Patterns, Profiles, and Parsimony: Dissecting Transcriptional Signatures From Minimal Single-Cell RNA-Seq Output With SALSA.
Patterns, Profiles, and Parsimony: Dissecting Transcriptional Signatures From Minimal Single-Cell RNA-Seq Output With SALSA.
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DOI：
10.3389/fgene.2020.511286
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发表时间：
2020
影响因子：
3.7
通讯作者：
Yao HH
中科院分区：
文献类型：
--
作者：
Lozoya OA;McClelland KS;Papas BN;Li JL;Yao HH
单细胞RNA测序（scRNA-seq）技术促进了生物信息学工具的发展，以单细胞精度重建细胞谱系规范和分化过程。然而，目前用于统计分析的启动成本和推荐数据量仍然非常昂贵，阻碍了scRNA-seq技术成为主流。在这里，我们介绍了潜在语义分析（SALSA）的单细胞融合，这是一种多功能的工作流程，将测量可靠性指标与潜在变量提取相结合，以从超稀疏sc-RNAseq数据中推断出强大的表达谱。SALSA使用矩阵聚焦方法，该方法从识别表达水平大于实验测量精度的兼性基因开始，并以基于Profiler基因的最小集合的细胞聚类结束，每个基因都是簇特异性表达谱的推定生物标志物。为了对SALSA在实验环境中的表现进行基准测试，我们使用了公开可用的10 X Genomics PBMC 3 K数据集，一种来自人冷冻外周血的预先策划的银标准品，包含2，700个单细胞条形码，并鉴定了与外周血细胞类型的转录谱匹配并由< 500个Profiler基因不可知地驱动的7个主要细胞群。最后，我们通过使用先前发表的来自多批次小鼠视网膜实验设计的DropSeq数据，在复制scRNA-seq场景中成功实施了SALSA，从而基于< 630个Profiler基因，从7个独立的生物重复中从> 64，000个单细胞中鉴定出10种转录上不同的细胞类型。有了这些结果，SALSA证明了scRNA-seq表达矩阵的强大模式检测只需要一小部分累积数据，这表明单细胞测序技术如果作为假设生成工具来提取大规模差异表达效应，可以变得负担得起和广泛。
Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, current start-up costs and recommended data volumes for statistical analysis remain prohibitively expensive, preventing scRNA-seq technologies from becoming mainstream. Here, we introduce single-cell amalgamation by latent semantic analysis (SALSA), a versatile workflow that combines measurement reliability metrics with latent variable extraction to infer robust expression profiles from ultra-sparse sc-RNAseq data. SALSA uses a matrix focusing approach that starts by identifying facultative genes with expression levels greater than experimental measurement precision and ends with cell clustering based on a minimal set of Profiler genes, each one a putative biomarker of cluster-specific expression profiles. To benchmark how SALSA performs in experimental settings, we used the publicly available 10X Genomics PBMC 3K dataset, a pre-curated silver standard from human frozen peripheral blood comprising 2,700 single-cell barcodes, and identified 7 major cell groups matching transcriptional profiles of peripheral blood cell types and driven agnostically by < 500 Profiler genes. Finally, we demonstrate successful implementation of SALSA in a replicative scRNA-seq scenario by using previously published DropSeq data from a multi-batch mouse retina experimental design, thereby identifying 10 transcriptionally distinct cell types from > 64,000 single cells across 7 independent biological replicates based on < 630 Profiler genes. With these results, SALSA demonstrates that robust pattern detection from scRNA-seq expression matrices only requires a fraction of the accrued data, suggesting that single-cell sequencing technologies can become affordable and widespread if meant as hypothesis-generation tools to extract large-scale differential expression effects.
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影响因子：
4.6
作者：
Cong Y;Chan YB;Ragan MA
通讯作者：
Ragan MA
影响因子：
7.3
作者：
Churlaud G;Pitoiset F;Jebbawi F;Lorenzon R;Bellier B;Rosenzwajg M;Klatzmann D
通讯作者：
Klatzmann D
影响因子：
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作者：
Andrews, Tallulah S;Hemberg, Martin
通讯作者：
Hemberg, Martin
DOI：
10.1073/pnas.1919800117
发表时间：
2020-02-11
影响因子：
11.1
作者：
Di, Lin;Fu, Yusi;Wang, Jianbin
通讯作者：
Wang, Jianbin
影响因子：
48
作者：
Cloonan, Nicole;Forrest, Alistair R. R.;Grimmond, Sean M.
通讯作者：
Grimmond, Sean M.