Scanpy

Load single-cell RNA-seq data from tumor samples.

Preprocess the data to remove noise and normalize gene expression.

Perform clustering to identify distinct cell populations.

Annotate cell types based on marker gene expression.

Analyze cell-cell interactions and communication pathways within the tumor microenvironment.

Load single-cell RNA-seq data from infected and control samples.

Perform data integration to correct for batch effects.

Identify immune cell subtypes and their activation states.

Analyze changes in gene expression profiles in response to infection.

Infer signaling pathways that regulate immune cell function.

Load single-cell RNA-seq data from developing tissues.

Perform pseudotime ordering to infer the temporal sequence of cells.

Identify genes that are dynamically regulated along the developmental trajectory.

Construct gene regulatory networks to understand the control of cell fate.

Validate findings using other experimental techniques.

Load single-cell RNA-seq data from drug-treated and control samples.

Preprocess the data to account for batch effects and technical variation.

Identify cell types present in the samples.

Compare gene expression profiles between drug-treated and control cells within each cell type.

Identify genes that are significantly up- or down-regulated in response to the drug.

Load spatial transcriptomics data.

Integrate the spatial data with single-cell RNA-seq data.

Identify spatially variable genes.

Analyze spatial expression patterns of genes.

Relate spatial gene expression patterns to tissue architecture and cell-cell interactions.