Single Cell Explorer-AI-powered single cell analysis

Example

Assisting in the preprocessing of scRNA-seq data, including quality control, normalization, and scaling.

Scenario

A researcher has raw scRNA-seq data and needs to preprocess it to remove low-quality cells and normalize gene expression levels. Single Cell Explorer can provide step-by-step instructions and recommend tools like Seurat or Scanpy for this purpose.

Example

Helping identify cell types within a heterogeneous population using clustering algorithms and marker gene analysis.

Scenario

A scientist is analyzing a mixed cell population and needs to classify different cell types. Single Cell Explorer can suggest clustering methods, such as Louvain or K-means, and guide the user through marker gene identification to label clusters accurately.

Example

Facilitating the integration of single-cell transcriptomics with other omics data, like proteomics or epigenomics.

Scenario

A researcher wants to correlate gene expression data with chromatin accessibility profiles. Single Cell Explorer can provide protocols and tools, like Seurat’s multi-omics integration feature, to combine these datasets and extract meaningful biological insights.