Open-source R/Shiny software

From experimental data to reproducible analysis and editable scientific figures

Name: BIOSZEN
Author: Benjamín Szenfeld

Benjamín Szenfeld · BIOSZEN 2.1.1 · August 2026

BIOSZEN helps researchers turn Excel tables, CSV files, and optical-density time series into statistical results, growth parameters, customizable plots, and reproducible exports—without requiring plotting code.

Install Try online Manual GitHub

R 4.1+ GPL-3.0 Local-first workflow

BIOSZEN application home screen showing its biological data analysis workspace — BIOSZEN home workspace. Select the image to view it at full resolution.

R/ShinyGuided analysis

R-universePackage distribution

ZenodoArchived and citable

bio.toolsLife-science registry

Why BIOSZEN

One workflow, from input to scientific communication

BIOSZEN brings data preparation, exploratory visualization, statistical analysis, growth-curve analysis, figure assembly, and reproducibility records into one guided local workflow.

Accessible analysis

Work through a graphical interface while retaining scriptable R functions for repeatable pipelines.

Biological statistics

Use normalization, replicate-aware quality control, normality tests, significance tests, post hoc comparisons, and multiple-testing correction.

Scientific figures

Create boxplots, bar plots, violin plots, curves, stacked plots, heatmaps, correlation plots, and correlation matrices.

Reproducible outputs

Export processed data, statistics, metadata, plots, composition panels, and reusable ZIP bundles.

Audience

Who is BIOSZEN for?

Experimental biologists, microbiologists, students, and research teams that want guided biological data analysis with reproducible R workflows and publication-ready outputs.

Install and launch

Start locally with R 4.1 or later

BIOSZEN is distributed through R-universe. After installation, launch it from R or from the BIOSZEN RStudio Addin.

install.packages(
  "BIOSZEN",
  repos = c(
    "https://bioszen.r-universe.dev",
    "https://cloud.r-project.org"
  )
)

BIOSZEN::BIOSZEN()

Local installation

The complete workflow runs on the user's computer. Files loaded into the local session are processed locally.

RStudio Addin

After installation and an RStudio restart, select Addins > Launch BIOSZEN in Browser.

Limited online demo

Use the hosted demo to explore the interface. Prefer the local installation for the complete workflow and for sensitive experimental data.

Complete workflow

Move from raw observations to reusable results

The recommended Platemap + Curves workflow combines a structured experimental workbook with optional well-based time-series measurements.

Step 1

Prepare

Use Platemap + Curves, grouped parameters, summary tables, or CSV input.

Step 2

Load

Import the primary experimental file and, when needed, the well-based time series.

Step 3

Inspect

Validate groups, parameters, replicates, and well mappings before analysis.

Step 4

Visualize

Configure plot families, labels, axes, fonts, colors, legends, and annotations.

Step 5

Analyze

Normalize against controls, run statistical tests, and inspect significance results.

Step 6

Export

Save plots, tables, metadata, PowerPoint figures, and reproducibility bundles.

Flexible experimental inputs

The recommended primary workbook contains a Datos sheet with observations and a PlotSettings sheet with plotting metadata. A separate curves file can provide a Time column and well measurements such as A1, A2, and A3.

Grouped-parameter workbooks
Summary inputs with mean, standard deviation, and sample size
CSV input for larger datasets
Biological and technical replicate information

BIOSZEN filters for experimental groups and media

Boxplot generated by BIOSZEN — Customizable group comparison figures.

Stacked plot generated by BIOSZEN — Stacked compositions for multi-component results.

Statistics

Normalize, test, compare, and annotate

BIOSZEN supports control-based normalization with replicate-aware behavior and provides a guided route from assumption checks to significance results.

Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling normality tests
ANOVA, Kruskal-Wallis, Student's t-test, and Wilcoxon tests
Post hoc comparisons and multiple-testing correction: Holm, FDR (Benjamini-Hochberg), Bonferroni, or none
Significance tables and annotations for supported plot types

Statistical test configuration in BIOSZEN

Growth curves

Extract biologically useful parameters from time series

The growth module plots trajectories and calculates parameters independently for each well or curve. It uses a robust exponential-phase detector first and a permissive detector only to complete estimates that remain missing.

BIOSZEN growth parameter extraction workflow

Scriptable growth analysis

growth_results <- BIOSZEN::growth_parameters(
  "Curvas.xlsx",
  max_time = 48,
  time_interval = 0.5,
  output_dir = "growth_results",
  overwrite = FALSE
)

Parameter	What it represents	How to interpret it
`µMax`	Maximum specific growth rate estimated during the detected exponential-growth interval.	Higher values indicate faster exponential growth; units are inverse time.
`max_percap_time`	Mean time of the interval used to estimate `µMax`.	Locates the strongest exponential-growth interval on the time axis.
`doub_time`	Estimated doubling time, calculated as `log(2) / µMax`.	Lower values indicate faster growth.
`lag_time`	Estimated start of the detected exponential phase.	Larger values indicate a longer delay before rapid growth.
`ODmax`	Highest observed optical-density value.	Maximum recorded density during the experiment.
`max_time`	First time at which `ODmax` is observed.	Indicates when the maximum recorded density is reached.
`AUC`	Area under the complete OD-versus-time curve.	Summarizes accumulated growth across the experiment.
`OD0`	First valid measurement after observations are ordered by time.	Describes starting density or baseline OD.

Units: time-related parameters inherit the unit used in the input Time column.

Export and reproduce

Prepare figures while preserving the analysis behind them

Export individual plots, multi-plot composition panels, processed workbooks, statistical tables, metadata, and reusable analysis bundles.

PNG and PDF figures
Processed Excel workbooks and statistical tables
PowerPoint figures and composition panels
Metadata import/export and ZIP bundles

BIOSZEN from R scripts

# Launch the app
BIOSZEN::BIOSZEN()

# Extract growth parameters
results <- BIOSZEN::growth_parameters("Curvas.xlsx")

# Check or install an approved update
BIOSZEN::bioszen_update_available()
BIOSZEN::bioszen_update()

# Display the official citation
BIOSZEN::bioszen_citation()

Availability