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cureit

website·Indexed 32 days ago

https://www.karissawhiting.com/cbioportalR//index.html

An R package for fitting and reporting results from cure mixture models using a tidy framework. It provides tools for cure model regression, predicted survival probability, Brier score inference, and integration with gtsummary and broom.

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What's inside cureit

  1. Overview of cureit package capabilities

    The cureit package provides a tidy framework for working with mixture cure models. Its primary capabilities include:

    • Fitting mixture cure models.
    • Summarizing coefficients using tidiers and gtsummary.
    • Visualizing data using nomograms.
    • Assessing model results using Brier scores and K-indices.

  2. Overview of cureit package capabilities

    The cureit package provides a tidy framework for working with cure mixture models. Its primary capabilities include:

    • Fitting mixture cure models.
    • Summarizing coefficients using tidiers and gtsummary.
    • Visualizing data using nomograms.
    • Assessing model results using Brier scores and K-indices.

  3. Introduction to the Tidyverse

    The tidyverse is a collection of R packages designed for data analysis that share a common design philosophy emphasizing code readability and pipe-based workflows. Key packages include:

    • readr: Data import/export
    • tibble: Enhanced data frames
    • dplyr: Data manipulation
    • tidyr: Data tidying
    • ggplot2: Data visualization
    • purrr: Functional programming (replaces many loops)
    • stringr: String manipulation
    • forcats: Factor handling
    library(tidyverse)

  4. Process targeted genomic panels with gnomeR

    gnomeR is an R toolkit used for processing, cleaning, and visualizing data derived from targeted genomic panels.
    # Visit GitHub for installation instructions
# https://github.com/MSKCC-Epi-Bio/gnomeR

  5. Retrieve cBioPortal data using cbioportalR

    cbioportalR is an R package used for programmatically retrieving and formatting data from the cBioPortal cancer genomics database.
    # Visit GitHub for installation instructions
# https://github.com/karissawhiting/cbioportalR

  6. Build mixture cure models with cureit

    cureit is an R package designed for building mixture cure models.
    # Visit GitHub for installation instructions
# https://github.com/karissawhiting/cureit

  7. Implement clinical trial design methods with clinfun

    clinfun is an R package that implements various methods for clinical trial design.
    # Visit GitHub for installation instructions
# https://github.com/veseshan/clinfun

  8. Analyze AACR Project GENIE data with genieBPC

    genieBPC is an R data-processing pipeline that ingests, standardizes, and streamlines the analysis of multi-institutional clinico-genomic data from the AACR Project GENIE Biopharma Collaborative.
    # Visit GitHub for installation instructions
# https://github.com/GENIE-BPC/genieBPC

  9. Create publication-ready tables with gtsummary

    gtsummary is an R package used to produce publication-ready analytical and summary tables.
    # Visit GitHub for installation instructions
# https://github.com/ddsjoberg/gtsummary

  10. Use bstfun for miscellaneous R functions

    bstfun is a collection of miscellaneous R functions.
    # Visit GitHub for installation instructions
# https://github.com/MSKCC-Epi-Bio/bstfun

  11. Pull genomic data by Study ID

    You can retrieve genomic data for an entire study using either specific functions for each data type or a general function to pull all available genomic data at once. This method is efficient for ensuring all available information for a single study is captured.
    # Option 1: Pull individual data types
mut_blca <- get_mutations_by_study(study_id = "blca_nmibc_2017")
cna_blca <- get_cna_by_study(study_id = "blca_nmibc_2017")
fus_blca <- get_fusions_by_study(study_id = "blca_nmibc_2017")

# Option 2: Pull all genomic data at once
all_genomic_blca <- get_genetics_by_study("blca_nmibc_2017")
# This returns a list containing $mutation, $cna, and $structural_variant

  12. Pull genomic data by Study ID

    You can retrieve genomic data for an entire study using either specific data-type functions or a general genetics function. Pulling by study ID returns all genomic data for all genes/panels included in that study.

    • get_mutations_by_study(): Pulls mutation data.
    • get_cna_by_study(): Pulls copy number alteration data.
    • get_fusions_by_study(): Pulls structural variant/fusion data.
    • get_genetics_by_study(): Pulls all available genomic data types at once and returns them as a list.
    # Pull individual data types
mut_blca <- get_mutations_by_study(study_id = "blca_nmibc_2017")
cna_blca <- get_cna_by_study(study_id = "blca_nmibc_2017")
fus_blca <- get_fusions_by_study(study_id = "blca_nmibc_2017")

# Pull all genomic data at once
all_genomic_blca <- get_genetics_by_study("blca_nmibc_2017")
# Access specific types from the result
# all_genomic_blca$mutation
# all_genomic_blca$cna
# all_genomic_blca$structural_variant