Data validation checks ensure that all dataset and sample-level metadata annotations contain non-NULL and non-blank values.
Rigorous QC checks to ensure metadata attributes are human-readable and accurately assigned at all levels (dataset, cell).
Normalization and batch Effect correction are applied wherever necessary to eliminate technical variations and enable meaningful comparisons between samples.
Doublets, which can arise during sample preparation and confound analysis, are identified and removed.
Poor-quality samples, genes, and probes are filtered out. Genes that drive biological variation are retained and used for downstream analyses.