Single-Cell Datasets for Temporal Gene Expression Integration

Multiple single-cell RNA sequencing datasets compiled by Jolene S. Ranek at the University of North Carolina at Chapel Hill for comparing temporal gene expression integration methods. The collection includes preprocessed data objects and raw files from public repositories like ArrayExpress and Gene Expression Omnibus. It covers biological processes such as mouse embryonic cell cycles, hematopoiesis, immune cell differentiation, and disease states including acute myeloid leukemia and multiple sclerosis.

Use Cases

• Benchmarking temporal gene expression integration methods based on the provided preprocessed adata objects.
• Studying cell differentiation dynamics using the hematopoiesis and NKT cell datasets mentioned in the description.
• Analyzing disease progression and treatment response based on the acute myeloid leukemia (AML) and multiple sclerosis (MS) datasets.
• Investigating cellular responses to stimuli using the LPS and INF-gamma stimulation datasets.

Strengths

• Includes data from at least 8 distinct biological studies, providing a diverse set of scenarios for method testing.
• Provides both raw loom files and preprocessed adata objects, catering to different analysis workflows.
• All datasets are sourced from established public repositories (ArrayExpress, GEO) with provided accession codes.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and file sizes are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Gene Expression Omnibus (GEO) and ArrayExpress

Collection Method

Compilation of publicly available single-cell RNA-seq datasets from published studies.