Pattern-Filter structural validation of single-cell RNA-seq reads reduces artifactual barcodes and improves biological resolution.

Single-cell RNA sequencing (scRNA-seq) pipelines rely on the assumption that sequencing reads possess correct structural architecture, a premise we show is incomplete. Standard quantification tools treat errors exclusively as base mismatches, failing to identify structural aberrations arising from off-target priming or nonspecific amplification. We demonstrate that these pervasive artifacts, reads lacking essential anchor motifs like poly(T) tracts, linkers, or template-switching oligos, generate large numbers of spurious barcodes, artificially inflate cell counts, and substantially affect biological interpretation. To resolve this, we developed Pattern-Filter, a universal preprocessing tool that systematically validates read integrity before alignment. It functions by detecting platform-specific anchor sequences and applying strict base-composition filtering to ensure barcodes and UMIs contain only canonical nucleotides. When applied across diverse platforms, including 10x Genomics, Drop-seq, BD Rhapsody, and SPLiT-seq, Pattern-Filter systematically removes 2%-18% of total reads yet reduces spurious barcode diversity by up to 80%. This asymmetric reduction confirms that a small fraction of invalid reads drives the majority of technical noise, compromising cluster stability. Consequently, this targeted removal enhances data reproducibility and recovers biologically relevant cell types, such as dopaminergic neurons in mouse striatum, which were previously obscured by artifact-induced noise. These findings establish structural validation as an essential prerequisite for analysis, positioning Pattern-Filter as a useful standard for ensuring molecular fidelity and reliable biological discovery in single-cell transcriptomics.