SALSA – infer robust expression profiles from ultra-sparse sc-RNAseq data

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. NIEHS researchers 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, the researchers 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, they 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.

Depiction of expression matrix focusing by total per-gene and per-barcode coverage
with the parametric PC-PD mixture model

(A) Sorted count data from scRNA-seq experiments exhibits transitions in total UMI counts per barcode, reminiscent of distinct regimes of UMI density between background (ambient noise), single-cell, and multi-cell barcodes; total UMI counts per gene exhibit an analogous profile, with distinct regimes between rare, facultative, and constitutively expressed genes. Latent patterns of expression within gene-cell matrices are most discriminative at the intersection of facultative genes and single-cell barcodes regimes, referred to as the focused expression matrix. To infer coverage regimes per barcode (B) and per gene aligned (C) from the raw gene-cell expression matrix, total UMI count data are fit to a 2-component mixture probabilistic parametric model; regime thresholds are defined systematically from estimated scale and shape parameters. (D) Stratified differential expression analysis starting from a focused expression matrix in SALSA. The flow chart depicts transformations used in SALSA toward generalized linear modeling (GLM) of expression data, and statistical criteria to extract significant gene subsets with rising statistical stringency.

Lozoya OA, McClelland KS, Papas BN, Li JL, Yao HH. (2020) Patterns, Profiles, and Parsimony: Dissecting Transcriptional Signatures From Minimal Single-Cell RNA-Seq Output With SALSA. Front Genet 11:511286. [article]

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