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:
- Mariathasan2018_PDL1_treatment: exemplary bladder cancer dataset with samples from 192 patients. This is provided as a
SummarizedExperimentobject containing:- Two assays:
countsandtpmexpression values. - Additional sample metadata in the
colDataslot, including pat_id (the id of the patient in the original study), BOR, and TMB (Tumor Mutational Burden).
The processed data is publicly available from Mariathasan et al. “TGF-B attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells”, published in Nature, 2018 doi:10.1038/nature25501 via IMvigor210CoreBiologies package under the CC-BY license.
- Two assays:
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:
- opt_models: the cancer-specific model feature parameters learned in Lapuente-Santana et al. (2021). For each quantitative descriptor (e.g. pathway activity), models were trained using multi-task learning with randomized cross-validation repeated 100 times. For each quantitative descriptor, 1000 models are available (100 per task). This is provided as a list containing, for each cancer type and quantitative descriptor, a matrix of feature coefficient values across different tasks.
- opt_xtrain_stats: the cancer-specific features mean and standard deviation of each quantitative descriptor (e.g. pathway activity) training set used in Lapuente-Santana et al. (2021) during randomized cross-validation repeated 100 times, required for normalization of the test set. This is provided as a list containing, for each cancer type and quantitative descriptor, a matrix with feature mean and sd values across the 100 cross-validation runs.
- TCGA_mean_pancancer: a numeric vector with the mean of the TPM expression of each gene across all TCGA cancer types, required for normalization of input TPM gene expression data.
- TCGA_sd_pancancer: a numeric vector with the standard deviation (sd) of the TPM expression of each gene across all TCGA cancer types, required for normalization of input TPM gene expression data.
- cor_scores_genes: a character vector with the list of genes used to define correlated scores of immune response. These scores were found to be highly correlated across all 18 cancer types (Lapuente-Santana et al. 2021).
- intercell_networks: a list with the cancer-specific intercellular networks, including a pan-cancer network.
- lr_frequency_TCGA: a numeric vector containing the frequency of each ligand-receptor pair feature across the whole TCGA database.
- group_lrpairs: a list with the information on how to group ligand-receptor pairs because of sharing the same gene, either as ligand or receptor.
- HGNC_annotation: a data.frame with the gene symbols approved annotations obtained from https://www.genenames.org/tools/multi-symbol-checker/ (Tweedie et al. 2020).
- scores_signature_genes: a list with the gene signatures for each score of immune response: CYT (Rooney et al. 2015), TLS (Cabrita et al. 2020), IFNy (McClanahan 2017), Ayers_expIS (McClanahan 2017), Tcell_inflamed (McClanahan 2017), Roh_IS (Roh et al. 2017), Davoli_IS (Davoli et al. 2017), chemokines (Messina et al. 2012), IMPRES (Auslander et al. 2018), MSI (Fu et al. 2019) and RIR (Jerby-Arnon et al. 2018).
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_genesAn 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_genesThe 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 TMBSession 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_2.6.1References
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.