---
title: "Interactive Shiny Application"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Interactive Shiny Application}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
resource_files:
- figures/logo.svg
- figures/logo-dark.svg
---
[](https://github.com/CTTIR/molpathR/actions/workflows/R-CMD-check.yaml)
[](https://cttir.github.io/molpathR/)
[](https://CRAN.R-project.org/package=molpathR)
[](https://app.codecov.io/gh/CTTIR/molpathR?branch=main)
[](https://cran.r-project.org/package=molpathR)
[](https://cran.r-project.org/package=molpathR)
[](https://opensource.org/licenses/MIT)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## Launching the app
```{r launch, eval = FALSE}
library(molpathR)
# Launch with pre-loaded example data
db <- mp_example_db()
mp_run_app(db)
# Or launch empty and upload data in the browser
mp_run_app()
```
## Application tabs
### Tab 1: Database
The home tab lets you upload data files or load the example database. It shows
summary cards (number of patients, samples, variants, reports) and a table of
loaded data layers. Non-RDS files are staged using their original filenames;
a batch replaces session state only after every file has parsed and validated.
A failed upload retains the previous database. Upload one database RDS by itself
to replace the current database. Incomplete cross-table links are reported
explicitly, without inventing missing patients or samples. Raw FASTQ/BAM files
are available through the R readers but require separately called and annotated
variants before they can be added to this database.
### Tab 2: Patients
A searchable, filterable master table of all patients. Click a row to expand
a detail panel showing that patient's samples, variants, clinical data, and
survival information. Filters include diagnosis, sex, and age range. Missing
filter values are included by default and can be excluded explicitly. Clear the
patient selection to return linked views to the whole cohort; replacing the
database or changing patient filters clears stale selections.
### Tab 3: Variants
Interactive variant table with gene, classification, and VAF filters. Includes
tabs for the variant landscape plot, mutation spectrum, and VAF distribution.
The figures and CSV download use the same current filtered variant rows.
Deselecting every available filter choice selects no known values; the separate
missing-value control governs unannotated values. Clicking a variant selects its
gene for survival stratification.
### Tab 4: Reports
Browse all parsed reports with filters for report type, date range, and patient.
Click a row to view the full report text in a detail panel.
### Tab 5: Survival
Interactive Kaplan-Meier curves with options for overall or progression-free
survival. Stratify by diagnosis, sex, or any gene mutation status. Displays
log-rank p-values and median survival statistics.
Gene selection from the Variants tab automatically propagates here. Gene groups
mean **recorded variant** and **no recorded variant**. The database does not
provide gene-level assay coverage: no recorded variant is not proof of a negative
assay or wild-type genotype. The curve, summary and CSV use the same outcome and
grouping records; missing outcome/group values cannot contribute to that analysis.
Survival always uses the full cohort, independent of the Patients-tab selection.
### Tab 6: Export
Select which data layers to export (patients, samples, variants, reports,
clinical, survival). Choose from CSV, TSV, Excel, or RDS formats. Preview
the data before downloading. These are full database-layer exports, independent
of the Patients/Variants table filters; their individual CSV buttons export the
filtered rows. CSV/TSV are ZIP archives and require a `zip` executable on `PATH`
or configured with `R_ZIPCMD`. Excel requires the optional `writexl` package;
if it is unavailable, the app reports this rather than disguising another format.
Structured cells (for example PDF report sections) are encoded as JSON strings
in CSV, TSV and Excel; decode an individual cell with `jsonlite::fromJSON()`.
RDS preserves the native R structures. Partial RDS exports preserve all six typed tables, with unselected layers empty,
so unresolved links may need attention after re-import.
## Cross-tab interactivity
- Selecting a patient in Tab 2 filters Variants and Reports to that patient.
Each view displays its cohort; Survival remains a cohort analysis.
- Selecting a gene/variant in Tab 3 pre-fills the survival stratification gene
## Deployment
```{r deploy, eval = FALSE}
# Deploy to shinyapps.io
rsconnect::deployApp(
appDir = system.file("shiny", "molpathR", package = "molpathR"),
appName = "molpathR"
)
```
