scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

Abstract

Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of indi- vidual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity, and extra (e.g., spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Here we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and cell-type annotation on targeted gene profiling data.

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

“…When benchmarking for pre-clustering, the top 1000 variable genes identified by SeuratVST [35], scPNMF (v1.0) [37], and Scater (v1.20.1) [38] were used for dimension reduction, respectively. Rand index (ARI) values were calculated as described previously [37].…”

Probe set selection for targeted spatial transcriptomics

“…Several of these approaches have also been applied to probe set selection 3,4,6 . Dedicated probe set selection approaches that use a reference scRNA-seq dataset to optimize for cell type classification [20][21][22][23][24][25][26][27][28][29][30] , or the capture of transcriptional variation [31][32][33][34] have also been proposed. However, few approaches account for both cell-type and gene variation, and none of the above methods include technical constraints in their selection procedure.…”

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq

“…The number of selected genes is typically dependent on a user-defined threshold, but ordinarily is on the order of one to a few thousand genes [ 2 , 5 ]. A recently developed approach, scPNMF, further addresses the gene complexity problem by leveraging a Non-Negative Matrix Factorization (NMF) representation of scRNA-seq, with selected features being suggested to represent interesting biological variability in the data [ 6 ]. scPNMF relies on the chosen dimension for the NMF representation and also does not directly compare informativeness between different factors, thus impeding the ability to compare the importance (i.e., gene weights) between different factors.…”