Code and results for the recount-brain project that enhances the recount2 project project. The recount_brain table can be accessed via the recount (Collado-Torres, Nellore, Kammers, Ellis, et al., 2017) Bioconductor package using recount::add_metadata(source = 'recount_brain_v2').

Contents

• select_studies uses the predicted phenotype information by Shannon Ellis et al. (Ellis, Collado-Torres, Jaffe, and Leek, 2018) version 0.0.03 to determine candidate studies for recount_brain from the Sequence Read Archive (SRA) that have at least 4 samples and over 70% of the samples are from the brain. It creates the list of candidate projects saved in projects_lists.txt.
• SRA_run_selector_info contains a table per study in projects_lists.txt with the data downloaded from the SRA Run Selector website https://www.ncbi.nlm.nih.gov/Traces/study/.
• SRA_metadata contains a CSV table with the curated metadata for each study. This is the data that is then used to create recount_brain. Note that not all candidate studies were brain studies so the final number of projects considered is 62.
• merged_metadata contains the recount_brain table that can be easily accessed via recount (Collado-Torres, Nellore, Kammers, Ellis, et al., 2017) using the add_metadata() function. The document merging_data describes how the recount_brain was created using the files from SRA_metadata and includes some brief examples on how to explore the recount_brain table. You can access this initial version of recount_brain using recount::add_metadata(source = 'recount_brain_v1').
• metadata_reproducibility contains a document describing how the metadata was processed for each SRA study. It is intended to be useful for reproducibility purposes.
• The cross_studies_metadata directory contains the cross_studies_metadata document describing how the recount-brain version 1 table was merged with GTEx and TCGA brain samples metadata to create the recount-brain version 2 table that facilitates cross-study comparisons. SupplementaryTable2.csv describes which fields from the GTEx and TCGA data were used to merge them with recount_brain and any manipulations required to do so. The cross_studies_metadata directory also contains a second document, recount_brain_ontologies, with the code used for adding Broadmann area, disease and tissue ontology information to recount_brain. This final table is the one you can access using recount::add_metadata(source = 'recount_brain_v2').
• metasra_comp contains a comparison of recount_brain_v2 and MetaSRA (Bernstein, Doan, and Dewey, 2017) as described in the metasra_comp html document.

Example analyses

• We used the data from SRP027383 (Bao, Chen, Yang, Zhang, et al., 2014) to show how recount_brain can be used for a gene differential expression analysis. See the full example for more information: example_SRP027383. You can also access the pdf version if you prefer over the HTML version.
• We used the data from ten studies to replicate some of the analyses by Ferreira et al. (Ferreira, Muñoz-Aguirre, Reverter, Godinho, et al., 2018) that explore the relationship between post-mortem interval and gene expression. See the full example for more information: example_PMI. You can also access the pdf version if you prefer it over the HTML version.
• We also illustrate how to perform an analysis across multiple studies present in recount_brain and combining them with specific tissue data from The Cancer Genome Atlas (TCGA). See the full example for more information: example_multistudy. You can also access the pdf version if you prefer it over the HTML version.

List of variables

This information is also available as a csv file at SupplementaryTable1.csv.

