Single Cell RNA-Seq Cell Type Prediction

Identifying cell types is a crucial yet challenging step in single-cell RNA sequencing analysis. The complexity arises from the vast heterogeneity of cell populations and the subtle differences in gene expression profiles, even within similar cell types. Cell type prediction algorithms play a pivotal role in addressing this challenge by comparing the gene expression profiles of the cells under study to annotated reference datasets.

While these tools provide valuable insights, it is important to recognize that they do not offer definitive predictions. Instead, they serve as an additional layer of evidence, guiding researchers toward a more informed interpretation of the likely cell types present in their data. Further exploration of the data with other references and by looking at group-specific gene expression is still needed to achieve a robust prediction.

Nevertheless, OmicsBox offers two powerful algorithms to perform cell type prediction:

• SingleR. This tool labels individual cells by comparing their gene expression profiles against a reference dataset, which can be downloaded from public databases. This approach assigns each cell a type based on gene expression profile similarities.
• CellKb. This tool labels groups of cells by comparing their differentially expressed genes against a curated knowledge base. With this tool, no external reference annotation is needed. A differential expression analysis with Scanpy between groups is performed previous to cell type annotation.