How to plot and analyze continuous (XY) data in BioRender Graphing How to plot and analyze continuous (XY) data in BioRender Graphing

How to plot and analyze continuous (XY) data in BioRender Graphing

Continuous data shows the relationship between two variables - an independent variable on the X-axis and a dependent variable on the Y-axis. In this guide, you'll learn how to plot and interpret this type of data using BioRender Graphing.

💡 Tip: You can upload your data directly using Smart Data Import - BioRender will automatically detect your variables and prepare your data for graphing.

Step 1: Create Your Graph

  • From the Gallery, click Create new > Graphing.
  • Upload a Dataset or start from a blank spreadsheet.

❗ Note: You may see both ‘Graphing’ and ‘Graph’ in the menu. Graphing is BioRender’s newer graphing experience, while Graph refers to the classic version of BioRender Graph, which will be retired September 30th, 2026.

Step 2: Select your data

  • Select your datasets.
  • Choose which dataset you want to use. 
  • Click “Create new Graph”.

Step 3: Choose your graph type

Select the visualization that best fits your data and what you want to communicate:

Scatter plot: Shows every individual data point at its exact X and Y coordinates. Best used when you want to show the full spread of your data, or run a correlation or regression analysis. Fit lines and confidence intervals display directly on the plot.

Dose-response: Shows a curve fitted to your data showing how the measured response changes as dose or concentration increases, with summary statistics (mean or median) and error bars (SD, SEM, or 95% CI) at each dose level. Best used when you want to model the relationship between dose and effect, identify key values like EC50 or IC50, or compare how different conditions respond across a concentration range.

Line plot: Shows data points connected by a line, displaying mean or median when multiple observations exist per X value. Best used when you want to emphasize trend or continuity across X values, such as a time course experiment.

 

Step 4: Assign your variables

Assign your variables:

  • X-axis variable - your independent variable 
  • Y-axis variable - your dependent variable
  • Grouping variable (optional) - splits your data by condition or group. 
    • Important to note: If your data contains groups, always assign this - without it, all data points will be treated as one population.

Step 5: Run an analysis

From the graph editing page, click Run analysis. A structured form appears with three sections that guide you through selecting and configuring the right test for your data.

Section 1 - Analysis type

Based on your graph type and data characteristics, you'll see the available analysis options. Some tests may appear greyed out if your data doesn't meet the requirements - hover over the tooltip to understand why.

For continuous XY data, common analysis types include:

Analysis type What it does Use when…
Correlation Analysis Measures the strength and direction of the relationship between two variables. Reports r (Pearson) or ρ (Spearman), p-value, and 95% CI. You want to know whether two variables are associated. BioRender recommends Pearson when both your X and Y variables are normally distributed, and Spearman otherwise. 
Linear Regression Fits a straight line to your data and reports slope, intercept, R², and p-value. The relationship between X and Y appears linear and you want to model or predict it.
Nonlinear Regression Fits a curve to your data using a nonlinear model (e.g., sigmoidal dose-response). Your data follows a curve - for example, EC50/IC50 dose-response analysis.

You can also select which categorical groups to include in the analysis. By default, all groups from the graph are included - deselect any you want to exclude from this particular analysis.

Section 2 - Fine-tune your analysis

This section lets you adjust statistical settings based on your experimental design. BioRender pre-populates recommendations based on tests run on your data, but you can modify them if needed. 

Options vary by analysis type and may include:

  • Data setup - define how your data is organized (e.g., independent vs. repeated measures, paired vs. unpaired).
  • Advanced options - expand to access additional configuration settings where available.
  • Assumptions - some analyses include configurable assumptions you can review and adjust.

Section 3 - Options and additional settings

Some analyses include additional options to refine how results are compared and calculated. These settings only appear when relevant to your selected analysis, and recommended options are applied automatically. 

Note: The below screenshot will look different for each analysis method. Select your analysis type to see the options for your analysis.

Step 6: Choose your display option

  • After running your analysis, use the display options in the left panel to toggle what appears on the graph - correlation lines, confidence intervals, coefficients, and p-values.
  • You can run multiple analyses on the same graph and control each independently.

Need help? Reach out to our support team at support@biorender.com or start a live chat by clicking the "Chat With Us" bubble in the bottom right corner.

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