looking4clusters

looking4clusters enables interactive visualization of dimensional reduction, clustering, and cell properties for scRNA-Seq results.

Key Features:

• Multi-format input support: Accepts numeric matrix, SummarizedExperiment, SingleCellExperiment, or Seurat objects as input.
• Dimensionality reduction: Projects input data into two-dimensional representations using PCA, MDS, t-SNE, UMAP, and NMF.
• Interconnected multi-view display: Displays multiple dimensionality reduction results within the same interface with interconnected graphs to facilitate accurate cell classification.
• Unsupervised clustering integration: Integrates unsupervised clustering techniques with interactively viewable results.
• Manual annotation and selection: Allows manual selection of groups and labeling of cell entities based on processed meta-information.
• Gene expression visualization: Generates new graphs displaying gene expression values for each cell.
• Sample comparison: Enables sample identification and visual comparison of samples and clusters.

Scientific Applications:

• Single-cell RNA-Seq analysis: Visualizes dimensionality reduction, clustering, and cell properties from scRNA-Seq experiments.
• Cell classification: Provides multiple perspectives through interconnected graphs to facilitate accurate classification of cell types.
• Gene expression profiling: Displays gene expression values across individual cells for biological interpretation.
• Sample and cluster comparison: Supports visual comparison of multiple samples and cluster assignments.

Methodology:

The tool generates an interactive HTML page from input data, applies dimensionality reduction methods (PCA, MDS, t-SNE, UMAP, NMF) to project data into two dimensions, and integrates unsupervised clustering techniques with interactive visualization.