The ability to analyze gene expression at the single-cell level—known as single-cell RNA sequencing (scRNA-seq)—has transformed life sciences, driving discoveries across immunology, oncology, and ...

Cell clustering serves as a key task in transcriptomic data analysis, playing a crucial role in cell type annotation, marker gene identification, and the discovery of rare cell populations. With the ...

The integration of single-cell and spatial transcriptomics with advanced metabolic profiling tools has revolutionized the ...

Cell clustering can vary wildly depending on algorithm settings like the random seed — even with the exact same data. scICE automatically detects and removes unstable groupings, giving researchers ...