Single-Cell Transcriptomics, Spatial Omics & Cell Atlases

Filter low-quality droplets (mitochondrial % > 15%, doublet removal with Scrublet, min genes > 500).

Assign standardized cell ontology labels using pretrained immune and organ-specific reference atlases.

Correct donor and sequencing cohort batch variation using variational autoencoders without distorting biological signal.

Infer cell fate lineage trajectories and identify key driver transcription factors along differentiation pathways.

Generate contextual gene and cell embeddings to uncover hidden disease state signatures across patient cohorts.

Map single-cell identified tumor-infiltrating lymphocyte subsets into 10x Visium / Xenium spatial tumor microenvironment slides.

Simulate combinatorial CRISPR knockouts to predict which dual-target knockouts revert exhausted T cells back to effector phenotypes.

Export prioritized target list to wet-lab screening team for primary human cell assay validation.