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Free MA Plot Generator for Expression Data

Plot processed expression values with explicit transformations, feature labels and a zero-change reference.

MA plot editor

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Free MA Plot Generator for Expression Data— templates & examples

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Free scientific tools

Free MA Plot Generator for Expression Data

Plot processed expression values with explicit transformations, feature labels and a zero-change reference.

An MA plot compares log abundance on the A axis with log2 fold change on the M axis. Plot processed expression values with explicit transformations, feature labels and a zero-change reference. A and M supplied directly. baseMean transformed to log2 abundance. Normalized control and treatment mode.

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MA scatter plots comparing normalized abundances and baseMean input
  • Choose the meaning of your columns
  • Read abundance against fold change
  • Identify features on the plot
  • Save the expression comparison

MA Plot Generator for Expression Data Features

A and M supplied directly. baseMean transformed to log2 abundance. Normalized control and treatment mode.

Choose the meaning of your columns

Tell SciFig whether the input contains A/M values, baseMean with log2 fold change, or positive normalized control and treatment abundances. The chosen mode determines the transformation, keeping already calculated log values from being logged a second time.

  • A and M supplied directly
  • baseMean transformed to log2 abundance
  • Normalized control and treatment mode
Choose the meaning of your columns

Read abundance against fold change

Draw the MA scatter with an M=0 reference and a visible fold-change threshold. For normalized control C and treatment T, the plot uses A=(log2 C+log2 T)/2 and M=log2 T−log2 C, making the axes interpretable from the input mode.

  • Explicit A and M axis meanings
  • Zero-change reference line
  • Editable fold-change threshold
Read abundance against fold change

Identify features on the plot

Retain feature IDs with the calculated coordinates so selected points can be labeled and inspected. Filtering changes the visible point selection while preserving the underlying input values and their defined transformation.

  • Feature identifiers retained
  • Point labels and filtering
  • Coordinates available as CSV
Identify features on the plot

Save the expression comparison

Download SVG or PNG for a report and CSV for the calculated A/M coordinates. Save JSON to keep the input mode and plot settings available when the comparison needs new labels or a revised presentation.

  • SVG and PNG output
  • Calculated-coordinate CSV
  • Reusable project JSON
Save the expression comparison

How to Plot Your Values

Choose direct A/M, baseMean/log2FC or normalized control/treatment before pasting the CSV.

  1. Select the expression input mode

    Choose direct A/M, baseMean/log2FC or normalized control/treatment before pasting the CSV. Use positive abundance values in modes that apply log2; already calculated A/M values can include zero or negative coordinates.

  2. Paste the feature values

    Enter the feature identifiers and numerical columns in the displayed format. The tool calculates the A and M coordinates according to the selected semantics and draws the expression comparison.

  3. Set labels and thresholds

    Adjust the fold-change threshold and the visible feature selection. Use point labels to identify features in the figure while retaining M=0 as the reference for unchanged abundance.

  4. Export the comparison

    Download SVG or PNG for the report, or CSV for the calculated points. Save the JSON project to preserve the original input mode and display settings for another editing session.

Graphs for Experimental Work

Start from normalized control and treatment abundance values to see fold change against average log abundance.

MA scatter plots comparing normalized abundances and baseMean input

Explore a processed expression contrast

Start from normalized control and treatment abundance values to see fold change against average log abundance. The explicit transformation makes the comparison reproducible from the two numerical columns.

Explore this example
MA plot computed from positive normalized control and treatment abundances

Present an analysis output

Paste baseMean and log2 fold change from an existing differential-expression workflow. The plot presents those processed values with feature IDs rather than repeating the statistical analysis.

Explore this example
MA scatter with A and M coordinates supplied directly

Teach the meaning of A and M

Use known abundance pairs in a classroom example to connect the formula with a point on the axes. Control 4 and treatment 16 produce A=3 and M=2 in normalized mode.

Explore this example
MA scatter using log2 baseMean and supplied log2 fold changes

Discuss selected gene features

Label a focused group of feature IDs for a laboratory meeting. The zero-change line and fold-change threshold provide common references while the input coordinates remain available in CSV.

Explore this example
MA plot with user-supplied adjusted p values and labeled features

Compare figure presentations

Save a project before adapting labels or filtering the visible points for a slide. Reopening the configuration preserves the expression values and their transformation while the presentation changes.

Explore this example
MA plot computed from positive normalized control and treatment abundances

Hand a figure to a collaborator

Share the SVG with the plotted-coordinate CSV and project JSON. The recipient can connect feature labels with their calculated values and reopen the same plot settings for subsequent edits.

Explore this example

How Researchers Use SciFig

The MA plot generator let me start with the A and M values already prepared for our lab meeting. I retained the gene identifiers and used the zero reference to explain the comparison. Exporting the plotted CSV alongside SVG meant the figure and the values discussed by the group stayed together.

Rina Das

Transcriptomics researcher · University of Cambridge

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