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Keywords {🔍}

cells, quality, low, cell, features, pubmed, fig, high, data, article, google, scholar, additional, genes, expression, file, cas, number, gene, training, deceptive, broken, reads, central, datasets, analysis, singlecell, classification, methods, rnaseq, set, damaged, sensitivity, type, pipeline, figure, accuracy, pca, specificity, rna, single, svm, categories, model, nat, sequencing, biological, compared, types, annotation,

Topics {✒️}

accuracy=\frac{tn+tp}{\ tn+tp+fn+fp} frederik otzen bagger tp+{\beta}^2fn+fp} navigating single-cell rna-sequencing quantitative single-cell rna-seq single-cell rna-seq experiments single-cell rna-seq data }_{\beta }=\frac{\left conventional rna-seq experiments single-cell rna-sequencing single-cell rna sequencing single cell rna-sequencing single-cell rna-seq full-length mrna-seq rna-seq gene profiling genome-wide studies add single-cell transcriptomics applied sided paired t-test outlier detection algorithm required pre-installed packages feature-based method makes high-throughput sequencing data rna-seq data processing single-cell transcriptome analysis article download pdf single-cell biology rna-seq metrics accurate long-read alignment processing scrna-seq data feature-based pca method performed nested cross-validation applied nested cross-validation scrna-seq dataset generated 80 tn/20 tp fast gapped-read alignment subsequent outlier detection outlier detection algorithms multivariate outlier detection t-test comparing features rna-seq experiments transcriptome-wide profiling rna sequencing workflow cell-type specific features rna sequencing data quality control method hfill \end{array} $$ single simple command pca feature-based methods author information authors rna sequencing reads

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         headline:Classification of low quality cells from single-cell RNA-seq data
         description:Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.
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      headline:Classification of low quality cells from single-cell RNA-seq data
      description:Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.
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         Capture Site
         Quality Cell
         Outlier Detection Algorithm
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         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
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      name:Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK
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