clustermarkersofallcells

To find marker genes for all clusters before T or B cell selection, run differential expression testing on unsupervised clusters of mixed immune cells. This identifies broad cell types like T cells, B cells, and NK cells to guide downstream purification.

Identifying cell-type markers across unsupervised clusters is necessary to distinguish broad lineages like myeloid and NK cells in mixed immune populations. This ensures accurate targeted subtyping by verifying cluster identities before selecting specific T or B cells.

Yes, you can configure differential expression parameters including min-pct, logfc-threshold, and test-use. These settings control the sensitivity and specificity of marker gene identification across your unsupervised single-cell clusters.

The best way to identify broad immune cell types in a mixed population is performing differential expression analysis on unsupervised clusters. This detects marker genes for each cluster, enabling broad cell-type identification prior to downstream selection.

Yes, the cluster marker identification workflow supports optional enrichment and plotting through upstream Seurat workflows. This allows you to visualize differential expression results and perform pathway enrichment on the identified marker genes.

You should not use unsupervised cluster markers for cell-type identification if your single-cell dataset lacks clear transcriptional separation or contains highly homogeneous cell populations, as unsupervised clustering may fail to form meaningful distinct groups.