Live App

thegrowthanalysisapp.streamlit.app

Abstract

TheGrowthAnalysisApp is a Streamlit-based application for analyzing microbial growth curves from plate reader time-series data. Users upload raw optical density measurements and a plate map, configure analysis parameters, and the app computes baseline-corrected growth statistics including maximum OD, maximum specific growth rate (μ_max), lag time, and exponential phase boundaries across all wells.

The app supports multiple analysis methods:

• Non-parametric methods: Sliding Window and Spline fitting for data-driven analysis
• Parametric models: Logistic, Gompertz, Richards, and Baranyi-Roberts models for theory-driven fitting

Key features include:

• Configurable phase boundary detection with adjustable μ_max thresholds (default 50%)
• Interactive quality control with lasso selection for manual data point selection
• Automatic replicate detection and averaging
• Comprehensive visualization options (OD curves, 1st derivative, specific growth rate μ)
• Bulk export of curated results and publication-ready plots

Installation

Prerequisites

• Anaconda or Miniconda installed on your system (recommended)
• OR Python 3.9+ with pip
• Git (optional, for cloning the repository)

Setup Instructions

Option 1: Using Conda (Recommended)

1. Clone or download the repository

   git clone https://github.com/yourusername/TheGrowthAnalysisApp.git
   cd TheGrowthAnalysisApp

2. Create the conda environment

   Use the provided environment.yaml file to create a conda environment with all required dependencies:

   conda env create -f environment.yaml

3. Activate the environment

   conda activate growth_curves_app_env

Option 2: Using pip

1. Clone or download the repository

   git clone https://github.com/yourusername/TheGrowthAnalysisApp.git
   cd TheGrowthAnalysisApp

2. Create a virtual environment (optional but recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

Running the Application

Launch the Streamlit app:

streamlit run app.py

The app will automatically open in your default web browser at http://localhost:8501.

• Ensure you're in the project directory when running streamlit run app.py

How to Use the App

1. Prepare Your Input Files

• Plate reader data: A time-series table with time in the first column and OD measurements for each well in subsequent columns
• Plate map: A table that maps wells to sample metadata (strain, condition, replicate, etc.)

Sample files are included in the repo:

• example_data.xlsx
• example_plate_map.xls

Required Header Formats

Plate reader data (first column must be Time, remaining columns must be wells like A1...H12):

Time | A1   | A2   | A3   | ... | H12
0    | 0.05 | 0.06 | 0.05 | ... | 0.04
12   | 0.08 | 0.09 | 0.07 | ... | 0.06

Plate map (first column must be rows, remaining columns must be numeric 1...12):

rows | 1              | 2              | 3     | ... | 12
A    | Sample1_Cond1  | Sample1_Cond2  | BLANK | ... |
B    | Sample2_Cond1  | Sample2_Cond2  |       | ... |

• Samples with the same name will be treated as replicates
• Use 'BLANK' for blank wells (used for baseline correction)
• Leave cells empty for wells to ignore
• The first '_' is used to split strain and condition labels for visualization

2. Upload and Configure (Upload & Analyse Page)

1. Upload files

   • Upload your plate reader data file (Excel format)
   • Upload your plate map (Excel format)
   • Click "Load plate" to validate and store the data

2. Configure preprocessing parameters

   • Time unit: Select the unit used in your data file (seconds, minutes, or hours)
   • Pathlength: Set the optical pathlength of your plate reader (default: 0.42 cm)
   • Time range: Clip the time series to focus on relevant growth phases
   • Blank subtraction: Enable to subtract the mean of BLANK wells
   • Exclude wells: Manually exclude contaminated or problematic wells

3. Select analysis method

   • Sliding Window (non-parametric): Uses a sliding window of data points to calculate growth rate
   • Spline (non-parametric): Fits a smoothing spline to the data for flexible curve fitting
   • Parametric models: Logistic, Gompertz, Richards, or Baranyi-Roberts models

