Data & Analytics

Advanced NFT and Metaverse analytics agent scanning collections for wash trading and predicting floor price trends.

Advanced NFT and Metaverse analytics agent that scans collections for wash trading and predicts floor price trends.

Advanced NFT and Metaverse analytics agent that scans collections for wash trading, predicts floor prices, and evaluates virtual real estate value.

Advanced NFT and Metaverse analytics agent that scans collections for wash trading and predicts floor price trends.

Crypto Q&A research agent for token analysis, market trends, trading strategies, and macro news.

MCP server for on-chain analytics, whale tracking, and smart money analysis.

Identifies trending tokens and fast-rising crypto projects by analyzing social sentiment, community buzz, and on-chain momentum.

Content intelligence API that analyzes live YouTube, TikTok, and Reddit comments to predict viral content with 47 stars.

Content intelligence API that analyzes live YouTube, TikTok, and Reddit comments to predict viral content.

AI blockchain analytics agent that scans on-chain data, tracks token activity, and generates real-time market insights.

Expert guidance for Malloy, the experimental data language from Google that replaces SQL for analytics with a composable, reusable, and more readable syntax. Helps developers write Malloy models, build nested queries, and explore data with Malloy's VS Code extension and notebook interface.

Parse and write protein structure files using Biopython Bio.PDB. Use when reading PDB, mmCIF, and MMTF files, downloading structures from RCSB PDB, or writing structures to various formats.

Track bacterial strains using MASH, sourmash, fastANI, and inStrain. Compare genomes, detect contamination, and monitor strain-level variation. Use when needing sub-species resolution for outbreak tracking, transmission analysis, or within-host strain dynamics.

Build, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.

Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.

Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.

Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.

Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.

Build spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.

Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.

Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.

Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.

Load spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.

Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.