Clustergrammer

Clustergrammer visualizes high-dimensional biological datasets as heatmaps to reveal patterns in gene expression, post-translational modifications, and single-cell proteomics.

Key Features:

• Heatmap visualization: Generates heatmaps for exploration of high-dimensional biological datasets.
• Enrichment analysis: Performs enrichment analysis to identify significant biological patterns within datasets.
• Dynamic gene annotations: Provides dynamic gene annotation overlays linked to visualization and enrichment results.
• Data-type compatibility: Applicable to gene expression, post-translational modification mass spectrometry, and single-cell proteomics datasets.

Scientific Applications:

• CCLE gene expression analysis: Visualization and pattern discovery in gene expression profiles from the Cancer Cell Line Encyclopedia (CCLE).
• Post-translational modification analysis: Analysis of mass spectrometry–derived PTM data in lung cancer cell lines.
• Single-cell proteomics: Analysis of single-cell proteomics datasets acquired using cytometry by time of flight (CyTOF).

Methodology:

Computational steps explicitly include heatmap generation, enrichment analysis, and overlaying dynamic gene annotations on input datasets.