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We began analyzing https://link.springer.com/article/10.1007/s11704-022-1172-z, but it redirected us to https://link.springer.com/article/10.1007/s11704-022-1172-z. The analysis below is for the second page.

Title[redir]:
Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data | Frontiers of Computer Science
Description:
Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.

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Keywords {🔍}

article, google, scholar, analysis, data, gene, singlecell, rnaseq, japan, clustering, expression, research, detection, nature, university, biology, tsukuba, information, computer, science, sakurai, journal, genes, principal, access, sequencing, society, highly, variable, cell, learning, genome, privacy, cookies, content, pca, sample, space, futamura, cells, noise, component, methods, feature, selection, open, applied, rna, statistical, satija,

Topics {✒️}

single-cell rna-seq profiling single-cell rna-seq experiments single-cell rna-seq data principal component analysis single-cell sequencing data low-cost rna sequencing distortion-free principal components month download article/chapter single-cell analysis rna-seq read counts single-cell data small-cell lung cancer feature selection nist/sematech e-handbook expression network analysis xiucai ye & tetsuya sakurai low-level analysis single-cell genomics rna-seq reads highly variable genes open source software intelligent data analysis exploratory data analysis distortion-free pca rna sequencing related subjects spectral clustering based robust similarity measure gene expression differential expression results residuals-based normalization gene selection improved statistical analysis background noise gene signature identification principal components homogeneous cell population unsupervised clustering privacy choices/manage cookies nucleic acids research artificial intelligence research scrna-seq includes full article pdf gene regulatory networks check access ieee access instant access xiucai ye received microarray data support vector machines

Schema {🗺️}

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         headline:Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data
         description:Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.
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      headline:Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data
      description:Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.
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