easier Data (original) (raw)

Intro to easierData

The easierData package includes an exemplary cancer dataset from Mariathasan et al. (2018) to showcase the easier package:

The easierData data package also includes multiple data objects so-called internal data of easier package since they are indispensable for the functional performance of the package. This includes:

Load easier Data

Starting R, this package can be installed as follows:

BiocManager::install("easierData")

The contents of the package can be seen by querying ExperimentHub for the package name:

suppressPackageStartupMessages({
    library("ExperimentHub")
    library("easierData")
})

eh <- ExperimentHub()
query(eh, "easierData")
#> ExperimentHub with 11 records
#> # snapshotDate(): 2025-04-11
#> # $dataprovider: NA, IMvigor210CoreBiologies package;  Mariathasan S, Turley...
#> # $species: Homo sapiens
#> # $rdataclass: list, numeric, data.frame, character, SummarizedExperiment
#> # additional mcols(): taxonomyid, genome, description,
#> #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> #   rdatapath, sourceurl, sourcetype 
#> # retrieve records with, e.g., 'object[["EH6677"]]' 
#> 
#>            title                         
#>   EH6677 | Mariathasan2018_PDL1_treatment
#>   EH6678 | opt_models                    
#>   EH6679 | opt_xtrain_stats              
#>   EH6680 | TCGA_mean_pancancer           
#>   EH6681 | TCGA_sd_pancancer             
#>   ...      ...                           
#>   EH6683 | intercell_networks            
#>   EH6684 | lr_frequency_TCGA             
#>   EH6685 | group_lrpairs                 
#>   EH6686 | HGNC_annotation               
#>   EH6687 | scores_signature_genes

An overview is provided also in tabular form:

list_easierData()
#>     eh_id                          title
#> 1  EH6677 Mariathasan2018_PDL1_treatment
#> 2  EH6678                     opt_models
#> 3  EH6679               opt_xtrain_stats
#> 4  EH6680            TCGA_mean_pancancer
#> 5  EH6681              TCGA_sd_pancancer
#> 6  EH6682               cor_scores_genes
#> 7  EH6683             intercell_networks
#> 8  EH6684              lr_frequency_TCGA
#> 9  EH6685                  group_lrpairs
#> 10 EH6686                HGNC_annotation
#> 11 EH6687         scores_signature_genes

The individual data objects can be accessed using either their ExperimentHub accession number, or the convenience functions provided in this package - both calls are equivalent. For instance to access the Mariathasan2018_PDL1_treatment example dataset:

mariathasan_dataset <- eh[["EH6677"]]
mariathasan_dataset
#> class: SummarizedExperiment 
#> dim: 31087 192 
#> metadata(1): cancertype
#> assays(2): counts tpm
#> rownames(31087): A1BG NAT2 ... CASP8AP2 SCO2
#> rowData names(0):
#> colnames(192): SAM7f0d9cc7f001 SAM4305ab968b90 ... SAMda4d892fddc8
#>   SAMe3d4266775a9
#> colData names(3): pat_id BOR TMB

mariathasan_dataset <- get_Mariathasan2018_PDL1_treatment()
mariathasan_dataset
#> class: SummarizedExperiment 
#> dim: 31087 192 
#> metadata(1): cancertype
#> assays(2): counts tpm
#> rownames(31087): A1BG NAT2 ... CASP8AP2 SCO2
#> rowData names(0):
#> colnames(192): SAM7f0d9cc7f001 SAM4305ab968b90 ... SAMda4d892fddc8
#>   SAMe3d4266775a9
#> colData names(3): pat_id BOR TMB

Session info

sessionInfo()
#> R version 4.5.0 RC (2025-04-04 r88126)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [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: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] SummarizedExperiment_1.38.0 Biobase_2.68.0             
#>  [3] GenomicRanges_1.60.0        GenomeInfoDb_1.44.0        
#>  [5] IRanges_2.42.0              S4Vectors_0.46.0           
#>  [7] MatrixGenerics_1.20.0       matrixStats_1.5.0          
#>  [9] ExperimentHub_2.16.0        AnnotationHub_3.16.0       
#> [11] BiocFileCache_2.16.0        dbplyr_2.5.0               
#> [13] BiocGenerics_0.54.0         generics_0.1.3             
#> [15] easierData_1.14.0          
#> 
#> loaded via a namespace (and not attached):
#>  [1] KEGGREST_1.48.0         xfun_0.52               bslib_0.9.0            
#>  [4] lattice_0.22-7          vctrs_0.6.5             tools_4.5.0            
#>  [7] curl_6.2.2              tibble_3.2.1            AnnotationDbi_1.70.0   
#> [10] RSQLite_2.3.9           blob_1.2.4              pkgconfig_2.0.3        
#> [13] Matrix_1.7-3            lifecycle_1.0.4         GenomeInfoDbData_1.2.14
#> [16] compiler_4.5.0          Biostrings_2.76.0       htmltools_0.5.8.1      
#> [19] sass_0.4.10             yaml_2.3.10             pillar_1.10.2          
#> [22] crayon_1.5.3            jquerylib_0.1.4         DelayedArray_0.34.0    
#> [25] cachem_1.1.0            abind_1.4-8             mime_0.13              
#> [28] tidyselect_1.2.1        digest_0.6.37           purrr_1.0.4            
#> [31] dplyr_1.1.4             BiocVersion_3.21.1      fastmap_1.2.0          
#> [34] grid_4.5.0              SparseArray_1.8.0       cli_3.6.4              
#> [37] magrittr_2.0.3          S4Arrays_1.8.0          withr_3.0.2            
#> [40] filelock_1.0.3          UCSC.utils_1.4.0        rappdirs_0.3.3         
#> [43] bit64_4.6.0-1           rmarkdown_2.29          XVector_0.48.0         
#> [46] httr_1.4.7              bit_4.6.0               png_0.1-8              
#> [49] memoise_2.0.1           evaluate_1.0.3          knitr_1.50             
#> [52] rlang_1.1.6             glue_1.8.0              DBI_1.2.3              
#> [55] BiocManager_1.30.25     jsonlite_2.0.0          R6_2.6.1

