Deterministic column subset selection for single-cell RNA-Seq

Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components analysis (PCA) fail to preserve non-negativity an...

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Bibliographic Details
Published in:PloS one 2019-01, Vol.14 (1), p.e0210571-e0210571
Main Authors: McCurdy, Shannon R, Ntranos, Vasilis, Pachter, Lior
Format: Article
Language:English
Subjects:
Algorithms
Applied mathematics
Bioinformatics
Biology
Biology and Life Sciences
Case studies
Cluster Analysis
Clustering
Collinearity
Consortia
Covariance
Eigen values
Eigenvalues
Engineering and Technology
Gene expression
Gene Expression Profiling - methods
Gene sequencing
Genes
Genomes
Genomics
High-Throughput Nucleotide Sequencing - methods
Methods
Noise reduction
Ontology
Physical Sciences
Principal Component Analysis
Principal components analysis
Research and Analysis Methods
Ribonucleic acid
RNA
RNA - genetics
RNA sequencing
RNA, Small Cytoplasmic - genetics
Scientific visualization
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Sparsity
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Summary:Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components analysis (PCA) fail to preserve non-negativity and sparsity structures present in the original matrices, and the coordinates of projected cells are not easily interpretable. Commonly used thresholding methods to filter genes avoid those pitfalls, but ignore collinearity and covariance in the original matrix. We show that a deterministic column subset selection (DCSS) method possesses many of the favorable properties of common thresholding methods and PCA, while avoiding pitfalls from both. We derive new spectral bounds for DCSS. We apply DCSS to two measures of gene expression from two scRNA-Seq experiments with different clustering workflows, and compare to three thresholding methods. In each case study, the clusters based on the small subset of the complete gene expression profile selected by DCSS are similar to clusters produced from the full set. The resulting clusters are informative for cell type.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0210571