Patterns, Profiles, and Parsimony: Dissecting Transcriptional Signatures From Minimal Single-Cell RNA-Seq Output With SALSA

Frontiers in Genetics
Oswaldo Alonso LozoyaHumphrey Hung-Chang Yao

Abstract

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 c...Continue Reading

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Datasets Mentioned

BETA
GSE63473

Methods Mentioned

BETA
RNA-seq
scRNA-seq
Profiler
DropSeq
single-cell
PCR
sc-RNAseq
scRNAseq
scRNA-seq-based predictions

Software Mentioned

Profiler
LSA
SALSA Profiler
Seurat
Profilers
scImpute
Ensembl
SALSA
DeepImpute
MAGIC

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