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Advanced Search Results For "cluster analysis"

1 - 10 of 148,954 results for
 "cluster analysis"
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CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts.

Publication Type:Academic Journal

Source(s):Computers in biology and medicine [Comput Biol Med] 2022 Jul; Vol. 146, pp. 105658. Date of Electronic Publication: 2022 May 21.

Abstract:Background: Single-cell RNA-sequencing enables the opportunity to investigate cell heterogeneity, discover new types of cells and to perform transcriptomic reconstruction at a single-cell resolution. Due to technical inadequacy, the presence of dropout...

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Improvements Achieved by Multiple Imputation for Single-Cell RNA-Seq Data in Clustering Analysis and Differential Expression Analysis.

Publication Type:Academic Journal

Source(s):Journal of computational biology : a journal of computational molecular cell biology [J Comput Biol] 2022 Jul; Vol. 29 (7), pp. 634-649. Date of Electronic Publication: 2022 May 16.

Abstract:In a single-cell RNA-seq (scRNA-seq) data set, a high proportion of missing values (or an excessive number of zeroes) are frequently observed. For the related follow-up tasks, such as clustering analysis and differential expression analysis, a data set...

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ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.

Publication Type:Academic Journal

Source(s):BMC bioinformatics [BMC Bioinformatics] 2022 Jul 22; Vol. 23 (1), pp. 291. Date of Electronic Publication: 2022 Jul 22.

Abstract:Background: In recent years, the introduction of single-cell RNA sequencing (scRNA-seq) has enabled the analysis of a cell's transcriptome at an unprecedented granularity and processing speed. The experimental outcome of applying this technology is a [...

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Visualizing Cluster-specific Genes from Single-cell Transcriptomics Data Using Association Plots.

Publication Type:Academic Journal

Source(s):Journal of molecular biology [J Mol Biol] 2022 Jun 15; Vol. 434 (11), pp. 167525. Date of Electronic Publication: 2022 Mar 07.

Abstract:Visualizing single-cell transcriptomics data in an informative way is a major challenge in biological data analysis. Clustering of cells is a prominent analysis step and the results are usually visualized in a planar embedding of the cells using method...

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FSCAM: CAM-Based Feature Selection for Clustering scRNA-seq.

Publication Type:Academic Journal

Source(s):Interdisciplinary sciences, computational life sciences [Interdiscip Sci] 2022 Jun; Vol. 14 (2), pp. 394-408. Date of Electronic Publication: 2022 Jan 14.

Abstract:Cell type determination based on transcriptome profiles is a key application of single-cell RNA sequencing (scRNA-seq). It is usually achieved through unsupervised clustering. Good feature selection is capable of improving the clustering accuracy and i...

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Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis.

Publication Type:Academic Journal

Source(s):Briefings in bioinformatics [Brief Bioinform] 2022 Jul 18; Vol. 23 (4).

Abstract:Developments of single-cell RNA sequencing (scRNA-seq) technologies have enabled biological discoveries at the single-cell resolution with high throughput. However, large scRNA-seq datasets always suffer from massive technical noises, including batch e...

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MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering.

Publication Type:Academic Journal

Source(s):Nucleic acids research [Nucleic Acids Res] 2022 Jul 08; Vol. 50 (12), pp. e71.

Abstract:The standard analysis pipeline for single-cell RNA-seq data consists of sequential steps initiated by clustering the cells. An innate limitation of this pipeline is that an imperfect clustering result can irreversibly affect the succeeding steps. For e...

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scSTEM: clustering pseudotime ordered single-cell data.

Publication Type:Academic Journal

Source(s):Genome biology [Genome Biol] 2022 Jul 07; Vol. 23 (1), pp. 150. Date of Electronic Publication: 2022 Jul 07.

Abstract:We develop scSTEM, single-cell STEM, a method for clustering dynamic profiles of genes in trajectories inferred from pseudotime ordering of single-cell RNA-seq (scRNA-seq) data. scSTEM uses one of several metrics to summarize the expression of genes an...

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Using the Kriging Correlation for unsupervised feature selection problems.

Publication Type:Academic Journal

Source(s):Scientific reports [Sci Rep] 2022 Jul 07; Vol. 12 (1), pp. 11522. Date of Electronic Publication: 2022 Jul 07.

Abstract:This paper proposes a KC Score to measure feature importance in clustering analysis of high-dimensional data. The KC Score evaluates the contribution of features based on the correlation between the original features and the reconstructed features in t...

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Evaluating the performance of dropout imputation and clustering methods for single-cell RNA sequencing data.

Publication Type:Academic Journal

Source(s):Computers in biology and medicine [Comput Biol Med] 2022 Jul; Vol. 146, pp. 105697. Date of Electronic Publication: 2022 Jun 08.

Abstract:Recent advances in single-cell RNA sequencing (scRNA-seq) provide exciting opportunities for transcriptome analysis at single-cell resolution. Clustering individual cells is a key step to reveal cell subtypes and infer cell lineage in scRNA-seq analysi...

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