1. age: Age of donor
2. age_units: Units of age - (Years / Months / Post Conception Weeks)
3. assay_type_s: Sequencing technique - (RNA-Seq)
4. avgspotlen_l: Average length of sequenced read
5. bioproject_s: NCBI BioProject ID
6. biosample_s: NCBI BioSample ID
7. brain_bank: Brain tissue repository source
8. brodmann_area: Brodmann area for tissue from cerebral cortex - (1-52)
9. cell_line: Cell line description
10. center_name_s: Project center
11. clinical_stage_1: Clinically relevant tissue sample information
12. clinical_stage_2: Clinically relevant tissue sample information
13. consent_s: Data availability - (Public)
14. development: Stage of human development - (Fetus / Infant / Child / Adolescent / Adult)
15. disease: Disease description
16. disease_status: Nature of tissue - (Disease / Control)
17. experiment_s: NCBI Experiment ID
18. hemisphere: Cerebral hemisphere - (Left / Right)
19. insertsize_l: Length of sequence between adaptors
20. instrument_s: High throughput sequencing system
21. library_name_s: Internal sample ID used by original study
22. librarylayout_s: Sequencing layout - (Single / Paired)
23. libraryselection_s: Sequencing library - (cDNA)
24. librarysource_s: Sequencing source - (Transcriptomic)
25. loaddate_s: Sequencing load date
26. mbases_l: Megabases
27. mbytes_l: Megabytes
28. organism_s: Organism - (Homo sapiens)
29. pathology: Tissue pathology
30. platform_s: Sequencing platform - (Illumina)
31. pmi: Postmortem interval
32. pmi_units: Units of postmortem interval - (Hours)
33. preparation: Specimen preparation - (Frozen)
34. present_in_recount: Expression data present in recount2
35. race: Race of donor - (Asian / Black / Hispanic / White)
36. releasedate_s: Sequencing release date
37. rin: RNA integrity number
38. run_s: NCBI Run ID
39. sample_name_s: GEO Accession ID
40. sample_origin: Tissue origin - (Brain / iPSC)
41. sex: Sex of donor - (Female / Male)
42. sra_sample_s: NCBI SRA Sample ID
43. sra_study_s: NCBI SRA Study ID
44. tissue_site_1: Anatomic site of tissue
45. tissue_site_2: Anatomic site of tissue, further specified
46. tissue_site_3: Anatomic site of tissue, further specified
47. tumor_type: Type of tumor - (Glioblastoma / Astrocytoma / Ependymoma / Oligodendroglioma)
48. viability: Tissue viability - (Postmortem / Biopsy)

You can access this initial version with recount::add_metadata(source = 'recount_brain_v1').

List of variables present in recount_brain_v2.

49. Study_full: either the SRA study accession, GTEX or TCGA.
50. drugName_full: the drug name for TCGA samples.
51. drug_info_full: logical, whether the sample has drug information; only for TCGA.
52. drug_type_full: the drug classification (chemotherapy, immunotherapy, …); only for TCGA.
53. full_260_280: the 260 to 280 ratio; only for TCGA.
54. count_file_identifier: the SRA run accession or the TCGA run (sample) identifier. Useful for merging with the rest of recount2 metadata.
55. Dataset: either SRA, GTEX or TCGA.
56. brodmann_ontology: URL for the Brodmann region ontology. See the recount_brain_ontologies file for how this information was added.
57. brodmann_synonyms: synonyms used for the Brodmann regions. These facilitate text based searches. Separated by |.
58. brodmann_parents: URLs for the Brodmann ontology parents. Separated by |.
59. brodmann_parents_label: Brodmann ontology parent text preferred labels. Separated by |.
60. disease_ontology: URL for the disease ontology.
61. tissue: tissue as prioritized by tissue_site_3 over tissue_site_2 over tissue_site_1.
62. tissue_ontology: URL for the tissue ontology.
63. tissue_synonyms: tissue synonyms which facilitate text based searches. Separated by |.
64. tissue_parents: URLs for the tissue ontology parents. Separated by |.
65. tissue_parents_label: tissue ontology parent text preferred labels. Separated by |.

You can access this version with recount::add_metadata(source = 'recount_brain_v2').

List of SRA projects present in recount_brain

1. ERP001304
2. SRP030628
3. SRP031501
4. SRP031868
5. SRP032539
6. SRP032540
7. SRP033078
8. SRP033291
9. SRP034929
10. SRP035524
11. SRP040622
12. SRP002274
13. SRP041044
14. SRP043684
15. SRP045590
16. SRP045638
17. SRP048683
18. SRP049333
19. SRP049776
20. SRP051844
21. SRP053043
22. SRP007483
23. SRP055440
24. SRP055730
25. SRP056287
26. SRP056604
27. SRP056863
28. SRP058181
29. SRP058640
30. SRP059579
31. SRP066394
32. SRP067645
33. SRP017809
34. SRP033725
35. SRP056477
36. SRP043364
37. SRP044668
38. SRP015668
39. SRP027383
40. ERP010930
41. ERP004592
42. DRP000499
43. SRP017933
44. SRP055447
45. SRP057855
46. SRP033464
47. SRP032798
48. SRP035417
49. SRP064454
50. SRP026562
51. SRP044749
52. SRP066009
53. SRP019762
54. SRP011903
55. SRP052294
56. SRP058351
57. SRP034737
58. SRP025982
59. SRP032476
60. SRP020661
61. SRP021924
62. SRP026048

Explore interactively

We recommend opening the interactive recount_brain exploration in another window.

This application is a custom version of shinycsv (Collado-Torres, Semick, and Jaffe, 2018). The code for making this application is available in the shinytable directory.

