Mouse and Human Prostate Cancer Single-Cell and Spatial Transcriptomics Data

Hubert Pakula from Cornell University provides normalized count matrices and cell annotations for mesenchymal prostate cancer data. The collection includes single-cell RNA-seq data from mouse models and human primary tumors, bone metastasis data, and Visium spatial transcriptomics data from mouse models. Files are in .h5ad and .csv formats, containing gene expression and regulon activity matrices.

Use Cases

• Identify distinct mesenchymal cell states based on single-cell RNA-seq expression profiles.
• Analyze regulon activity in different cell clusters using the provided AUC and binary matrices.
• Compare gene expression patterns between primary tumors and bone metastasis samples.
• Integrate spatial transcriptomics data from Visium platforms to map cell locations.
• Validate findings across mouse models and human patient data.

Strengths

• Includes data from multiple sources: mouse models, human primary cancer, and bone metastasis.
• Provides both single-cell RNA-seq and spatial transcriptomics (Visium) data formats.
• Contains pre-processed normalized count matrices and cell annotations.
• Includes additional analysis files for regulon activity (AUC and binary matrices).

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Cornell University

Collection Method

Data generated for the paper 'distinct mesenchymal cell states mediate prostate cancer progression', likely from laboratory experiments and public repositories.