Create publication-ready scatter plots from uploaded research data.
Upload paired measurements, map x and y variables, describe the figure you need, then generate a clean scatter plot with trends, groups, labels, outliers, and manuscript-ready styling.
Upload data and describe the AI chart you need.
Costs 5 credits. Uses your uploaded data and prompt to generate a publication-ready PNG.
Preview
Aspect ratio: 16:9
These are sample previews. Upload your dataset to generate your own scatter plot.
Use these examples as visual directions for correlations, dose-response data, calibration curves, clusters, QC trends, and manuscript-ready relationship figures.
Prompt
Based on the uploaded biomarker_response.csv file, create a clean scientific scatter plot showing biomarker expression on the x-axis and treatment response on the y-axis. Color points by patient cohort, add a linear trend line with a light confidence band, label only the most important outliers, and use a publication-ready white background with readable axis labels.
Prompt
Based on the uploaded dose_response_measurements.xlsx file, create a scatter plot showing drug concentration on the x-axis and measured response on the y-axis. Use a subtle dose-response curve or fitted trend line, show individual replicate points, color groups by treatment condition, and include clear units, compact legends, and manuscript-ready styling.
Prompt
Based on the uploaded patient_outcome_scores.csv file, create a clinical scatter plot comparing baseline score on the x-axis with follow-up outcome score on the y-axis. Color points by responder group, add a diagonal reference line, highlight high-response and low-response clusters, and keep the figure clean for use in a research presentation.
Prompt
Based on the uploaded assay_calibration_curve.tsv file, create a scientific calibration scatter plot with known concentration on the x-axis and signal intensity on the y-axis. Add a fitted regression line, display the equation or R-squared value only if available, show replicate points with light jitter, and use precise axis labels suitable for a methods or supplementary figure.
Prompt
Based on the uploaded single_cell_marker_intensity.csv file, create a scatter plot showing Marker A intensity on the x-axis and Marker B intensity on the y-axis. Color points by cell population, use semi-transparent markers for dense regions, add simple cluster labels where helpful, and preserve a clean scientific layout without excessive decoration.
Prompt
Based on the uploaded batch_qc_metrics.csv file, create a scatter plot showing run order on the x-axis and quality-control measurement on the y-axis. Color points by batch, add a subtle trend line, mark the acceptable QC range with a light shaded band, and label only clear outliers so the chart is easy to read in a lab report.
Add CSV, TSV, TXT, or Excel data with at least two numeric columns for the x and y variables.
Tell SciFigure AI which variables to compare, how to color groups, and whether to include trends, labels, or outlier highlights.
Create a clean scatter plot, check the mapping and labels, then download PNG for papers and slides.
It turns paired scientific measurements into a clear scatter plot for exploring relationships between variables. Use it for expression levels, assay signals, patient outcomes, dose-response measurements, calibration curves, QC metrics, and other x-y datasets.
A scatter plot works best with at least two numeric columns: one for the x-axis and one for the y-axis. Optional group, label, or batch columns can improve the final figure.
Yes. Use a group column such as treatment, cohort, genotype, batch, responder status, or cell population.
Yes. You can request a linear trend, confidence band, calibration fit, reference line, or other visual guide when it helps explain the data.
Yes. Ask the generator to label only the most important outliers or unusual samples so the figure stays readable.
Yes, but always verify the source data, axis labels, statistics, and figure caption before submission.
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