Questions

If you have any questions about recount_brain please post them as an issue at LieberInstitute/recount-brain and include the relevant session information using the following code. Thank you!

library('sessioninfo')
options(width = 120)
session_info()

References

The analyses were made possible thanks to BioPortal (Whetzel, Noy, Shah, Alexander, et al., 2011), MetaSRA (Bernstein, Doan, and Dewey, 2017), and:

• R (R Core Team, 2019)
• BiocStyle (Oleś, Morgan, and Huber, 2018)
• devtools (Wickham, Hester, and Chang, 2019)
• downloader (Chang, 2015)
• DT (Xie, Cheng, and Tan, 2018)
• knitcitations (Boettiger, 2017)
• knitr (Xie, 2014)
• phenopredict (Ellis, Collado-Torres, Jaffe, and Leek, 2018)
• recount (Collado-Torres, Nellore, Kammers, Ellis, et al., 2017)
• rmarkdown
• sessioninfo (Csárdi, core, Wickham, Chang, et al., 2018)
• shinycsv (Collado-Torres, Semick, and Jaffe, 2018)
• tidyverse (Wickham, 2017)

Bibliography file

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[2] M. N. Bernstein, A. Doan, and C. N. Dewey. “MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive”. In: Bioinformatics 33.18 (May. 2017). Ed. by J. Wren, pp. 2914–2923. DOI: 10.1093/bioinformatics/btx334. URL: https://doi.org/10.1093/bioinformatics/btx334.

[3] C. Boettiger. knitcitations: Citations for ‘Knitr’ Markdown Files. R package version 1.0.8. 2017. URL: https://CRAN.R-project.org/package=knitcitations.

[4] W. Chang. downloader: Download Files over HTTP and HTTPS. R package version 0.4. 2015. URL: https://CRAN.R-project.org/package=downloader.

[5] L. Collado-Torres, A. Nellore, K. Kammers, S. E. Ellis, et al. “Reproducible RNA-seq analysis using recount2”. In: Nature Biotechnology (2017). DOI: 10.1038/nbt.3838. URL: http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.

[6] L. Collado-Torres, S. Semick, and A. E. Jaffe. shinycsv: Explore a table interactively in a shiny application. R package version 0.99.8. 2018. URL: https://github.com/LieberInstitute/shinycsv.

[7] G. Csárdi, R. core, H. Wickham, W. Chang, et al. sessioninfo: R Session Information. R package version 1.1.1. 2018. URL: https://CRAN.R-project.org/package=sessioninfo.

[8] S. E. Ellis, L. Collado-Torres, A. E. Jaffe, and J. T. Leek. “Improving the value of public RNA-seq expression data by phenotype prediction”. In: Nucl. Acids Res. (2018). DOI: 10.1093/nar/gky102. URL: https://doi.org/10.1093/nar/gky102.

[9] P. G. Ferreira, M. Muñoz-Aguirre, F. Reverter, C. P. S. Godinho, et al. “The effects of death and post-mortem cold ischemia on human tissue transcriptomes”. In: Nature Communications 9.1 (Feb. 2018). DOI: 10.1038/s41467-017-02772-x. URL: https://doi.org/10.1038/s41467-017-02772-x.

[10] A. Oleś, M. Morgan, and W. Huber. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.10.0. 2018. URL: https://github.com/Bioconductor/BiocStyle.

[11] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2019. URL: https://www.R-project.org/.

[12] P. L. Whetzel, N. F. Noy, N. H. Shah, P. R. Alexander, et al. “BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications”. In: Nucleic Acids Research 39.suppl (Jun. 2011), pp. W541–W545. DOI: 10.1093/nar/gkr469. URL: https://doi.org/10.1093/nar/gkr469.

[13] H. Wickham. tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. 2017. URL: https://CRAN.R-project.org/package=tidyverse.

[14] H. Wickham, J. Hester, and W. Chang. devtools: Tools to Make Developing R Packages Easier. R package version 2.0.2. 2019. URL: https://CRAN.R-project.org/package=devtools.

[15] Y. Xie. “knitr: A Comprehensive Tool for Reproducible Research in R”. In: Implementing Reproducible Computational Research. Ed. by V. Stodden, F. Leisch and R. D. Peng. ISBN 978-1466561595. Chapman and Hall/CRC, 2014. URL: http://www.crcpress.com/product/isbn/9781466561595.

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