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Getting Started with molpathR

Source: vignettes/getting-started.Rmd
getting-started.Rmd

R-CMD-check pkgdown CRAN status Codecov test coverage CRAN downloads CRAN downloads total License: MIT Lifecycle: experimental

Installation

Install molpathR from GitHub:

# install.packages("remotes")
remotes::install_github("cttir/molpathR")

Quick start

Load the package and create an example database:

library(molpathR)
db <- mp_example_db(n_patients = 50, seed = 42)
db
#> 
#> ── molpath_db ──────────────────────────────────────────────────────────────────
#> ℹ patients: 50 records x 5 columns
#> ℹ samples: 116 records x 5 columns
#> ℹ variants: 2151 records x 10 columns
#> ℹ reports: 116 records x 5 columns
#> ℹ clinical: 195 records x 5 columns
#> ℹ survival: 50 records x 5 columns
#> ℹ Sample date range: 2021-04-01 to 2025-06-26
#> ℹ Overall completeness: "93.7%"
#> ℹ Created: "2026-06-24 09:25:33"
#> ℹ Source files: 0

Explore the data

Get a cohort summary:

mp_summary(db)
#> 
#> ── molpathR Database Summary ───────────────────────────────────────────────────
#> 
#> ── Record counts ──
#> 
#> • Patients: 50
#> • Samples: 116
#> • Variants: 2151
#> • Reports: 116
#> • Clinical: 195
#> 
#> ── Diagnoses ──
#> 
#> • Breast carcinoma: 15
#> • Colorectal carcinoma: 10
#> • Lung adenocarcinoma: 21
#> • Melanoma: 4
#> 
#> ── Top mutated genes ──
#> 
#> • TP53: 272
#> • KRAS: 241
#> • PIK3CA: 173
#> • APC: 138
#> • EGFR: 138
#> • PTEN: 122
#> • BRAF: 119
#> • ERBB2: 97
#> • BRCA1: 86
#> • STK11: 85
#> 
#> ── Completeness ──
#> 
#> • patients_with_samples: 100%
#> • patients_with_variants: 100%
#> • patients_with_survival: 100%
#> • patients_with_clinical: 100%

Query patients 

# Find melanoma patients over 60
melanoma <- mp_query_patients(db, diagnosis == "Melanoma", age > 60)
head(melanoma)
#> # A tibble: 3 × 5
#>   patient_id      age sex   diagnosis diagnosis_date
#>   <chr>         <int> <chr> <chr>     <date>        
#> 1 PAT-2024-0011    77 F     Melanoma  2023-06-25    
#> 2 PAT-2024-0031    67 M     Melanoma  2021-07-20    
#> 3 PAT-2024-0032    70 F     Melanoma  2024-11-25

Query variants 

# Pathogenic TP53 variants with VAF > 10%
tp53 <- mp_query_variants(db, genes = "TP53", classification = "Pathogenic", min_vaf = 0.1)
head(tp53)
#> # A tibble: 6 × 10
#>   sample_id  gene  variant variant_type classification   vaf chromosome position
#>   <chr>      <chr> <chr>   <chr>        <chr>          <dbl> <chr>         <int>
#> 1 SAM-2021-… TP53  TP53 d… CNV          Pathogenic     0.420 17           1.91e7
#> 2 SAM-2021-… TP53  TP53-U… Fusion       Pathogenic     0.464 17           7.83e7
#> 3 SAM-2022-… TP53  TP53-U… Fusion       Pathogenic     0.322 17           1.89e8
#> 4 SAM-2021-… TP53  TP53 l… CNV          Pathogenic     0.214 17           5.81e7
#> 5 SAM-2022-… TP53  p.R282W SNV          Pathogenic     0.250 17           5.91e7
#> 6 SAM-2023-… TP53  p.R248W SNV          Pathogenic     0.213 17           1.14e8
#> # ℹ 2 more variables: ref_allele <chr>, alt_allele <chr>

Visualize 

mp_plot_survival(db, group_by = "diagnosis", type = "os")

Launch the Shiny app

Use of LLM tools

Portions of this package were prepared with assistance from large language model tooling for narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting, scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI, the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via Ollama and the ollamar R package — local inference only, with no data sent to third parties for the self-hosted model.

All scientific claims, methodological choices, analyses, interpretations, and conclusions are the author’s own. No LLM-generated text was incorporated without review and revision, and every reference was verified against its DOI, arXiv ID, or ISBN.