GECO

GECO performs non-linear dimensionality reduction and clustering of gene and protein expression matrices to visualize and discover expression patterns across bulk RNA-seq, proteomics, and other omics datasets.

Key Features:

• Non-Linear Dimensionality Reduction: Employs t-SNE (t-distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) to reduce data complexity while aiming to preserve intrinsic structure of high-dimensional expression data.
• Customizable Analysis Settings: Provides adjustable dimensionality reduction parameters and data normalization techniques along with visualization filters and coloring schemes.
• Versatile Data Compatibility: Accepts .csv formatted expression matrices and is applicable to bulk RNA-seq, proteomics, and other omics data types with genes, proteins, or other unique identifiers as rows and samples as columns.
• Interactive Visualization and Clustering: Produces visual outputs that cluster genes or proteins by expression patterns across samples to reveal trends such as similar trajectories in multiple cell types, time courses, or dose responses and to identify co-regulated gene programs.

Scientific Applications:

• Exploratory visualization of -omics datasets: Visualizes high-dimensional bulk RNA-seq and proteomics expression matrices to reveal global expression structure and sample relationships.
• Identification of co-regulated genes or proteins: Detects groups of genes or proteins with similar expression trajectories indicative of potential co-regulation.
• Trajectory and time-course analysis: Reveals temporal or dose-dependent expression patterns across samples for time-course or dose-response studies.
• Complementary analysis to statistical methods: Serves as an exploratory complement to conventional statistical analyses by highlighting patterns and relationships not immediately apparent from summary statistics.

Methodology:

Applies non-linear dimensionality reduction using t-SNE and UMAP on .csv expression matrices with configurable normalization and dimensionality-reduction parameters to generate visual clusterings of genes or proteins across samples.