Genetics & GenomicsUpdated Aug 18, 2026Version v1
Reviewed milestones, validation shifts, standards, datasets, and debates linked to public evidence.
Evidence from Nature Communications indicates that A map of the cellular landscape of the human liver was generated using single-cell RNA sequencing. This is tracked as a milestone because it changes how Single-Cell Genomics is understood, validated, or applied.
Evidence from Experimental & Molecular Medicine indicates that scRNA-seq overcomes bulk sequencing limitations by analyzing individual cells. This is tracked as a clinical because it changes how Single-Cell Genomics is understood, validated, or applied.
Evidence from Database indicates that PanglaoDB hosts over 1054 single-cell experiments with more than 4 million cells. This is tracked as a dataset because it changes how Single-Cell Genomics is understood, validated, or applied.
Evidence from GigaScience indicates that Ambient RNA contamination is a significant issue in droplet-based scRNA-seq. This is tracked as a standard because it changes how Single-Cell Genomics is understood, validated, or applied.
Evidence from Nature Methods indicates that Highly variable gene selection improves data integration performance. This is tracked as a dataset because it changes how Single-Cell Genomics is understood, validated, or applied. It is supported by 5 papers in the same timeline signal.
Evidence from Nature indicates that Microbiota populates microniches characterized by low vascularization and immunosuppression. This is tracked as a clinical because it changes how Single-Cell Genomics is understood, validated, or applied.
Evidence from Experimental & Molecular Medicine indicates that Single-cell omics overcomes bulk sequencing limitations by analyzing individual cells. This is tracked as a review because it changes how Single-Cell Genomics is understood, validated, or applied.
Evidence from Nucleic Acids Research indicates that SPOTlight accurately deconvolutes ST spots by leveraging scRNA-seq data. This is tracked as a clinical because it changes how Single-Cell Genomics is understood, validated, or applied.
Evidence from Genome Medicine indicates that Spatial transcriptomics preserves spatial context lost in traditional scRNA-seq. This is tracked as a review because it changes how Single-Cell Genomics is understood, validated, or applied. It is supported by 2 papers in the same timeline signal.
Evidence from Genome biology indicates that Poisson model is appropriate for sparse scRNA-seq data. This is tracked as a standard because it changes how Single-Cell Genomics is understood, validated, or applied.
Evidence from Nature Methods indicates that SnapATAC2 achieves precise capture of single-cell omics data heterogeneities. This is tracked as a dataset because it changes how Single-Cell Genomics is understood, validated, or applied.