单细胞长读长测序大比拼；碱基编辑重建高精度单细胞家谱；深度学习预测细胞周期；新技术助力肿瘤免疫反应定位、让旧数据重获免疫信息 等
Ranking
Observed public metrics from 1 member.
Merged summary
TL;DR - A WeChat digest of recent single-cell methods papers: a systematic benchmark of nanopore long-read single-cell/spatial analysis tools, a CRISPR base-editing lineage recorder (BASELINE), and deep-learning cell-cycle phase prediction from scRNA-seq. It matters because it maps which computational tools to trust for isoform-level, lineage-level, and cell-state-level single-cell analysis.
- Long-read benchmark (NAR Genom Bioinform): 10 tools compared on paired Illumina/ONT MPNST mouse data, Visium spatial long reads, and AsaruSim simulations. wf-single-cell was most robust for barcodes (precision ~0.91, recall ~0.99), UMI correction, and gene quantification; Sicelore 2.1 led known-isoform read assignment (F1 ~0.88); Bambu/Isosceles led novel isoform discovery (~70–73% F1). Long-read-only pipelines no longer require paired short reads.
- BASELINE (Nucleic Acids Research): Cas12a adenine base editor writes A→G marks into a 1.5 kb, 50-target array (272 editable A's) without double-strand breaks, avoiding Cas9-style large deletions. ONT + UMI consensus cut error from ~1.5% to ~0.0024% (Q46); ~98% of 35,035 KPCY cells linked to lineage, with trees averaging 29 divisions deep.
- Cell-cycle prediction (Briefings in Bioinformatics): Consensus labels from four existing tools (73–75% agreement with FUCCI ground truth) trained DNN/ensemble models; Top-3 fusion hit 74.3% on GSE146773 vs ~68.3% for Revelio, and 56.9% cross-species on mouse ESCs. Gene-intersection preprocessing beat zero/mean imputation; SHAP showed little overlap with canonical Seurat/Revelio markers.
- Common thread: all three depend on data quality upstream — R10.4.1 chemistry and PromethION depth narrowed tool gaps, consensus sequencing enabled lineage inference, and proliferation-rich training data (REH) outperformed quiescent PBMC/brain data.
Sources (1)
单细胞长读长测序大比拼；碱基编辑重建高精度单细胞家谱；深度学习预测细胞周期；新技术助力肿瘤免疫反应定位、让旧数据重获免疫信息 等
TL;DR - A WeChat digest of recent single-cell methods papers: a systematic benchmark of nanopore long-read single-cell/spatial analysis tools, a CRISPR base-editing lineage recorder (BASELINE), and deep-learning cell-cycle phase prediction from scRNA-seq. It matters because it maps which computational tools to trust for isoform-level, lineage-level, and cell-state-level single-cell analysis.
- Long-read benchmark (NAR Genom Bioinform): 10 tools compared on paired Illumina/ONT MPNST mouse data, Visium spatial long reads, and AsaruSim simulations. wf-single-cell was most robust for barcodes (precision ~0.91, recall ~0.99), UMI correction, and gene quantification; Sicelore 2.1 led known-isoform read assignment (F1 ~0.88); Bambu/Isosceles led novel isoform discovery (~70–73% F1). Long-read-only pipelines no longer require paired short reads.
- BASELINE (Nucleic Acids Research): Cas12a adenine base editor writes A→G marks into a 1.5 kb, 50-target array (272 editable A's) without double-strand breaks, avoiding Cas9-style large deletions. ONT + UMI consensus cut error from ~1.5% to ~0.0024% (Q46); ~98% of 35,035 KPCY cells linked to lineage, with trees averaging 29 divisions deep.
- Cell-cycle prediction (Briefings in Bioinformatics): Consensus labels from four existing tools (73–75% agreement with FUCCI ground truth) trained DNN/ensemble models; Top-3 fusion hit 74.3% on GSE146773 vs ~68.3% for Revelio, and 56.9% cross-species on mouse ESCs. Gene-intersection preprocessing beat zero/mean imputation; SHAP showed little overlap with canonical Seurat/Revelio markers.
- Common thread: all three depend on data quality upstream — R10.4.1 chemistry and PromethION depth narrowed tool gaps, consensus sequencing enabled lineage inference, and proliferation-rich training data (REH) outperformed quiescent PBMC/brain data.