4. Configure phase boundary detection

   • Lag phase cutoff: Fraction of μ_max used to define lag phase end (default: 0.5)
   • Exponential phase cutoff: Fraction of μ_max used to define exponential phase end (default: 0.5)
   • Higher values = more conservative phase detection

5. Set 'No Growth' thresholds

   • Minimum data points required
   • Minimum signal-to-noise ratio
   • Minimum growth rate threshold

6. Click "Update parameters and analyse selected plate"

3. Review and Curate Fits (Check Growth Fits Page)

• View individual well growth curves with fitted parameters
• Toggle log scale to view exponential growth more clearly
• Toggle annotations to show/hide phase boundaries and growth metrics
• Interactive lasso selection: Click and drag on the plot to select specific data points for re-analysis
• Adjust phase boundaries using sliders
• Set maximum OD manually if needed
• Mark wells as "No Growth" to exclude from analysis
• Re-analyse individual wells with current settings
• Exclude wells from the analysis entirely
• Navigate between wells using arrow buttons or keyboard shortcuts (Left/Right arrows)

The page displays three plots:

• Main growth curve with annotations (OD vs Time)
• 1st derivative (dOD/dt vs Time)
• Specific growth rate (μ vs Time)

4. Visualize Results (Plate Overviews & Create Visualizations Pages)

Plate Overviews

• View all wells in a 96-well plate layout
• Color-coded by growth metrics or sample groups

Create Visualizations

• Generate publication-ready plots
• Compare replicates and conditions
• Customize plot appearance

5. Export Results (Download Analyzed Data Page)

Export options include:

Tabulated Data

• Baseline-corrected time series (CSV)
• Growth statistics per well (CSV)
• Growth statistics averaged per sample (CSV)
• Analysis parameters used (CSV)

Global Plots

• Baseline plot (blank well measurements)
• Plate view (96-well overview)
• Replicates plot (grouped by sample name)

Well Level Plots

• OD growth curves with annotations
• 1st Derivative (dOD/dt)
• Specific growth rate (μ)

Annotation Options

• Phase boundaries
• Time at μ_max
• OD at μ_max
• Maximum OD
• μ_max point
• Fitted model curve

All exports are packaged in a single ZIP file for easy download.

Analysis Methods

Non-Parametric Methods

Sliding Window

• Slides a fixed-size window across the growth curve
• Fits a linear regression to log-transformed OD at each position
• Maximum slope = μ_max

Spline

• Fits a smoothing spline to log-transformed OD
• Derivative of spline gives growth rate at each time point
• More flexible and smoother than sliding window

Parametric Models

Logistic

• Classic S-shaped curve with symmetric inflection point
• Most commonly used for microbial growth

Gompertz

• Asymmetric S-curve
• Often fits bacterial growth better than logistic

Richards

• Generalized logistic with shape parameter
• Most flexible, use when other models don't fit well

Baranyi-Roberts

• Mechanistic model with physiological lag parameter
• Accounts for cell adaptation during lag phase

Phase Boundary Detection

All methods use the same approach:

1. Lag phase end: First time point where μ exceeds the threshold (default: 50% of μ_max)
2. Exponential phase end: First time point after μ_max where μ drops below threshold (default: 50% of μ_max)

The threshold is configurable and affects how conservatively phase transitions are detected.

Tips

• Start with the example files to validate the workflow before using your own data
• If uploads fail, confirm your files are Excel-compatible and have consistent well names
• Use the "Check Growth Fits" page to quality-control your results before export
• Higher phase boundary thresholds (closer to 0.5) give more conservative phase detection
• Lower phase boundary thresholds (closer to 0.1) detect transitions earlier
• The lasso selection tool is useful for excluding outliers or focusing on specific growth phases

Dependencies

• Python 3.9+
• streamlit
• pandas
• numpy
• scipy
• plotly
• openpyxl (for Excel file handling)
• xlrd (for .xls file support)
• growthcurves (core analysis package)
• streamlit_sortables
• streamlit-aggrid

See requirements.txt or environment.yaml for specific versions.

License

MIT

Citation

t.b.d

Support

For issues, questions, or feature requests, please open an issue on the GitHub repository.