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Optimal marker gene selection for cell type discrimination in single cell analyses.

ABSTRACT: Single-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit?selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization.

SUBMITTER: Dumitrascu B

PROVIDER: S-EPMC7895823 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Optimal marker gene selection for cell type discrimination in single cell analyses.

Dumitrascu Bianca B Villar Soledad S Mixon Dustin G DG Engelhardt Barbara E BE

Nature communications 20210219 1

Single-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative m ...[more]

PMID: 33608535

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Project description:BackgroundWe have developed a rice-based oral cholera vaccine named MucoRice-CTB (Cholera Toxin B-subunit) by using an Agrobacterium tumefaciens-mediated co-transformation system. To assess the genome-wide effects of this system on the rice genome, we compared the genomes of three selection marker-free MucoRice-CTB lines with those of two wild-type rice lines (Oryza sativa L. cv. Nipponbare). Mutation profiles of the transgenic and wild-type genomes were examined by next-generation sequencing (NGS).ResultsUsing paired-end short-read sequencing, a total of more than 300 million reads for each line were obtained and mapped onto the rice reference genome. The number and distribution of variants were similar in all five lines: the numbers of line-specific variants ranged from 524 to 842 and corresponding mutation rates ranged from 1.41 × 10(-6) per site to 2.28 × 10(-6) per site. The frequency of guanine-to-thymine and cytosine-to-adenine transversions was higher in MucoRice-CTB lines than in WT lines. The transition-to-transversion ratio was 1.12 in MucoRice-CTB lines and 1.65 in WT lines. Analysis of variant-sharing profiles showed that the variants common to all five lines were the most abundant, and the numbers of line-specific variant for all lines were similar. The numbers of non-synonymous amino acid substitutions in MucoRice-CTB lines (15 to 21) were slightly higher than those in WT lines (7 or 8), whereas the numbers of frame shifts were similar in all five lines.ConclusionsWe conclude that MucoRice-CTB and WT are almost identical at the genomic level and that genome-wide effects caused by the Agrobacterium-mediated transformation system for marker-free MucoRice-CTB lines were slight. The comparative whole-genome analyses between MucoRice-CTB and WT lines using NGS provides a reliable estimate of genome-wide differences. A similar approach may be applicable to other transgenic rice plants generated by using this Agrobacterium-mediated transformation system.

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Optimal marker gene selection for cell type discrimination in single cell analyses.

Publications

Optimal marker gene selection for cell type discrimination in single cell analyses.

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