OriginPro for Life Science
OriginPro provides an integrated environment for organizing, analyzing, and visualizing life science data. With specialized Structured Tables, context-sensitive analysis tools, and publication-quality graphing, researchers can streamline their workflows from raw data to final results.
OriginPro 2027 further enhances life science workflows with dedicated Western Blot and qPCR analysis tools, improved statistical comparisons and significance plots, and expanded dose-response fitting capabilities.
Key Benefits:
- Organize experimental data using structured tables.
- Import and analyze data from GraphPad Prism and Excel.
- Streamline Western Blot and qPCR analysis.
- Perform statistical tests and visualize significant differences.
- Analyze dose-response data with advanced fitting tools.
- Generate publication-quality figures with fewer manual steps
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Transition Easily from GraphPad Prism
Researchers using GraphPad Prism can quickly bring existing data into Origin Structured Tables. No manual restructuring required for many common Prism table formats.
- Import data tables from Prism project files (.prism and .pzfx)
- Copy and paste data directly from Prism
- Preserve common life science data layouts
- Continue analysis using Origin's extensive graphing and statistical tools
- Expand beyond traditional Prism workflows with advanced visualization, automation, and data management capabilities
Organize and Explore Data Around Your Experiment
Structured Tables make it easy to organize data the way experiments are actually performed, instead of forcing information into flat spreadsheets.
Examples:
- Cell viability studies with treatment groups and replicates
- Time-course experiments
- Gene and protein expression studies
- Pharmacology and dose-response assays
- Animal studies with multiple factors
- Omics datasets with annotations and metadata
Keep measurements, experimental conditions, sample information, annotations, and metadata together in one place. View data from different perspectives without manually rearranging worksheets. Compare experimental groups and uncover meaningful patterns while preserving the structure and context of your original dataset.
From data collection through analysis and visualization, Structured Tables help you manage complex datasets, improve traceability and reproducibility, and gain deeper insights from your experiments.
Lab Assays Analysis
Streamline common life science assays with dedicated workflows for Western Blot and qPCR data.
qPCR Analysis
Analyze gene expression data with flexible input templates and fold-change calculation methods.
Input templates:
- qPCR with ΔCt Values
- qPCR with Reference Genes
- qPCR with Reference Genes and Technical Replicates
Fold-Change calculation methods:
- Classical ΔΔCt (Livak) Method
- Biological Replicates Method
Western Blot Analysis
Simplify protein expression analysis using dedicated input templates for Western Blot data.
Input templates:
- Western Blot with Raw Band Intensities
- Western Blot with Reference Protein or Total Protein
Multiple-comparison plots generated from Lab Assays analysis reports are displayed in the Graph Browser, allowing users to easily browse plots across different genes and select multiple plots for side-by-side comparison.
Access ready-to-use qPCR and Western Blot templates directly from the Startup dialog. Each template provides a predefined data structure and built-in guidance to help users enter experimental data and quickly get started with analysis.
The Lab Assays dialog provides a decision tree to help users select appropriate statistical tests and post-hoc comparisons based on normality and equal variance assumptions.
Graph Data Directly from Structured Tables
Explore and Visualize Data
Reveal patterns, trends, and relationships in your data. Graphing tools are designed to work seamlessly with structured datasets, making it easy to move from data exploration to presentation.
Choose from a wide range of workflows:
- Bar and column plots
- Scatter and dot plots
- Box charts
- Violin plots
- Line and time-course plots
- Grouped and multi-factor visualizations
- Heat maps
Customize your figures and save your settings as a template for future use. Compare groups, identify trends, detect outliers, and create publication-ready graphs that help you gain insights and tell the story behind your data.
Visualize Statistical Significance
Create Multiple Comparisons and Significance Plots directly from ANOVA, t-test and nonparametric test reports, with statistical results automatically transferred to the graphs.
- Choose from Bar, Box, Violin, and Scatter plots.
- Display p-values and significance annotations.
- Customize significance brackets, stars, and labels.
Multiple Comparisons and Significance Plots created from ANOVA and t-test reports, with statistical results automatically transferred to the graphs. Various plot types and customizable significance annotations help users easily visualize statistical differences and create publication-ready figures.
Statistical Analysis
Perform common life science analyses directly from structured data without manually reorganizing datasets or exporting data to separate statistical software.
Choose from a wide range of statistical methods:
- Descriptive statistics
- t-tests and nonparametric tests
- One-way, two-way, and three-way ANOVA. Repeated measures ANOVA
- Linear regression
- Correlation analyses
- Survival analysis
Analysis tools automatically recognize the experimental factors, groups, and replicates stored in Structured Tables. Move seamlessly from data organization to statistical analysis and visualization while maintaining complete experimental context, traceability, and reproducibility.
One-Way ANOVA analysis report with a built-in Conclusion Chart summarizing the statistical results and a Multiple Comparisons Plot generated directly from the report. These visualizations help users easily interpret analysis results and identify significant differences between groups.
Perform Logistic5 fitting on XY data with replicates directly from a Structured Table. The data are automatically processed to generate fitted dose-response curves with EC50 values for easy comparison.
Dose-response curve fit with automatic outlier detection and removal via robust regression (red point excluded from fit) and EC50 reference lines.
Kaplan-Meier survival curves comparing overall survival among non-small cell lung cancer patients receiving Drug A, Drug B, or standard care. The graph illustrates differences in survival probabilities over time, with censoring events marked on each curve.
Curve Fitting and Dose-Response Analysis
OriginPro provides powerful and easy-to-use curve fitting tools for life science research. A comprehensive library of built-in functions covers dose-response analysis, enzyme kinetics, ligand binding, exponential growth and decay, and other common biological assays, eliminating the need to derive or program fitting equations.
- Fit XY data with replicates directly from Structured Tables.
- Automatically generate initial parameter estimates for built-in fitting functions.
- Obtain biologically meaningful parameters such as EC50, IC50, Kd, Bmax, Km, Vmax, Hill slope, and half-life, along with confidence intervals.
- Automatically update fitting results and graphs when source data changes.
- Create publication-ready graphs with customizable fitted curves and annotations.
Advanced Fitting Options
Perform advanced nonlinear regression through an intuitive graphical interface without programming.
- Replicate Fitting: Fit datasets with replicates without averaging the raw data first.
- Global Fitting: Fit multiple datasets simultaneously with shared parameters.
- Weighted Fitting: Account for measurement errors and unequal variance.
- Parameter Constraints: Fix parameters or specify bounds during fitting.
- Robust Regression: Reduce the influence of outliers.
- Fit Diagnostics: Inspect residuals and goodness-of-fit statistics to evaluate fitting quality.
Reusable Analysis and Batch Processing
Save fitting settings as reusable analysis templates and apply them to new datasets. Automate repetitive fitting tasks across multiple experiments using batch processing.