In olapuentesantana/easierData: easier internal data and exemplary dataset from IMvigor210CoreBiologies package

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library("easierData")

Intro to easierData

The easierData package includes an exemplary cancer dataset from @Mariathasan2018 to showcase the easier package:

• Mariathasan2018_PDL1_treatment: exemplary bladder cancer dataset with samples from 192 patients. This is provided as a SummarizedExperiment object containing:

• Two assays: counts and tpm expression values.

• Additional sample metadata in the colData slot, 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.

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 @LAPUENTESANTANA2021100293. 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 @LAPUENTESANTANA2021100293 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 [@LAPUENTESANTANA2021100293].

• 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/ [@Tweedie2021].

• scores_signature_genes: a list with the gene signatures for each score of immune response: CYT [@ROONEY201548], TLS [@Cabrita2020], IFNy [@Ayers2017], Ayers_expIS [@Ayers2017], Tcell_inflamed [@Ayers2017], Roh_IS [@Roheaah3560], Davoli_IS [@Davolieaaf8399], chemokines [@Messina2012], IMPRES [@Auslander2018], MSI [@Fu2019] and RIR [@JERBYARNON2018984].

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")

An overview is provided also in tabular form:

list_easierData()

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

mariathasan_dataset <- get_Mariathasan2018_PDL1_treatment()
mariathasan_dataset

Session info {-}

sessionInfo()

References {-}

olapuentesantana/easierData documentation built on Dec. 22, 2021, 4:19 a.m.