Sciviewer

Sciviewer visualizes two-dimensional embeddings derived from single-cell RNA sequencing (scRNA-Seq) data to support interpretation of gene-expression patterns.

Key Features:

• 2D embedding visualization: Displays and facilitates exploration of two-dimensional embeddings such as UMAP and tSNE computed from high-dimensional scRNA-Seq data.
• Differential expression analysis: Performs differential expression testing on user-selected cells within the embedding space.
• Local gene variation identification: Identifies genes that vary locally along any user-specified direction on the 2D embedding.

Scientific Applications:

• Cellular heterogeneity analysis: Interpretation of gene-expression patterns to characterize heterogeneity within single-cell populations.
• Cell population identification and clustering: Support for clustering and identification of distinct cell populations based on expression profiles.
• Differential gene expression exploration: Investigation of genes differentially expressed across conditions or cell types using selections in embedding space.
• Pathway and local-variation discovery: Detection of locally varying genes that can reveal novel biological pathways or spatially localized expression signals.

Methodology:

Operates on two-dimensional embeddings (e.g., UMAP, tSNE) derived from high-dimensional scRNA-Seq data, performs differential expression analysis on selected cells within the embedding, and computes local gene variation along arbitrary directions on the 2D embedding.