_1.2.4 pkgconfig_2.0.3 dbplyr_2.5.0
[22] S4Vectors_0.44.0 lifecycle_1.0.4 GenomeInfoDbData_1.2.13
[25] compiler_4.4.1 stringr_1.5.1 git2r_0.35.0
[28] Biostrings_2.74.1 progress_1.2.3 getPass_0.2-4
[31] httpuv_1.6.15 GenomeInfoDb_1.42.3 htmltools_0.5.8.1
[34] sass_0.4.9 yaml_2.3.10 tidyr_1.3.1
[37] later_1.3.2 pillar_1.10.1 crayon_1.5.3
[40] jquerylib_0.1.4 whisker_0.4.1 cachem_1.1.0
[43] tidyselect_1.2.1 digest_0.6.37 stringi_1.8.4
[46] purrr_1.0.2 rprojroot_2.0.4 fastmap_1.2.0
[49] cli_3.6.3 magrittr_2.0.3 utf8_1.2.4
[52] readr_2.1.5 withr_3.0.2 filelock_1.0.3
[55] prettyunits_1.2.0 UCSC.utils_1.2.0 promises_1.3.2
[58] rappdirs_0.3.3 bit64_4.5.2 rmarkdown_2.28
[61] XVector_0.46.0 httr_1.4.7 bit_4.5.0
[64] png_0.1-8 hms_1.1.3 memoise_2.0.1
[67] evaluate_1.0.1 knitr_1.48 IRanges_2.40.1
[70] BiocFileCache_2.14.0 rlang_1.1.4 Rcpp_1.0.13
[73] glue_1.8.0 DBI_1.2.3 xml2_1.3.6
[76] BiocGenerics_0.52.0 vroom_1.6.5 rstudioapi_0.17.1
[79] jsonlite_1.8.9 R6_2.5.1 fs_1.6.4
[82] zlibbioc_1.52.0