References

Auslander, Noam, Gao Zhang, Joo Sang Lee, Dennie T. Frederick, Benchun Miao, Tabea Moll, Tian Tian, et al. 2018. “Robust Prediction of Response to Immune Checkpoint Blockade Therapy in Metastatic Melanoma.” Nature Medicine 24 (10): 1545–9. https://doi.org/10.1038/s41591-018-0157-9.

Cabrita, Rita, Martin Lauss, Adriana Sanna, Marco Donia, Mathilde Skaarup Larsen, Shamik Mitra, Iva Johansson, et al. 2020. “Tertiary Lymphoid Structures Improve Immunotherapy and Survival in Melanoma.” Nature 577 (7791): 561–65. https://doi.org/10.1038/s41586-019-1914-8.

Davoli, Teresa, Hajime Uno, Eric C. Wooten, and Stephen J. Elledge. 2017. “Tumor Aneuploidy Correlates with Markers of Immune Evasion and with Reduced Response to Immunotherapy.” Science 355 (6322). https://doi.org/10.1126/science.aaf8399.

Fu, Yelin, Lishuang Qi, Wenbing Guo, Liangliang Jin, Kai Song, Tianyi You, Shuobo Zhang, Yunyan Gu, Wenyuan Zhao, and Zheng Guo. 2019. “A Qualitative Transcriptional Signature for Predicting Microsatellite Instability Status of Right-Sided Colon Cancer.” BMC Genomics 20 (1): 769. https://doi.org/10.1186/s12864-019-6129-8.

Jerby-Arnon, Livnat, Parin Shah, Michael S. Cuoco, Christopher Rodman, Mei-Ju Su, Johannes C. Melms, Rachel Leeson, et al. 2018. “A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade.” Cell 175 (4): 984–997.e24. https://doi.org/10.1016/j.cell.2018.09.006.

Lapuente-Santana, Oscar, Maisa van Genderen, Peter A. J. Hilbers, Francesca Finotello, and Federica Eduati. 2021. “Interpretable Systems Biomarkers Predict Response to Immune-Checkpoint Inhibitors.” Patterns, 100293. https://doi.org/10.1016/j.patter.2021.100293.

Mariathasan, Sanjeev, Shannon J. Turley, Dorothee Nickles, Alessandra Castiglioni, Kobe Yuen, Yulei Wang, Edward E. Kadel III, et al. 2018. “TGFB Attenuates Tumour Response to Pd-L1 Blockade by Contributing to Exclusion of T Cells.” Nature 554 (7693): 544–48. https://doi.org/10.1038/nature25501.

McClanahan, Mark Ayers AND Jared Lunceford AND Michael Nebozhyn AND Erin Murphy AND Andrey Loboda AND David R. Kaufman AND Andrew Albright AND Jonathan D. Cheng AND S. Peter Kang AND Veena Shankaran AND Sarina A. Piha-Paul AND Jennifer Yearley AND Tanguy Y. Seiwert AND Antoni Ribas AND Terrill K. 2017. “IFN-Y–Related mRNA Profile Predicts Clinical Response to Pd-1 Blockade.” The Journal of Clinical Investigation 127 (8): 2930–40. https://doi.org/10.1172/JCI91190.

Messina, Jane L., David A. Fenstermacher, Steven Eschrich, Xiaotao Qu, Anders E. Berglund, Mark C. Lloyd, Michael J. Schell, Vernon K. Sondak, Jeffrey S. Weber, and James J. Mulé. 2012. “12-Chemokine Gene Signature Identifies Lymph Node-Like Structures in Melanoma: Potential for Patient Selection for Immunotherapy?” Scientific Reports 2 (1): 765. https://doi.org/10.1038/srep00765.

Roh, Whijae, Pei-Ling Chen, Alexandre Reuben, Christine N. Spencer, Peter A. Prieto, John P. Miller, Vancheswaran Gopalakrishnan, et al. 2017. “Integrated Molecular Analysis of Tumor Biopsies on Sequential Ctla-4 and Pd-1 Blockade Reveals Markers of Response and Resistance.” Science Translational Medicine 9 (379). https://doi.org/10.1126/scitranslmed.aah3560.

Rooney, Michael S., Sachet A. Shukla, Catherine J. Wu, Gad Getz, and Nir Hacohen. 2015. “Molecular and Genetic Properties of Tumors Associated with Local Immune Cytolytic Activity.” Cell 160 (1): 48–61. https://doi.org/10.1016/j.cell.2014.12.033.

Tweedie, Susan, Bryony Braschi, Kristian Gray, Tamsin E M Jones, Ruth L Seal, Bethan Yates, and Elspeth A Bruford. 2020. “Genenames.org: the HGNC and VGNC resources in 2021.” Nucleic Acids Research 49 (D1): D939–D946. https://doi.org/10.1093/nar/gkaa980.