Package: ICBioMark 0.1.5

ICBioMark: Data-Driven Design of Targeted Gene Panels for Estimating Immunotherapy Biomarkers

Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arxiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.

Authors:Jacob R. Bradley [aut, cre], Timothy I. Cannings [aut]

ICBioMark_0.1.5.tar.gz
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ICBioMark.pdf |ICBioMark.html✨
ICBioMark/json (API)
NEWS

# Install 'ICBioMark' in R:

install.packages('ICBioMark', repos = c('https://cobrbra.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cobrbra/icbiomark/issues

Datasets:

ensembl_gene_lengths - Gene Lengths from the Ensembl Database
example_first_pred_tmb - First-Fit Predictive Model Fitting on Example Data
example_gen_model - Generative Model from Simulated Data
example_maf_data - Simulated MAF Data
example_predictions - Example Predictions
example_refit_panel - Refitted Predictive Model Fitted on Example Data
example_refit_range - Refitted Predictive Models Fitted on Example Data
example_tables - Mutation Matrices from Simulated Data
example_tib_tables - Tumour Indel Burden of Example Train, Validation and Test Data.
example_tmb_tables - Tumour Mutation Burden of Example Train, Validation and Test Data.
nsclc_maf - Non-Small Cell Lung Cancer MAF Data
nsclc_survival - Non-Small Cell Lung Cancer Survival and Clinical Data

On CRAN:

22 exports 0.63 score 46 dependencies 2 scripts 253 downloads

Last updated 1 years agofrom:e5040a9892. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 17 2024
R-4.5-win	NOTE	Sep 17 2024
R-4.5-linux	NOTE	Sep 17 2024
R-4.4-win	NOTE	Sep 17 2024
R-4.4-mac	NOTE	Sep 17 2024
R-4.3-win	NOTE	Sep 17 2024
R-4.3-mac	NOTE	Sep 17 2024

Exports:fit_gen_model fit_gen_model_uninteract fit_gen_model_unisamp generate_maf_data get_auprc get_biomarker_from_maf get_biomarker_tables get_gen_estimates get_K get_mutation_dictionary get_mutation_tables get_p get_panels_from_fit get_predictions get_r_squared get_stats get_table_from_maf pred_first_fit pred_intervals pred_refit_panel pred_refit_range vis_model_fit

Dependencies:cli codetools colorspace dplyr fansi farver foreach generics gglasso ggplot2 glmnet glue gtable isoband iterators labeling latex2exp lattice lifecycle magrittr MASS Matrix matrixStats mgcv munsell nlme pillar pkgconfig PRROC purrr R6 RColorBrewer Rcpp RcppEigen rlang scales shape stringi stringr survival tibble tidyselect utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Gene Lengths from the Ensembl Database	ensembl_gene_lengths
First-Fit Predictive Model Fitting on Example Data	example_first_pred_tmb
Generative Model from Simulated Data	example_gen_model
Simulated MAF Data	example_maf_data
Example Predictions	example_predictions
Refitted Predictive Model Fitted on Example Data	example_refit_panel
Refitted Predictive Models Fitted on Example Data	example_refit_range
Mutation Matrices from Simulated Data	example_tables
Tumour Indel Burden of Example Train, Validation and Test Data.	example_tib_tables
Tumour Mutation Burden of Example Train, Validation and Test Data.	example_tmb_tables
Fit Generative Model	fit_gen_model
Fit Generative Model Without Gene/Variant Type-Specific Interactions	fit_gen_model_uninteract
Fit Generative Model Without Sample-Specific Effects	fit_gen_model_unisamp
Generate mutation data.	generate_maf_data
AUPRC Metrics for Predictions	get_auprc
Produce a Table of Biomarker Values from a MAF	get_biomarker_from_maf
Get True Biomarker Values on Training, Validation and Test Sets	get_biomarker_tables
Investigate Generative Model Comparisons	get_gen_estimates
Construct Bias Penalisation	get_K
Group and Filter Mutation Types	get_mutation_dictionary
Produce Training, Validation and Test Matrices	get_mutation_tables
Construct Optimisation Parameters.	get_p
Extract Panel Details from Group Lasso Fit	get_panels_from_fit
Produce Predictions on an Unseen Dataset	get_predictions
R Squared Metrics for Predictions	get_r_squared
Metrics for Predictive Performance	get_stats
Produce a Mutation Matrix from a MAF	get_table_from_maf
ICBioMark: A package for cost-effective design of gene panels to predict exome-wide biomarkers.	ICBioMark
Non-Small Cell Lung Cancer MAF Data	nsclc_maf
Non-Small Cell Lung Cancer Survival and Clinical Data	nsclc_survival
First-Fit Predicitve Model with Group Lasso	pred_first_fit
Produce Error Bounds for Predictions	pred_intervals
Refitted Predictive Model for a Given Panel	pred_refit_panel
Get Refitted Predictive Models for a First-Fit Range of Panels	pred_refit_range
Visualise Generative Model Fit	vis_model_fit