![]() and the recently published review by Zeng. For a more detailed discussion of this topic, we recommend the review by Wagner et al. activated versus resting) and some researchers use the term “cell identity” to avoid this sometimes arbitrary distinction of cell types, cell subtypes and cell states. ![]() Cell types are therefore often further classified into “subtypes” or “cell states” (e.g. because new technologies allow for a higher resolution view of cells, or because specific “sub-phenotypes” that were not considered biologically meaningful are found to have important biological implications (see e.g. However, like with any categorization the size of categories and the borders drawn between them are partly subjective and can change over time, e.g. For example, knowing that there are specific immune cell types in a tumor or unusual hematopoietic stem cells in your bone marrow sample can be a valuable insight into the disease you might be studying. Knowing which cell types are in your sample is essential in understanding your data. For example, a plasma B cell is a type of white blood cell that secretes antibodies used to fight pathogens and it can be identified using specific markers. ![]() proteins or gene transcripts), and often linked to specific functions. So what is a cell type? Biologists use the term cell type to denote a cellular phenotype that is robust across datasets, identifiable based on expression of specific markers (i.e. based on batch, disease, sex and more), in this notebook we will focus on the annotation of “cell types”. Whereas there are many ways to annotate your cells (e.g. ![]() The process of labeling groups of cells in your data based on known (or sometimes unknown) cellular phenotypes is called “cell annotation”. To understand your data better and make use of existing knowledge, it is important to figure out the “cellular identity” of each of the cells in your data. Gene regulatory networks using chromatin accessibility Gene set enrichment and pathway analysisĢ5. ![]()
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