MaGIC Gene Expression Plot Tool

Welcome to the Gene Expression Plot Tool by the Molecular and Genomics Informatics Core (MaGIC).

How to Use This Tool

  1. Navigate to the Data Input tab. Upload your Expression Matrix and Sample Metadata files, or click 'Load Demo Data' to explore with a built-in example.
  2. Choose a data transformation (optional). Select from log2, log10, z-score, or VST-like transformations if your data requires it.
  3. Submit your data. Click the Submit button. The Expression Visualization tab will become visible once data is successfully loaded.
  4. Select gene(s) of interest. On the Expression Visualization tab, use single-gene mode for individual plots or multi-gene mode for faceted views.
  5. Customize your plot. Use the control panels in the left sidebar to adjust plot type, statistical annotations, colors, fonts, axes, and more.
  6. Download your results. Download the plot (PNG/PDF) and per-group summary statistics (CSV) from the results panel.

Required Input Data Formats

Expression Matrix
  • File format: CSV or TSV
  • Rows: Genes (one gene per row)
  • Columns: Samples (one sample per column)
  • First column: Gene identifiers (symbols or IDs)
  • All remaining columns: Numeric expression values (raw counts, normalized counts, or TPM)
Gene,   Sample1, Sample2
BRCA1,  6.5,     7.1
TP53,   8.2,     7.9
Sample Metadata
  • File format: CSV or TSV
  • Rows: Samples (one sample per row)
  • First column: Sample names — must match matrix column names exactly
  • Additional columns: Categorical metadata variables (e.g., Group, Treatment, Tissue, Batch)
Sample,  Group,   Gender
Sample1, Control, Male
Sample2, Treated, Female

Input Data

Expression Matrix

Sample Metadata

Use pre-loaded synthetic expression data to explore the tool's features.

Demo dataset: ~5,000 genes across 60 samples in 3 treatment groups with multiple metadata variables.

Gene Selection

Caution: These exploratory statistics are computed directly on expression values and are not a substitute for proper differential expression testing (DESeq2, edgeR, limma, etc.). Using these p-values for publication alongside DE results may constitute statistical double-dipping.

Paste pairwise comparison p-values from DESeq2 or similar.


Download Plot