ICARUS

ICARUS performs single-cell RNA-seq data processing, analysis, and visualization using Seurat to characterize gene expression patterns and cell populations.

Key Features:

• Seurat integration: Uses Seurat for core single-cell analysis functions including preprocessing, clustering, and visualization.
• Data pre-processing: Provides workflows for data pre-processing and application of quality control thresholds.
• Dimensionality reduction: Supports parameter modification and computation of 2D and 3D UMAP and t-SNE embeddings.
• Cell clustering: Enables parameter adjustment for cell clustering to define cell populations.
• Cell cycle curation: Includes dataset curation options to mitigate confounding from cell cycle heterogeneity.
• Multispecies support: Supports human, dog, mouse, rat, zebrafish, fruit fly, nematode, yeast, cattle, chicken, and pig.
• Multimodal visualization: Visualizes multimodal datasets generated by technologies such as CITE-seq and the 10X Genomics Multiome kit.
• Differential expression analysis: Performs differential expression comparisons with user-defined cell group definitions.
• Gene set enrichment analysis: Conducts gene set enrichment analyses to identify impacted biological pathways.
• Save analysis state: Provides a function to save the current analysis state to reduce computational demands for subsequent sessions.

Scientific Applications:

• scRNA-seq preprocessing and QC: Apply quality control thresholds and preprocessing to single-cell transcriptomic datasets.
• Cell population identification: Use dimensionality reduction and clustering to identify and characterize cell populations.
• Transcriptomic visualization: Explore transcriptomic landscapes with 2D and 3D UMAP and t-SNE plots.
• Multimodal dataset analysis: Visualize and interpret multimodal data from CITE-seq and 10X Genomics Multiome experiments.
• Differential expression studies: Compare gene expression between defined cell groups to identify marker genes.
• Pathway analysis: Perform gene set enrichment to detect biological pathways associated with expression changes.
• Cross-species analyses: Analyze scRNA-seq data across supported organisms including human, mouse, and non-model species listed above.

Methodology:

Implements Seurat-based data pre-processing and quality-control thresholding, dimensionality reduction (2D/3D UMAP and t-SNE), cell clustering, cell cycle curation, multimodal visualization for CITE-seq and the 10X Genomics Multiome kit, differential expression with user-defined groups, gene set enrichment analysis, and saving of analysis state.